Posted by: drracing | October 19, 2017

WEC – Fuji Race analysis

Hi everybody!

Here we are again after another WEC race, this time in Japan, at the beautiful circuit of Fuji.

Before starting the analysis itself, though, i would like to share again a video of a simulation session we did in preparation for the race, using the 2017 LMP2 vehicle model that was also employed for the ELMS – Spa race preparation video.
You find it here.
Unfortunately, we didn’t really have any chance of any comparison between simulation and reality, because the whole weekend has been affected by a very unpleasant weather, with rain and fog being always a constant theme in every session, in particular during the race.

The race itself has been wet conditions for its whole duration, with a lot of interruptions (in the form of red flags or safety cars) due mainly to scarce visibility. This has made the race more exciting, on one side, with strategy, vision and luck playing an important role on the final result and because it lead to many track fights, but also left (at least for me) some disappointment, since it was not possible to analyze how quick 2017 cars would really be on this very interesting track.
Also the distance covered was much shorter than it could be, with the final red flag signing the end of contentions after “only” 113 laps, with only 83 laps done under green flag.

These are the main reasons why also my analysis will be shorter than the previous ones. Nonetheless, there are a few interesting points that came out crunching the numbers publicly available.

The race was won by Toyota n.8 car, with the sister n.7 coming into second, in a very important 1-2 for the Japanese manufacturer, that has always obtained very good reults on its home track.
Car n.8 victory is even more important, considering that car n.2, which is leading the championship standing, finished in fourth place, plagued by a dramatic lack of pace.

Something similar happened also in LMP2, with Rebellion car n.31 taking the victory in front of an extremely quick Alpine n.36. DC Racing / Jota sport Oreca n.38 finished third and lost some of the advantage they had in the championship, with Rebellion car n.31 now closer.

Let’s take a look at how the performance of each of the main actors of this race was.

LMP1

As we said, the very difficult conditions seemed to fit the Toyotas much better than the Porsches.
This is a very interesting point, above all if we consider that Porsche’s car n.2, the one who struggled the most in terms of performance during the race, actually signed the best lap time, with a gap of about 0.25 seconds on car n.8 best lap and was very fast at the beginning of the race.
If we also consider the average of the best 20 lap times anyway, Porsche n.2 seems still pretty competitive, being about three tenths quicker than car n.8 and with an even clearer gap to car n.1 and car n.7.
The situation changes completely, anyway, if we look at the best 50 lap times average: Toyota n.8 car is clearly the fastest, with about 0.3 seconds gap on car n.7 and car n.1, which are very close to each other. Porsche n.2 falls heavily behind and is nearly eight tenths slower than car n.8.

 

LMP1 average times table

 

The table above shows a summary where, for each average, the quickest time is shown in red.
We will not consider the “all clean laps” average this time, since the race was run for so long under safety car or yellow flag conditions, that it would not add much to our analysis.

The picture we got from the best and average best lap times is confirmed if we  look at the plots of the best 20 and 50 lap times of each car.

 

LMP1 Best 20 laps

 

LMP1 Best 50 laps

 

The first plot in particular (best 20 lap times of each car), shows how car n.2 is indeed the fastest if we only look at the best 13 lap times. Car n.8 follows closely, even in the very left part part of the plot.
The situation changes completely if we look at the second plot, anyway, where we can clearly see how dramatically the performance of car n.2 falls compared to all the other competitors (in particular compared to car n.8), above all if we focus on the right side of the 25 mark.
At the same time, this plot clearly shows how, for nearly the whole race length, car n.8 was consistently quicker the the other three crews, including the sister car n.7. It is also interesting to notice how close the performance of car n.7 and car n.1 was, with the line of each crew’s best 50 lap times intersecting the one of the other several times in this second plot. Car n.7, anyway, still has an edge on the very right side of the plot, confirming how also the second Porsche struggled in the last part of the race compared to the two Toyota.

According to the info i have and to what we learnt watching the race, Porsche (car n.2 in particular) had issues with the tyres, being affected by poor grip and by issues in bringing them up to temperature, above all in the second part of the race. 
The reason behind this seems to lie in the tyres specifications that each team planned/used for the race. As far as i know, there are two main types of rain tyres available for the teams, one fitting better heavier rain and lower temperature conditions (with Michelin being able to also modify them a bit on the track, if needed in the search for quicker heating up) and one adapting better for less extreme rain / less water / higher temperature conditions.
Apparently, Porsche could use the first tyre type at the beginning of the race but was unable to use them after the first pit stops; probably because of a different planning / pre race strategy, they didn’t have enough of these tyres for the race. As far as i know, these were also the tyres they used during free practice and qualyfing, where both Porsche were still extremely competitive.
If this is true, it means they found themselves running with tyres suiting the very special conditions that WEC encountered in Fuji much less than the ones that Toyota had at its disposal.
I think it is fair to say that, from a strategy perspective, Toyota did an excellent job in Fuji or, at least, any mistake.

According also to post-race interviews, Toyota n.8 was also helped a bit by a gamble with the fuel strategy, avoiding a third pit stop that actually both Toyota n.7 and Porsche n.1 did. As a consequence, car n.8 was also the one that spent less time in the pit.

 

LMP1 Pit times

 

It is now clear that car n.8 seemed to have a more consistent performance than the other cars. Let’s try to identify if this pace advantage was built on a specific part of the track more than in others.
As usual, the track was divided in three sectors, as shown in the image below:

 

Fuji track

 

The first sector is composed of about half of the main straight (which is very long) and the first corner, a very slow hairpin.
The second one includes the two fastest corners of the track, namely turn 3 and turn 4-5 and is (at least in dry condition) a very good section to evaluate of how much downforce a car has and, in general, how good the car’s balance and handling is in high speed corners.  There is then a mid-low speed corner (turn 6) that lead to a quick, full throttle section of the circuit (again, referring to dry conditions).
Finally, the third sector is composed mainly by slow corners, driven in first or second gear and, in some cases, with adverse camber producing very slippery car behavior.

Let’s take a look to Sector 1 times first.

 

LMP1 Sec1 average times table

 

The two Toyota are clearly in front here, no matter if we consider the best sector 1 time or the average of the best 20 and 50 sector 1 times.
This is particularly interesting, considering that, although the overall top speed was achieved by car n.7, the two Japanese cars seemed to have less straight line speed than the two Porsches, at least according to the trap speed measurements.
The best 20 and 50 sector 1 times plot confirms what shown by the table above:

 

LMP1 Best 20 sec1

 

LMP1 Best 50 sec1

 

Car n.7 and car n.8 are clearly quicker than the two Porsches, with car n.8 being faster than car n.7 up to the 46 mark and then dropping down a bit.
As we mentioned, this is particularly interesting because the two Porsche had often higher top speeds than the two Toyota, if we exclude for a moment the drop that car n.2 shows after the 25 mark and a big part of sector 1 is actually the final section of the main straight, where top speeds is surely important.

 

LMP1 Best 20 Top speed

 

LMP1 Best 50 Top speed

This drives some considerations.

  • we have to be careful, since i don’t know exactly where the TS was located: it was at the end of the main straight, but there is no indication about its exact distance from the start or the finish line. Maybe in certain phases of the race the Porsche n.2 had to brake so early (because of lack of grip) that the top speed measurement was affected (because the TS is in a zone where the car was already decelerating)?
  • For many laps there was a slow zone on the main straight, but i am not sure about how many laps exactly and if and how it affected cars speed
  • with LMP1 cars, there is always the unknown of how and where each car starts its coasting phase (energy recuperation strategy) before the driver hits the brakes
  • in general, it looks like Toyota simply had a better car than Porsche in this very special conditions and both crews were probably able to brake later and/or drive through turn 1 quicker and/or accelerate earlier at the exit of turn 1.
  • with such a bad visibility and the typical rain low grip conditions, i imagine that managing traffic situations was much more difficult than with a dry track, above all approaching such a hard braking zone as the one before turn 1. The car seating in front of the field (above all at the beginning of the race, when the lapping of slower classes has not begun yet), has sure an advantage.

 

As we are going to see analyzing sector 2, we could even think that the Toyotas had more downforce than the Porsche (and hence more drag too, which would explain the lower top speeds), but with a wet track one has always to be careful in coming to similar conclusions, as the pace of each car is dramatically affected by many factors, like the ability to have the tyres working in the right window (both in terms of temperature and pressure), mechanical settings (in order for the driver to have confidence but also to avoid typical rain issues like aquaplaning), the ability of each driver to deal with such a difficult scenario.

In any case, both Toyota cars seemed to have an edge on the two Porsche also in sector 2, where, in normal conditions, downforce would play a very important role.
Looking at the best sector times and average of the best 20 and 50 sector times, we see how car n.8 is always on top. Interestingly again, Porsche n.2 was extremely quick too if we look at the best sector time overall and also at the average of the best 20 sector 2 times (although in this later case the gap to car n.8 increases sensibly). On the other hand, we see again a dramatic drop when considering the average of the best 50 sector 2 times.
It is also interesting to notice how car n.7 and car n.1 have very similar performances no matter which value in the following table we look at:

 

LMP1 Sec2 average times table

 

Car n. 8 is pretty much in a league of its own, above all if we look at the best 50 sector 2 times average.
This is confirmed also by the plots relative to the best 20 and 50 sector 2 times.
Again, we see how car n.8 is constantly the fastest and how car n.2 had some good pace at the beginning of the race, producing very good times before the 16 mark, but falling down after.
It is also interesting to see how close the performance of car n.7 and car n.1 were, as the two cars indeed fight repeatedly with each other during the race.

 

LMP1 Best 20 sec2

 

LMP1 Best 50 sec2

 

On the contrary to what we have seen till now, sector 3 seems definitely Porsche’s territory, with both cars signing the best times here and with car n.2 being clearly the fastest if we look at the best 15-20 sector times.
The following table, relative to the best sector 3 times and the average of the best 20 and 50 sector 3 times underlines how Porsche had really an edge on the Toyotas in the final part of the track:

 

LMP1 Sec3 average times table

 

The picture appears even clearer if we look at the best 20 and 50 sector 3 times plot.
Interestingly, car n.8 was not particularly brilliant in this sector, being often also slower as car n.7.

 

LMP1 Best 20 sec3

 

LMP1 Best 50 sec3

 

Before closing with LMP1 and switching to LMP2, i would like to underline again how, beside a pure performance analysis, it is difficult to drive safe conclusions about each car performance in such difficult weather and track conditions, because a lot of different factors could play into the equation.
For sure, Toyota seemed to have an edge on Porsche and put in place a better strategy (with car n.8 also being helped a bit by the latest red flag) and, probably a better tyre choice/management.

 

LMP2

Let’s now take a look at the LMP2 class. The race was won by Rebellion car n.31, followed by Signatech Alpine car n.36 and DC Racing / Jota sport car n.38.
Albeit all the teams using the same chassis, some differences could be identified in how each car performed and in how each team tackled both setup and strategy.

There has been some post race discussions because some teams didn’t have their Silver/Bronze driver in the car at all, because of the red flags and interruptions that reduced significantly the race time with respect to the planned six hours. But this goes beyond the scope of this article, that wants to focus more on cars/crews performance.

As we will see shortly, actually the fastest car on track didn’t win, but Rebellion managed the race very well and was also able to do one less stop than Signatech (who finished second) and than several other teams. The same is true also for Jota Sport car n.38. Interestingly, most of the teams stopped more or less at the same time for the first pit stop, but then differentiated their strategy and/or needed to stop because of driver changes (as happened to car n.36 just only five laps after its second pit stop).
Some drivers’ mistakes also affected how the race evolved: beside some more spectacular crashes, one effective game changer was probably the spin of Nicolas Lapierre at lap 51.

Because of all of this, it is no surprise to see that car n.31 and car n.38 were the ones spending less time in the pit lane, with car n.38 having the shortest pit time.

 

LMP2 Pit times

 

To start looking at cars performance, let’s consider the following table first, showing the best lap time and the average of the best 20 and 50 lap times of each of the first five classified cars.

 

LMP2 average times table

 

Already by looking to the above table, it is crystal clear that car n.36 was by far the fastest during the whole race. If the difference between car n.36 and car n.31 best lap is only slightly above 1 tenth, the gap increases progressively if we consider the average of the best 20 and 50 lap times, with a massive difference of nearly 0.6 seconds in the latter.

This is well confirmed by the plots relative to the best 20 and 50 lap times obtained during the race by each car.

 

LMP2 Best 20 laps

 

LMP2 Best 50 laps

 

Beside showing clearly how much quicker car n.36 was compared to the others, these two plots also confirms car n.31 being the second fastest car on track and car n.38 and car n.24 being very close to each other, as we also could see in the average times table.
Fourth classified car n.28 is constantly and sensibly off pace instead, but was one of the few crews that led his non-professional driver in the car for a pretty long time, during the first stints.

Let’s try now to break down each car performance analyzing each track sector times and the top speeds.

If we look at sector 1, we immediately notice how, actually, car n.31 is here quicker than car n.36, with all the first five classified crews being pretty much packed together with the exception again of car n.28, which seems to be a bit more off.
The table below, listing the best sector 1 time and the average of the best 20 and 50 sector 1 times, shows car n.36 being in front of car n.31 only if we consider the best time overall; car n.31 is anyway faster in both the best 20 and 50 sector times average:

 

LMP2 Sec1 average times table

 

This is pretty much confirmed by the plots relative to the best 20 and 50 sector 1 times of each car.

 

LMP2 Best 20 sec1

 

LMP2 Best 50 sec1

 

On the contrary to what we have seen in the LMP1 class, the information we gather here seems to match with what we see if we look at the top speeds of each car: as we said already, sector 1 is composed in a very big part by the main straight, therefore top speed plays surely an important role in defining how fast a car is in this track section.
Looking to the previous plots we would expect car n.31 achieving higher top speeds than car n.36, most probably because of a lower downforce/drag setup. And, indeed, this is exactly the case.

 

LMP2 Best 20 Top speed

 

LMP2 Best 50 Top speed

 

Car n.31 has the highest top speed in the bigger part of the best 50 top speeds each car achieved during the race. Car n.36 and car n.38 are both slower than car n.31 and that seems to match pretty well with what we saw looking at sector 1 times.
Interestingly, car n.24 is faster on the main straight than both car n.36 and car n.38, but its sector 1 times are generally slower than car n.36 and not dramatically quicker than car n.38.

Sector 2 seems to be the proof that our assumptions about each car aerodynamic setup could be correct, as both car n.36 and n.38 are faster than car n.31 here.
This is what we can conclude by looking at the table showing best sector 2 times and the averages of the best 20 and 50 sector 2 times and also what the graphs plotting the best 20 and 50 sector 2 times of each car underline.

 

LMP2 Sec2 average times table

 

LMP2 Best 20 sec2

 

LMP2 Best 50 sec2

 

Car n.36 is clearly the fastest in sector 2, with car n.38 following. Car n.31 and car n.24 are relatively close together.
It seems reasonable to conclude that car n.36 and car n.38 opted for an higher downforce/drag setup, while car car n.31 and car n.24 for a less draggy/higher top speed one. 

Sector 3 has a more balanced situation, in terms of performance, between the two contenders, with car n.31 and car n.36 being very close to each other and having a pretty big edge on all the other crews.
Car n.31 has the best sector 3 time overall, but car n.36 is slightly forward if we consider the average of the best 20 sector 3 times. Finally, car n.31 is again on top in the best 50 sector 3 times average metrics, but with the two cars practically achieving the same results (we have a difference of only 0,003 seconds!):

 

LMP2 Sec3 average times table

 

In a very twisty track section, where we mainly find slow corners and tricky road cambers, both the mechanical balance of the car and the ability of the drivers in the crew play a major role.
The equivalence of performance between the first two classified cars and their advantage on all the other competitors is reflected also in the best 20 and 50 sector 3 times plots:

 

LMP2 Best 20 sec3

 

LMP2 Best 50 sec3

 

Car n.36 has a small edge on car n.31 on the very left of both plots above, as confirmed also by the best 20 sector 3 times average. Indeed, the best 10-15 sector 3 times of the Signatech crew are slightly lower the ones of Rebellion’s car n.31.
But after the 15 mark the two cars seems to be pretty much a copy of each other.

Closing also this LMP2 analysis, it was interesting to find out how the winning car probably succeeded not because of a better pace, but because of a better strategy/management, while the fastest car was indeed the second classified one.
Also, it is always intriguing to notice how, although all teams use the same chassis, very often each one comes to a different setup choice and how this is reflected by each car’s performance in different track sections.

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Posted by: drracing | September 29, 2017

ELMS – Spa Race analysis

Hi everybody!

I start to like this race analysis thing! That’s why i am here again with a new one, this one crunching the publicly available numbers relative to the ELMS race in Spa, held on the weekend of the 23rd and 24th of September.

Before to dive into the analysis though, i would like to share some data and a video about a simulation session done in preparation for the same race. The session took place on my simulator and the latest iteration of the LMP2 vehicle model has been used.
Here is a link to the video.
Incidentally, the best lap of the session was a 2’02.427, which compares pretty well to the pole position time of 2’02.457 done by Ben Hanley. In the video we managed to catch a lap slightly below 2’02.7.
This tells nothing in particular about the accuracy of the model, but as I had a chance to compare the simulations results with logged data, i can say i am pretty happy with how it matches to the real car!

And now, let’s look at Spa and the available data, to try and understand what happened exactly.

This beautiful track, which needs no introduction, offers always not only very exciting racing action but also a very interesting scenario from a technical perspective, mixing long straights and fast sections with quick and slow corners and leaving room for very different setup choices potentially producing a very similar lap performance.

The race was won with merit by Graff Racing car n.40, driven by James Allen, Richard Bradley and Gustavo Jacaman.
It was a complex race, with many neutralization phases either done with FCY or letting the safety car into the track. This was necessary, for example, when the Algarve car crashed at Pouhon and the car flipped over, after impacting with the barriers.
Because of the many neutralization periods, strategy played a very important role and teams went through the race using very different approaches, making it very difficult to read. For example, Dragonspeed cars (n.21 and n.22) pitted early at lap 8 when the first FCY was declared, while SMP, even if not using the FCY for an early pit stop, was able to run 19 laps with a full tank before stopping to refuel, while all the other main contenders stopped at lap 18.
To make all more spicy, the two leading cars both had to serve a drive through penalty at the end of the race and ended up with a gap to each other of less than 0.6 seconds.

Let’s look at the pit stops first and at the overall time spent in the pit lane.

Graff #40:                    5:58.598
G-Drive #22:               6:36.065
SMP #27:                     5:15.185
Autosport #32:           5:31.889
Dragonspeed #31:     6:51.258

The winning car n.40 spent 5:58.598 in the pit, including the late drive through, which took about 23.6 seconds and went into the pit lane six times.
Car n.22, who finished second, spent more time in the pit (6:36.065) and went in seven times, but, as we said, the first pit stop was done very early and under FCY, a condition where all the other cars are forced to drive slow anyway, while the latest was a drive through penalty that took 23.4 seconds. Without the drive through, the strategy could have proved very good indeed.
Car n.27 is the one who spent less time in the pit lane among the main contenders, with 5 pit stop and an overall 5:15.185.
Car n.32 also had a shorter pit time than the two leading teams, with five pit stops and an overall time of 5:31.889.
Finally, car n.21 also stopped seven times for a total of 6:51.258.
I am not totally sure about this, but my taking is that each team changed the tyres only twice during the race.
Again, it was a very complex race, from a strategy perspective. The five stop strategy that SMP and Autosport put in place was probably also in Graff plans, but the latest penalty forced them to stop once more. Interestingly, without that stop, they would have been in the pit for 5:34.987, not too much longer than what SMP did.

What about each car performance?

If we look at the best laps of each car, car n.22 is on top and the winning crew is actually “only” fourth:

Best lap times:
Graff #40:                    2:05.939
G-Drive #22:               2:05.236
SMP #27:                     2:06.25
Autosport #32:           2:05.719
Dragonspeed #31:     2:05.623

As we see, car n.22 was very clearly the quickest one on the single lap, with the good surprise of car n.32 being now much closer to the best contenders’ pace than in Paul Ricard.
Anyway, as we have seen already in the two previous race analysis, looking at the best lap time doesn’t tell too much in endurance racing.
If we take a look at the average of the best 20 laps, we start getting a better picture:

Best 20 lap times average:
Graff #40:                    2:07.158
G-Drive #22:               2:07.285
SMP #27:                     2:07.888
Autosport #32:           2:07.365
Dragonspeed #31:     2:07.912

The first thing we notice is car n.40 taking the lead, with car n.22 and n.32 being very close. It is not a case that also car n.32 was for a long time in contention for the lead and it is interesting to see how much closer to the Orecas they were here compared to Paul Ricard.
Another interesting thing here is that car n.21, which was clearly the fastest on track in Paul Ricard, was not as successful in Spa, at least in terms of pace. This is partially due also to how the race evolved, with FCY and SC periods probably affecting the stints of the two quick drivers in the crew, Lapierre and Hanley. One of the latest stints of Nico Lapierre was indeed very quick and car n.21 was probably the fastest car on track during that same stint. Still, the impression is that they struggled much more than in Paul Ricard, also in terms of pace.
All of these trends seems to be confirmed if we look at the best 50 lap times average, with car n.40, car n.22 and car n.32 ever closer to each other:

Best 50 lap times average:
Graff #40:                    2:08.32
G-Drive #22:               2:08.33
SMP #27:                     2:09.019
Autosport #32:           2:08.498
Dragonspeed #31:     2:09.327

It is impressive to see how car n.22 and car n.40 had nearly the same average time. As we will see later on, this is even more interesting considering that i suspect the two cars were running different setup philosophies.

Since the race was so much affected by FCY and SC situations, we will not have a chance to look at a best 100 lap times average (the leader only run 97 laps) and we will directly jump to the “all clean laps” times average (or something at least close to it, as several cars found themselves involved in a FCY or SC for a different time).

All clean laps lap time average:
Graff #40:                   2:09.059
G-Drive #22:               2:08.949
SMP #27:                     2:09.758
Autosport #32:           2:09.147
Dragonspeed #31:     2:09.94

Again, car n.40 and car n.22 are very close to each other, but with car n.22 now being slightly faster.
It is also very interesting to notice how car n.27 was pretty much always out of pace, compared to the other competitors, including car n.32, which was in every of the averages we looked at the third quickest.

If we look to the plots showing the best 20 and 50 lap times for each car, we pretty much identify the same trends. Car n.40 and car n.22 are pretty close together, with Graff’s car being clearly the fastest in the 20 best lap graph and the G-Drive one getting better if we look at the 50 best laps one.

 

Best 20 laps

 

Best 50 laps

 

It is also interesting to notice how car n.32 was pretty close to two Orecas pace for more or less the whole race and also was significantly quicker than the n.27 SMP Dallara. The latter, although being probably closer to the best cars compared to what achieved during qualifying, was still pretty much off in terms of lap times.
The plot also shows that car n.21 was not really as brilliant in Spa as it was in Paul Ricard.

Since the ELMS offers some variety in terms of Chassis used by each team, it is interesting to take a look at the sector times and analyse where each car was faster or slower, trying to find indications about each car strong or weak points and about how each team approached its setup.

The track was divided, as usual, in three sectors, as shown in the image below.

 

track map

 

The first sector is pretty much about top speed and having less drag. The second one is pretty long and represents a good test of both car’s low and high speed grip and handling. Downforce is very important here, together with a well balanced behavior in both low and high speed corners.
Last sector is again about speed and, partially, about braking stability (there is a very hard braking before the last chicane, where the cars downshift from 6th to 1st gear and from about 290 km/h to about 70).

If we look at the best sector 1 times, we can immediately recognize a very important trend:

Best sector 1 times average:
Graff #40:                   35.769
G-Drive #22:              35.981
SMP #27:                    36.038
Autosport #32:          36.086
Dragonspeed #31:    36.115

Just by looking at these numbers we immediately catch something extremely important: while all the other cars are very close to each other (with car n.27, car n.32 and car n.21 nearly all in less then a tenth of a second), car n.40 has a clear edge, being some two tenths faster than car n.22 and about three tenths faster than all the others.
This trend doesn’t change if we look at the average of the best 20 sector 1 times, but actually the advantage of car n.40 becomes even more evident.

Best 20 sector 1 times average:
Graff #40:                   35.984
G-Drive #22:              36.265
SMP #27:                    36.346
Autosport #32:          36.229
Dragonspeed #31:    36.335

Interestingly, the advantage of car n.40 on car n.22 increases, while the gap between car n. 40 and car 32 reduces slightly, with car n.32 being quicker than car n.22.
It is also interesting to notice how, in this first sector, the SMP Dallara doesn’t seem to be too far off from the competition, if we exclude car n.40.
If we look at the average of the best 50 sector 1 times, the trends remains very similar:

Best 50 sector 1 times average:
Graff #40:                   36.142
G-Drive #22:              36.430
SMP #27:                    36.554
Autosport #32:          36.374
Dragonspeed #31:    36.611

The gap between car n.32 and car n.40 reduces again a tiny bit but in general all the cars seem to increase their average times similarly compared to the 20 best sector 1 times average, with the exception of car n.27 and car n.31 that seem to have a slightly stronger performance deterioration.
This is particularly interesting for car n.27, as it is the only car in our analysis having only two silver drivers in the crew, both performing very similarly and, as far as we could see in Paul Ricard, pretty well too. We would expect a smaller effect due to the drivers when looking at performance deterioration in the 50 best sector 1 average than for other crews, but still the average time goes up slightly more than for other cars. This could signalize the car suffering more the effects of tyre deterioration in this sector, where, we have to remember, we also find the famous Eau Rouge corner, which could maybe become a bigger challenge if the tyres are not new anymore and if the car has less downforce than the competitors. These are of course mainly speculations, i am simply trying to interpret what we see, but of course i could be completely wrong.
In terms of performance deterioration, the worst case is still car n.21 (as in Paul Ricard), with the third (non-professional) driver probably still having a very important impact on the average performance of the crew.

If we finally look at the “all clean laps” sector 1 times average, we identify more or less the same trends:

All clean laps sector 1 average:
Graff #40:                   36.449
G-Drive #22:              36.761
SMP #27:                    36.940
Autosport #32:          36.739
Dragonspeed #31:    37.023

The main message, summarizing what we can deduce analyzing first sector performances, is that car n.40 seems to have a pretty clear edge on the competition. Graff’s car is consistently about three tenths quicker than the other cars, in a track section where performance are defined mainly by straight line speed.
This is well confirmed by the best 20 and 50 first sector times plots:

 

Best 20 sec 1

 

Best 50 sec 1

This seems to strongly suggest that car n.40 was running less downforce/drag than the other Orecas (and less drag than the other cars) and/or that they had more engine power. Since we deal with a spec engine class and the difference in time is pretty big, i think the reason should be mainly connected to a different aerodynamic setup, compared for example to car n.22 (although there is nothing like “all engines are the same” and i would not be surprise if, because of engine fatigue of other teams or simply because of the window that each manufacturer allows in terms of “power tolerances”, a part of a similar gap could rely on the engine itself).
Another interesting point that the plots confirm is also how competitive car n.32 was in this first sector, also compared to car n.22, which is consistently slightly slower.

Our conclusions seem to find further support if we look at the top speeds (the speed trap was located at the end of the Kemmel Straight, that means at the end of sector 1). Here, car n.40 has again a clear advantage on the competition, as we can see looking at the best speed overall and to the average of the best 20 and 50 top speeds each car achieved:

Best top speed:
Graff #40:                   301
G-Drive #22:              294.5
SMP #27:                    297.7
Autosport #32:          297.7
Dragonspeed #31:    292.9

Average best 20 top speeds:
Graff #40:                   297.95
G-Drive #22:              292.1
SMP #27:                    294.5
Autosport #32:          294.7
Dragonspeed #31:    290

Average best 50 top speeds:
Graff #40:                   296.5
G-Drive #22:              288.2
SMP #27:                    292.9
Autosport #32:          292.4
Dragonspeed #31:    287.7

As we could imagine, car n.40 is the fastest, with car n.27 and car n.32 following and being relatively close to each other and car n.22 being a bit slower.
Looking at the plots relative to the best 20 and 50 top speeds, we identify more or less the same situation but it looks like car n.27 had a slight advantage on car n.32 on the long distance:

 

Best 20 top speed

 

Best 50 top speed

 

Beside Graff having the best speed (and also the best sector 1 time, which looks very consistent) we see something interesting: although car n.27 seems to have a higher top speed than car n.32 on the long run, it constantly produces slower sector 1 times, which seems to suggest the SMP Dallara having slightly less drag than Autosport’s Ligier but also maybe some issues in driving fast through the Eau Rouge section, maybe because of lower downforce or different setup choices. The same conclusions seems realistic if we compare SMP Dallara to G-Drive Oreca, that has slower top speeds, but is nonetheless consistently faster in sector 1.

Contrary to sector 1, in sector 2 downforce and handling play a central role, while having low drag is surely not the top priority to obtain competitive times.
Following what we learnt analyzing sector 1, it is not a surprise to find out that the fastest car in sector 2 is actually G-Drive Oreca (n.22).
If we only look to the best sector two time of each car, the advantage of car n.22 on the competition is an astonishing half a second, with car n.32 and car n.21 following and car n.40 being the slowest.

Sector 2 best times:
Graff #40:                   57.588
G-Drive #22:              56.745
SMP #27:                    57.475
Autosport #32:          57.248
Dragonspeed #31:    57.249

The situation changes a bit if we look at the best 20 sector 2 times average, with car n.22 still being fastest than all the others, but with a much smaller gap, above all compared to car n.40 and car n.32, with the latter being very competitive in this second sector.

Sector 2 best 20 times average:
Graff #40:                   58.335
G-Drive #22:              58.157
SMP #27:                    58.566
Autosport #32:          58.241
Dragonspeed #31:    58.594

We see a similar picture if we also look at the average of the best 50 sector 2 times, only the gap between car n.22 and the closest among the others (car n.32 and car n.40) increasing a bit. SMP Dallara remains pretty much out of pace in this sector:

Sector 2 best 50 times average:
Graff #40:                   58.972
G-Drive #22:              58.717
SMP #27:                    59.197
Autosport #32:          58.966
Dragonspeed #31:    59.385

Nothing changes significantly in the “all clean laps” average, with car n.22 still on top of car n.32 and car n.40:

Sector 2 “all clean laps” times average:
Graff #40:                   59.651
G-Drive #22:              59.422
SMP #27:                    59.927
Autosport #32:          59.640
Dragonspeed #31:    60.267

The performance of each car compared to the others in sector 2 is somehow easier to perceive if we look at the best 20 and 50 sector 2 times plots:

 

Best 20 sec 2

 

Best 50 sec 2

 

Above all when looking at the best 50 sector 2 times plot, we clearly identify how big was the edge that G-Drive crew had on the competitors. We also clearly see that car n.32 and car n.40 performance were pretty close to each other. Again, if we assume that the two cars have the same engine power and we consider also what we saw in sector 1, this could indicate that the Oreca 07 has a higher aerodynamic efficiency than the Ligier Js P217 (in this case, in particular, meaning the Oreca had similar downforce but less drag), which seems to be reasonable considering that the Oreca 07 defined pretty much LMP2 performance target until today.

Sector 3 is, again, a quick section of the track where top speed plays a very important role. The only quick corner belonging to this sector is Blanchimont, which is an easy flat out with these cars. Beside this, as we said, there is a heavy braking into the bus stop chicane, which is composed of two very slow corners.
In this sector, car n.40 is again in front in terms of performance, above all compared to the direct competitor during the race, car n.22. The latter, gets closer to Graff’s pace only if we consider the last part of the best 50 sector times list: is it maybe connected to a bigger tyre wear affecting car n.40? Or maybe to a worse traffic management? Difficult to say.
Looking to the best sector 3 times, car n.40 is con top but interestingly the closest car following is car n.32:

Sector 3 best times:
Graff #40:                   32.175
G-Drive #22:              32.27
SMP #27:                    32.409
Autosport #32:          32.183
Dragonspeed #31:    32.259

Car n.40 gains a stronger gap on the competition in the average of the best 20 sector 3 times:

Sector 3 best 20 times average:
Graff #40:                   32.487
G-Drive #22:              32.608
SMP #27:                    32.618
Autosport #32:          32.615
Dragonspeed #31:    32.584

Car n.22, car n.27 and car n.32 are extremely close to each other, with car n.21 being slightly quicker than the other two.
The situation changes a bit if we look at the average of the best 50 sector 3 times, with car n.40 being pretty much on the same level as car n.22 and car n.27:

Sector 3 best 50 times average:
Graff #40:                   32.747
G-Drive #22:              32.795
SMP #27:                    32.797
Autosport #32:          32.894
Dragonspeed #31:    32.896

It is interesting to notice how close to each other are car n.22 and car n.27, confirming in this sector the Dallara is pretty competitive.
All cars are also very much packed together if we look at the “all clean laps” average, with car n.40 still holding a small advantage and car n.22, car n.27 and car n.32 being very close to each other.

Sector 3 “all clean laps” times average:
Graff #40:                   33.06
G-Drive #22:              33.12
SMP #27:                    33.178
Autosport #32:          33.153
Dragonspeed #31:    33.261

As always, the best 20 and 50 laps plots give a more “visual” feeling about the relative performance of each car.

 

Best 20 sec 3

 

Best 50 sec 3

 

Up to the 30 mark, Graff’s Oreca is clearly the fastest car, while between the 30 and 50 mark the performances of car n.40, car n.22 and car are very close to each other.
Car n.32 is very quick on the very left side of the plot, but tends to loose performance and, in general, seems a bit slower than the competition, despite having average times that are not too far from the ones of the other cars.
Again, it is also interesting to notice how in this sector the n.27 SMP Dallara pretty much matches car n.22 pace, in a track section which is probably the one resembling more closely the second sector of Paul Ricard track, where car n.27 was very competitive (mainly inline speed and a hard braking).
Interestingly, it looks like Autosport’s Ligier performs better in the first sector than in the third one, although in both of them top speed plays a central role. We cannot forget, anyway, how an important part of first sector performance depends on how good the car flies through the Eau Rouge complex.

To close we can take a quick look at the lap time each car produced during the race at each lap. Again, the plot comparing all cars is pretty messy (even more in this race, because of the many SC and FCY that came in), but still tells interesting things:

 

all laps all cars

 

Some points we can extract just by looking at all the lines are:

  • Car n.21 lost a lot of time in the first two stints, where the non-professional driver was at the wheel
  • Car n.21 was extremely quick between laps 60 and 70, but was in general not as quick as it was in Paul Ricard compared to the competition
  • Car n.40 had a pretty bad performance between laps 60 and 70, being in this interval of time slower than car n.22 and car n.32
  • maybe also because of the many safety cars, we don’t seem to identify a dramatic performance deterioration as, for example, in COTA WEC race

 

More information can be deduced by looking at the comparison between car n.40 and each other car.
The following plot compares car n.40 with its main contender for the final victory, car n.22.

 

all laps 40 vs 22

 

Car n.22 was apparently slightly faster during the first stints and again between laps 63 and 76, but slower in the stint immediately after and in the final stint, when it probably got caught in traffic. Car n.40 was faster also between lap 45 and 52.

What about SMP Dallara?

 

all laps 40 vs 27

 

As we have already seen, car n.27 was constantly a bit slower than car n.40 and never really on the same pace as the Orecas. The green line in the plot above lies nearly always above the blue one, in particular during the last four stints.

A comparison between Graff’s car n.40 and Autosport’s car n.32 is, on the other hand, a bit harder to read:

 

all laps 40 vs 32

 

Car n.32 was indeed very fast in the first part of the race and again faster than car n.40 between laps 65 and 75, as we saw. Anyway, it didn’t match car n.40 pace in the last stints nor it did between lap 45 and 52.

Finally, as we already had a chance to say, car n.21 was not so fast in Spa and this is again confirmed if we look at the following plot:

 

all laps 40 vs 21

 

Until lap 60, car n.40 seems constantly faster. Car n.21 runs some very competitive laps 61 and 74, but also afterward it never really match car n.40 pace anymore, also showing maybe the only case of visible performance degradation, with its lap times constantly increasing in the last two stints.

Closing, this analysis confirms that car n.40 not only worked out its strategy very well, but also obtained its victory relying on its pace, which proved to be extremely good, also thanks to some setup choices that were apparently different than the other Orecas (see for example Dragonspeed – G-Drive).
Spa is surely one of the best track for the Oreca to express its potential, but it was also interesting to see how the Autosport’s Ligier was not too far off from Orecas pace (above all car n.22), while the Dallara seemed to struggle more.

Posted by: drracing | September 24, 2017

WEC – COTA Race analysis

Hi everyone!
This post will be again an attempt at an Race analysis, as i did in my previous one. This time we will look at WEC COTA race data.
Once again, i hope i will not come to completely wrong conclusions, trying to understand the how and why of this race (in terms of performance) by just looking at the numbers openly available online

Let’s start by saying, it was a very interesting race, with a pretty close battle (at least up to a point) in LMP1 and with very nice discussion points in LMP2 too (although having in the WEC only one chassis maker, there is some less variety, also in terms of performance, compared to the ELMS).
The race had only one safety car neutralization, with this being made not with a full course yellow, but letting the safety car into the track, practically deleting any existing gap for many of the track battles that were taking place.

Temperatures were very high and this has surely been an important element, also in terms of tyre degradation.
LMP1 teams also always had to double stint their tyres (i am not sure if LMP2 teams also did it, unfortunately i could not take note of every pit stop) and Toyota and Porsche did that in different moments during the race (Toyota double stinted its first set of tires, while Porsche did change the tyres at the second stop, following always alternating one stop with full service and one only with fuel and driver change only). That lead to interesting situations on track too, since the two marques often had a different tyre status in a certain phase of the race.

We will look at LMP1 first and then move to LMP2.

LMP1

The race has been won by Porsche, who also put in place team orders to favor their championship leading car (Car n.2), despite being car n.1 most of the race slightly quicker.
The good surprise was that Toyota, after having shown no promising pace in qualifying, was pretty much close to Porsche during the race, with very exciting attrition taking place on track between car n.1 and car n.7 in a fight for second place.
During the race, car n.8 also suffered a small issue with the hybrid system, apparently becoming unable to play with the boost “freely” when needed (for example for an overtake maneuver) but still managed to end up in front of the sister car.

Looking at the fastest laps first, Porsche was slightly ahead, but with a much smaller gap than what we saw in qualyfing:

Porsche #1:      1’47.149
Porsche #2:      1’47.302
Toyota #7:        1’47.391
Toyota #8:        1’47.556

The gap between the two big teams stays more or less the same also if we look at the average of the best 20 laps:

Porsche #1:      1’47.768
Porsche #2:      1’47.908
Toyota #7:        1’48.022
Toyota #8:        1’48.011

Car n.1 is still the quickest and this was a topic of discussion during the race, since its performance was clearly sacrificed to favor car n.2, currently leading the world championship. The two Toyotas are extremely close together, but the situation changes already a bit if we look at the average of the best 50 laps:

Porsche #1:      1’48.135
Porsche #2:      1’48.279
Toyota #7:        1’48.353
Toyota #8:        1’48.470

As we can see, the gap between Toyota and Porsche becomes slightly smaller and the Toyota n.7 seems to now show more pace than the sister car. It is still amazing to see how close each car is to the others, to underline (if necessary) how good the drivers are and how intense is the competition.
If we consider now the average of the best 100 laps, we start to see the effects of team orders inside Porsche, with car n.1 and car n.2 being extremely close.
As a consequence, the gap between Porsche and Toyota becomes even smaller than before:

Porsche #1:      1’48.630
Porsche #2:      1’48.681
Toyota #7:        1’48.763
Toyota #8:        1’48.875

Interestingly, Toyota n.7 car is still faster than car n.8.
If we finally look at the “all clean laps” average, we still see a similar situation but with the gap between Toyota and Porsche now stretching again a tiny bit:

Porsche #1:      1’49.416
Porsche #2:      1’48.485
Toyota #7:        1’49.568
Toyota #8:        1’49.514

Car n.1 is still the fastest, while car n.8 becomes faster than car n.7.

Just looking at the numbers, we can identify how, on one side, Porsche had a small advantage on Toyota (above all considering car n.1) in terms of pace and how car n.1 was clearly the quickest.
This seems to be confirmed also by the plots looking at the best 20, 50 and 100 laps.

 

LMP1 best 20 laps

 

LMP1 best 50 laps

 

LMP1 best 100 laps

 

We immediately see, once again, how car n.1 has the best pace, with the only exception being in the window after the 65th best lap, where it becomes slightly slower than car n.2.
if we look at the best 20 laps plot only, the two Toyota seem to be very close to each other; but pretty big gap between the two emerges after the 22nd best lap mark, with car n.8 becoming sensibly slower than car n.7, at least up to the 90th best lap mark.

Clearly, in this second phase of the season Porsche has an advantage on Toyota. It can be anyway interesting to try to understand if there are some track sections where the gap is bigger and we will try to do this by looking to sector times.
The track was divided in three sectors, as shown in the following image:

 

track map

 

A first relatively short first sector is followed by two longer ones. Last sector is a pretty twisty one, with several slow corners and a very long right double bend (turn 16, 17 and 18) where both tyres and aerodynamics are very important, followed by a medium speed left corner (turn 19).

Analyzing the first sector, if we would just look at the best times of each car, we could come to partially wrong conclusions. The two Porsche are clearly in front, but the situation changes slightly when looking at the average of the best 20, 50 and 100 laps.

First Sector best times:

Porsche #1:      22.567
Porsche #2:      22.737
Toyota #7:        22.891
Toyota #8:        22.936

First sector best 20 times average:

Porsche #1:      22.962
Porsche #2:      22.968
Toyota #7:        22.960
Toyota #8:        23.007

First sector best 50 times average:

Porsche #1:      23.046
Porsche #2:      23.05
Toyota #7:        23.004
Toyota #8:        23.047

First sector best 100 times average:

Porsche #1:      23.121
Porsche #2:      23.133
Toyota #7:        23.061
Toyota #8:        23.099

 

We immediately see how, actually, all the car have pretty similar performances, also because we are dealing with a very short sector. We also notice how the Toyotas stay more constant, compared to the Porsches and become slightly quicker than the competitors already in the best 20 laps average (car n.7).
Since the first sector is mainly composed by the box straight and the first corner, could it maybe lie in the hybrid boost strategy/flexibility? We are of course just speculating, the cars could also have different aerodynamic properties, or different traction in corner exit, just to name a few possible reasons.

Interestingly enough, if we look at the “all clean laps” average, the situation bends even more in favor of Toyota:

Porsche #1:      23.292
Porsche #2:      23.304
Toyota #7:        23.2
Toyota #8:        23.243

If we look at the plots relative to the best 20, 50 and 100 first sector times, we can pretty much confirm what the average times told us. On the single quick lap, Porsche seems to have an advantage, but on the all race pace, Toyota is slightly quicker.

 

LMP1 best 20 sec1

 

LMP1 best 50 sec1

 

LMP1 best 100 sec1

Each of the three plots above shows how car n.7 is constantly the quickest in the first sector, excluding the very left portion of the window. Car n.8 is a bit slower and lies below Porsche’s pace for at least the best 5-6 laps, but after the 20-mark becomes clearly the second quickest car.

What about sector 2? The situation looks exactly the opposite as in sector 1, with Porsche cars being clearly and constantly quicker than the two Toyota; no matter if we look at the best time overall, the average of the best 20, 50, 100 or “all clean laps”, Porsche seems to retain a pretty sensible advantage, with car n.1 looking again stronger than car n.2, while the two Toyota are closer to each other.

Second Sector best times:

Porsche #1:      40.476
Porsche #2:      40.786
Toyota #7:        40.815
Toyota #8:        41.122

Second Sector best 20 times average:

Porsche #1:      40.793
Porsche #2:      40.952
Toyota #7:        41.238
Toyota #8:        41.287

Second Sector best 50 times average:

Porsche #1:      40.955
Porsche #2:      41.062
Toyota #7:        41.385
Toyota #8:        41.403

Second Sector best 100 times average:

Porsche #1:      41.161
Porsche #2:      41.210
Toyota #7:        41.576
Toyota #8:        41.571

Second Sector all clean laps average:

Porsche #1:      41.573
Porsche #2:      41.687
Toyota #7:        42.031
Toyota #8:        41.935

The plots of the best 20, 50 and 100 sector times tells more or less the same story. Toyota’s lines are always and sensibly above Porsche’s ones.

 

LMP1 best 20 sec2

 

LMP1 best 50 sec2

 

LMP1 best 100 sec2

 

Again, we could speculate trying to understand why Toyota is slower than Porsche in this sector. Personally, it is for me not straightforward to come to a conclusion, because sector 2 is actually a combination of a very demanding sequence of corners where both Aerodynamics and mechanical grip play an important role, then followed by two segments where top speed, hybrid boost and corner exit traction are surely very important.
Analyzing top speeds, we can try to get a better insight about the main performance differences between the two manufacturers, assuming both cars has a similar overall engine power and similar boost effects on terminal speed (which is unfortunately not necessary the case, but we don’t have any better data than this to try to understand where the differences are).
It is not hard to notice how, since the introduction of their high downforce kit in Nürburgring, Porsche seems to have gained a good advantage on Toyota, in terms of pure performance. The 919 has most probably more downforce than the TS050, maybe even with a small edge in term of aerodynamic efficiency, but here we are already speculating. What we could see analyzing both marques top speeds is that the Porsche (above all car n.1) seems to be a bit slower than the Toyotas, while car n.2 has more similar performances.
If we look to the plots relative to the best 20, 50 and 100 top speeds achieved during the race, we can identify a pretty clear trend.

 

LMP1 best 20 TS

 

LMP1 best 50 TS

 

LMP1 best 100 TS

 

The first thing we can clearly see is that Porsche n.1 has constantly the lowest top speed, if we exclude the best top speed overall. This could mean, assuming the coasting strategy were not too different than the one of Porsche n.2,  that car n.1 maybe had an higher downforce set up and, even if paying this in terms of drag, this choice seems to have paid in terms of performance.
The second thing is that Porsche n.2 and Toyota have similar top speeds, but Porsche n.2 is still quicker than both Toyota cars in the second sector. This seems to suggest that the 919 has effectively a similar drag compared to the TS050, but a higher downforce and hence a better efficiency.
We could get some more data to evaluate this point also by looking at the third sector times. Last sector’s performance is also very much handling-driven, since the combination of slow and fast corners surely requires a very good combination of low speed (mechanical) grip, downforce and traction.

Let’s start looking to the best sector time overall and best average sector times first.
The situation is pretty well balanced, with Toyota having a small advantage on Porsche:

Third Sector best times:

Porsche #1:      43.103
Porsche #2:      43.266
Toyota #7:        43.073
Toyota #8:        43.079

The two Toyota are amazingly close to each other and a bit quicker than the two Porsche, with car n.1 following very close and car n.2 being about 0.2 seconds slower.
The situation remains extremely close even when looking to the best 20, 50, 100 and “all clean laps” averages:

Third Sector best 20 times average:

Porsche #1:      43.579
Porsche #2:      43.603
Toyota #7:        43.584
Toyota #8:        43.461

Third Sector best 50 times average:

Porsche #1:      43.841
Porsche #2:      43.795
Toyota #7:        43.727
Toyota #8:        43.726

Third Sector best 100 times average:

Porsche #1:      44.078
Porsche #2:      44.016
Toyota #7:        43.899
Toyota #8:        43.940

Third Sector “all clean laps” times average:

Porsche #1:      44.453
Porsche #2:      44.462
Toyota #7:        44.360
Toyota #8:        44.361

As we said, there is in general a pretty well balanced situation, with one car gaining or loosing its ranking position depending on the average we consider, but with differences to its mate car and to the other brand that stay more or less always pretty constant.
Toyota is slightly quicker in this segment, but the gap between the two brands is really small.
This is confirmed also by the plots relative to the best 20, 50 and 100 sector 3 times.

 

LMP1 best 20 sec3

 

LMP1 best 50 sec3

 

LMP1 best 100 sec3

 

We can see the Toyotas are constantly slightly quicker than the two Porsches, with car n.8 signing the best 20 laps, but falling a bit behind car 7 after the 25th best lap mark.

A final note about the pit stops each team did. The strategies proposed by Toyota and Porsche were pretty much aligned, with both teams double stinting their tyres. The main difference was that Porsche changed the tyres in the first pit stop and double stinted the second set, while Toyota went for double stinting the first set and did a first shorter pit stop.
Anyway, if we look at the overall time spent in the pit the two teams were pretty much aligned, with Toyota overall pit time being only slightly higher.

Porsche #1:      396.473
Porsche #2:      396.361
Toyota #7:        397.762
Toyota #8:        398.31

Also, all pit stops happened very close to each other.

 

LMP2

The LMP2 class has also produced a very interesting race, with exciting track action. Even if the WEC misses the technical variety of the ELMS series in terms of chassis manufacturers, the extremely high quality of teams and drivers ensure always a top level show.

COTA races was dominated by Signatech Alpine, that was able to succeed with a comfortable gap on the second classified car (Rebellion car n.13) even with one more pit stop of about 46 seconds, required to repair a a rear light and even if they had to double stint the tires with Menezes (who felt several positions during that stint), because they used two sets during the qualifying. Also, the safety car that was sent on the track during the race surely didn’t help them, but they really have been stronger of any odd in Texas.

We will look at the first five classified cars, run respectively by Alpine, Rebellion and DC Racing / Jota.

All of them did 7 pit stops during the race, with the exception of the winner crew (already mentioned) and DC Racing Car n.37, that pitted 8 times.
The overall pit stop times are shown here:

Signatech – Alpine #36:       546.293
Valiante – Rebellion #13:    506.293
Valiante – Rebellion #31:    536.899
DC Racing #38:                     482.938
DC Racing #37:                     546.526

In terms of performance, car n.36 has always been among the quickest cars on the track, also running the fastest lap during the race and being clearly the fastest if we look at the 100 best laps and “all clean laps” average:

Best Lap times:

Signatech – Alpine #36:             115.427
Valiante – Rebellion #13:          115.616
Valiante – Rebellion #31:          115.695
DC Racing #38:                           116.094
DC Racing #37:                           115.434

Best 20 lap times average:

Signatech – Alpine #36:            116.578
Valiante – Rebellion #13:         116.532
Valiante – Rebellion #31:         116.925
DC Racing #38:                          116.781
DC Racing #37:                          116.302

Best 50 lap times average:

Signatech – Alpine #36:            117.190
Valiante – Rebellion #13:         117.174
Valiante – Rebellion #31:         117.620
DC Racing #38:                          117.542
DC Racing #37:                          117.246

Best 100 lap times average:

Signatech – Alpine #36:            117.728
Valiante – Rebellion #13:         117.870
Valiante – Rebellion #31:         118.099
DC Racing #38:                          118.284
DC Racing #37:                          118.351

“All clean laps” lap times average:

Signatech – Alpine #36:            118.569
Valiante – Rebellion #13:         118.997
Valiante – Rebellion #31:         118.982
DC Racing #38:                          119.564
DC Racing #37:                          119.698

The first thing to notice is that car n.36 looses the “lead” in the above lap times averages only in the best 20 and 50 lap times average (still being anyway always very close to the fastest car), but is clearly in front of all the others again in the best 100 lap times average.
It is also interesting to notice how car n.37 has indeed a very competitive pace if we look at the best lap times, best 20 and 50 lap times average, being either the fastest or very close to the fastest car in each of these classifications. Anyway, its performance falls down if we look at the best 100 and “all clean laps” lap times average, with a pretty big gap to car n.36.
It is not a case that, during the first half of the race, car n.37, with Brundle at the wheel, was able to climb the ladder up to P1, with a lot of very exciting overtakings. If we look to the best 20 laps plot, Car 37 is extremely competitive.

LMP2 best 20 laps

 

Anyway, already looking at the best 50 laps plot, the situation changes completely and car n.37 shows a very strong fall off after the 25th mark

 

LMP2 best 50 laps

 

Looking at the best 100 laps, finally, we see as car 37 performance drops really dramatically with the crew becoming the slowest of the first 5 classified cars after the .. mark:

 

LMP2 best 100 laps

 

On the other side, looking at the best 50 and 100 laps plots, we can see how car 36 is the one staying more constant in terms of performance, not producing the best lap times up to the 10-15 mark, but clearly becoming the quickest after the 35 mark, showing the smallest performance degradation.

It would be interesting to try to understand what exactly lead to car n.37 being so fast in certain phases and much slower in other moments of the race. As we have seen also analyzing ELMS Paul Ricard race, the pace of the non-professional driver seating in the car can really define the race, but was it really a problem with the slower driver here?
If we look at following plot, showing the lap times every car did at every lap, we can see once again, on one side, how constant car n.36 was, but also how other car was affected by both different drivers performance or by performance degradation:

 

LMP2 stints analysis

 

Although the plot seems simply messy, we can identify a few interesting points:

  • car n.36 line is the one staying averagely more horizontal
  • same cars had apparently very slow stints, like car n.38 in its second and car n.13 in its third
  • some cars show more performance degradation during a single stint, like car n.37 in the last stint and car n.38 in the third one.
  • car n.37 was the quickest on track during the second stint

 

If we compare each car to car n.36, it is a bit easier to identify what happens during each stint:

LMP2 stints analysis 36-13.JPG

 

This first plot shows car n.13, compared to car n.36. We can see how car 13 was particularly slow in its third stint (the non professional driver was in the car), but slightly quicker in stint n.2. Also, both cars seems to show a pretty constant performance in each stint, with the lap times line not going up too aggressively (which means no extreme pace degradation), if we exclude stint 3 for car n.13.

The following plot is a comparison between car n.31 and car n.36:

 

LMP2 stints analysis 36-31

 

Both cars seem pretty constant, but car n.31 is slightly slower during pretty much the whole race (excluding maybe the fourth stint), as confirmed also by the best laps plots.

 

LMP2 stints analysis 36-38

 

If we look at the previous plot, showing a comparison between car n.36 and car n.38, things get more interesting. Car n.38, even if showing sometimes comparable pace to car 36, seems to suffer of a much stronger performance degradation during a single stint, as we can see looking to the second, fourth , sixth and last stint (lap times lines goes up). We could suspect this happens mainly during the second stint done with a certain set of tyres, but i am honestly not sure this is really the case, since i could not follow when they exactly did their tyre changes.
The same tendency is even more evident if we look at the comparison between car n.37 and car n.36:

 

LMP2 stints analysis 36-37

 

Car n.37 has a tremendous pace during its second stint (ad this is well confirmed by the 20 best laps plot, where car n.37 is clearly the fastest), but is slower in pretty much all the other stints and shows also a much more pronounced performance degradation in every second stint, as the sister car n.38.

I will not go through the track sectors analysis for the LMP2 class, since, because of the absence of chassis variety in the WEC, we would simply confirm the same tendencies we saw analyzing the overall lap performance.

Posted by: drracing | September 11, 2017

ELMS – Paul Ricard race analysis

Hi everybody!
This will be a different post than what I normally do in recent times. I will try to dig into something I have never done before, hopefully without coming to completely wrong conclusions.

Before i dig into today’s topic, a short update about the latest activities i have been involved with.
I am happy to say that also in 2017 something i wrote will be featured in the “24 Hour Race Technology” magazine! Because of this article i came into contact with many extremely well prepared people and that also gave a chance to validate the latest iteration of 2017 LMP2 vehicle model i built, now based on another manufacturer data. It was again extremely interesting to see how close simulation results and logged data are to each other!

More about this soon!

Let’s now start with what i wanted to discuss today.

A couple of weeks ago the European Le Mans Series had its fourth race of this season in Paul Ricard, in France and the race offered some food for though.
First of all, the track layout used this year is not the same as in 2016, because the long Mistral straight had been cut in two using one of the chicane options available.
As far as I understood, this decision has been taken to make the track safer, probably thinking about the top speed that the LMP2 cars could achieve at the end of the Mistral straight, to then run into a very, very quick corner. Anyway, in my very humble opinion, this has made the track much less interesting and, “de facto” eliminated probably the only very high speed corner that Le Castellet circuit had to offer (making probably also the decision about how much downforce / drag to run easier, because of the absence of the very long straight).

The track is known to have a very flat and leveled surface, with less bumps compared to other circuits and features now mainly low-to-medium speed corners (first, second and third gear).

The most interesting thing was the first victory of SMP team and Dallara since the new LMP2 rules and cars were presented. The team competes with two very young Russian drivers coming from single seaters and who seem to have found very quickly a good feeling with the LMP2 Dallara. This was their second race in LMP2 and already in Austria, with nearly no practice before the race, they obtained very encouraging results.
With this article I will try to analyze how they came to a win and where they did better than the others. To do this, we will mainly use the data made available by the organizer after each race.

Before we look at numbers and plots, anyway, one thing to consider is that SMP was the only team having only two drivers that, although being classified as Silver, are actually more or less Professionals and are both reasonably quick. As we will see, this plays a very important role in defining a team’s strategy and results.

Beside this, the first thing that we can notice is that the SMP team has been the only one to go through the race with only four pit stops, while all the other teams (who didn’t encounter car issues) did five. The time they spent in the pit during the race was 4:51.592, while the team that ended in second place (G-Drive, Oreca n. 22) spent 6:24.037 minutes in the pit, with a difference between the two teams of about 1 minute and 22 seconds. The gap between the two cars on the finish line was about 1 minute 28 seconds, so it seems that a big part of it was actually up to the one less pit stop that SMP did.
SMP doing only four pit stops came for sure as the result of a specific strategy but also of a gamble, supported very well by a Full Course Yellow that came close to the end of the race, which surely helped to save some fuel. On the other hand, it looked like the Dallara was a tiny bit more gentle on the tires than the Oreca and car 22 (second classified) was forced to change the rear tires at the end of the race (during their latest pit stop) to avoid problematic balance issues, thus losing precious time.

Interestingly enough, anyway, SMP car was surely not the one with the best pace on the track.
If we simply look at the best lap time that each car achieved during the race, the SMP team has a 1.55.756 on his list, while car n. 22 (G-Drive by Dragonspeed, Oreca07) did a 1:55.252 and car n. 21 (Dragonspeed, Oreca07) did a 1:55.463.
Also looking to the average of the twenty best lap times that each car did, we have the following situation:

27 SMP:                      1:56.937
22 G-Drive:                 1:56.717
21 Dragonspeed:         1:56.655

The Oreca still seems to have a small edge on the Dallara on this track, although Paul Ricard is probably one the tracks that suites SMP car and setups better.
The situation anyway bends a bit toward SMP if we look at the average of the best 50 lap times:

27 SMP:                      1:57.672
22 G-Drive:                 1:57.647
21 Dragonspeed:         1:57.439

This shows a substantial parity between SMP and G-Drive and a smaller advantage of Dragonspeed compared to the 20 best laps average.
Finally, if we look at the average of all lap times (considering only clean laps, so no FCY and no pit-in / pit-out laps), the situation bends even further to SMP side:

27 SMP:                      1:58.66
22 G-Drive:                 1:58.958
21 Dragonspeed:         1:59.871

As far as I can see, this shows exactly the advantage of having a crew composed “only” of two quick silver drivers in the car. As we will see shortly, the main disadvantage for at least one of the two Dragonspeed-managed cars was in the time lost when their non-pro driver was at the wheel.

If we look at the following plots, the small advantage of the two Dragonspeed Orecas seems to be well confirmed. The first picture depicts the quickest twenty laps of each car, the second the best fifty.

 

20 best laps

 

50 best laps

 

Looking to the first plot first (quickest twenty laps for each car), it is pretty clear how, on the pure pace, the Oreca is consistently faster than the Dallara. This is especially true with car 21, during the two stints driven by Ben Hanley, but seems to also be the case for car 22, although the difference to the Dallara looks smaller, above all if we consider the right part of plot.
The second plot seems to tell, again, that the car 21 was the quickest, while car 22 and car 27 (SMP Dallara) were much closer, especially on the right side of the 20 mark. This suggests a more constant performance of the Dallara and, maybe, a better tyre management.
It is also interesting to notice how, on the very right side of the plot, the lap times of car 21 jumps up, going above the green line representing SMP car. As we will see, this is what happens when the crew has a non-professional driver unable to drive at a comparable pace than the two other team mates.

It is also interesting to look into the sector times that each car produced. First of all, the image below shows how the track was divided:

 

track

 

Considering sector 1 (which is a mix of straights and second and first gear corners), we can identify a similar situation to what we have seen for the overall lap times. Considering each car overall best sector 1 time, the two Oreca (car 21 and 22) still have an edge on SMP Dallara, with car n.22 being slightly quicker than car n.21 and the Dallara being slightly more than one tenth slower:

27 SMP:                      32.527
22 G-Drive:                 32.406
21 Dragonspeed:         32.416

Anyway, if we consider again the average of the best 20 and 50 lap times of each car, the gap reduces significantly, with the Dallara achieving equivalent results to car 22 on the best 20 laps average and jumping to second place if we look at the best 50 laps.

Best 20 laps average:
27 SMP:                      32.845
22 G-Drive:                 32.846
21 Dragonspeed:         32.67

Best 50 laps average:
27 SMP:                      33.027
22 G-Drive:                 33.103
21 Dragonspeed:         32.929

If we consider all the clean laps done during the race, SMP has the best average overall also in the first sector, which underlines once again the positive effects of their crew mix (with regard to this point, the Dragonspeed Oreca n.21 is clearly the car in the weakest position, becoming the slowest of the three even if it still was the quickest considering both the best 20 and 50 laps averages):

27 SMP:                      33.348
22 G-Drive:                 33.521
21 Dragonspeed:         33.685

Looking at the plots relative to the best 20 and 50 first sector times, we can see, once again, how the n.21 Oreca was the quickest car, while there is pretty much equivalence between car 22 and 27 on the 20 laps plot and a clear advantage of car 27 over car 22 on the 50 laps one, with car 22 times increasing sensibly after the 15-20 mark, compared to car 27 ones.

20 best sect1

 

50 best sect1

Sector 2, being practically composed only by a straight and a chicane, is a good indicator about car overall top speed (and, hence, drag) and inline accelerations performance (both braking and accelerating) and shows pretty much equity between the Dallara and the two Orecas.
Car 21 is still the quickest, if we only look at the best sector 2 time overall, but with less than 0.1 seconds difference to car 27:

27 SMP:                      34.279
22 G-Drive:                 34.347
21 Dragonspeed:         34.2

Anyway, if we consider the average of 20 best first sector times for each car, all cars have pretty much the same performance:

27 SMP:                      34.52
22 G-Drive:                 34.56
21 Dragonspeed:         34.51

Also looking at the average of the best 50 laps, the situation doesn’t change significantly, although we can start to identify the same tendency already seen before, with the Dallara coming to the front:

27 SMP:                      34.648
22 G-Drive:                 34.709
21 Dragonspeed:         34.665

Once again, SMP has indeed the best average if we consider all the clean laps done during the race:

27 SMP:                      34.866
22 G-Drive:                 35.023
21 Dragonspeed:         35.176

As for the whole lap and for sector one, it is interesting to notice as, also for sector two, the Dallara seem to be more constant in its performance compared to cars 22 and 21, with less difference between the best lap overall, the best 20, 50 laps averages and the “all clean laps” average. This surely suggests how good and well balanced the crew was, as we said already, but also maybe a better tyre management.

All of this is well reflected looking at the plots of the best 20 and 50 sector 2 times.

 

20 best sect2

 

50 best sect2

 

Both plots clearly show how, on the single lap performance, Car 21 is still clearly the fastest.
Anyway it is also clear to see how, on the long distance, car 21 and car 27 are very close to each other, with car 22 being a bit slower, with the gap increasing as the lap counter goes forward. It is also interesting to see how, after the 43 mark in the second plot, car 21 sector times jump upward leaving car 27 to be clearly the quickest on track.
This suggests two reflections: as we will see also looking at sector 3 times, maybe car 22 and car 21 were using slightly different aerodynamics settings, although belonging to the same team. In particular, it looks like car 21 went for a setup favoring top speed and, maybe sacrificing a bit of downforce, while car 22 probably went for more downforce and, hence, more drag.
The second point regards, again, the winning crew: on one side, we can see that sector two was probably the one fitting SMP car the best, also if we compare Dallara’s and Oreca’s performance; on the other side, it is interesting to see, once again, how close to each other are the best sector times that car 27 was able to produce, compared to that of car 21 and 22, with car 27 probably suffering of less degradation and using the good drivers performance at the best level possible (including traffic management).

The Dallara showing less difference between the best sector time and the three averages we consider, seems to be also well reflected by Sector 3 analysis.
Anyway, being sector 3 the most twisty one, with the only medium-speed corner of the track (turn 12), it seems to be the best one to evaluate each car handling and downforce. It is probably not a case that, in this sector, performance gap between Oreca and Dallara is bigger, also considering the 50 laps and “all clean laps” average. This is even more interesting, if we keep in mind how car 22 in particular seemed to suffer of a bad tyre degradation during the last stints.

Let’s look again at the best sector 3 time overall for each car:

27 SMP:                      48.711
22 G-Drive:                 48.391
21 Dragonspeed:         48.328

Car 21 is again the quickest, with nearly 0.4 seconds gap to the Dallara and only a small difference to car 22.
Looking at the 20 best sector 3 times average, Car 21 is not the quickest though, with car 22 now jumping on top and the Dallara still being about 0.4 seconds away:

27 SMP:                      49.439
22 G-Drive:                 49.085
21 Dragonspeed:         49.183

The best 50 laps average shows a similar situation, with a the gap between the two marques closing a bit:

27 SMP:                      49.751
22 G-Drive:                 49.538
21 Dragonspeed:         49.631

The interesting part about this sector comes anyway when looking at the “all clean laps” average. For the first time SMP is not on top and the n.22 Orecas still has an edge (again, even with the car suffering of bad tyre degradation at the end of the race):

27 SMP:                      50.246
22 G-Drive:                 50.187
21 Dragonspeed:         50.599

Once again Car 21 is affected by the Bronze driver, while car 22 remains on top.
We immediately notice that, what we identified looking at sector 2 times could be true, with car 22 being, on the longer distance, a bit quicker than car 21 in this third sector, probably because of different setup choices. Even more interesting is how, also on the “all clean laps” average, the Dallara remains slower than the Orecas (inverting a tendency that could be saw in both overall lap times and the first two sectors times), in a track section where handling and downforce probably pay a very important role.

This is confirmed also looking 20 and 50 best sector 3 times plots.

 

20 best sect3

 

50 best sect3

 

We again see how SMP crew is constantly slower than G-Force and Dragonspeed ones, if we only exclude car 21 last 10-15 quickest laps in the 50 best sector 3 times plot.
Equally interesting, if we just exclude the best sector time overall, car 22 pace looks always stronger than car 21 in this sector, with better times even when looking at the 50 best times plot and at the very right side of the graph and this underlines again how car 21 and 22 could have probably gone on two different setup strategies.

Finally, let’s look shortly at the lap times history during the whole race for each car. The following plots show the lap time vs lap number for the whole race. Vertical peaks indicate either a pit stop or a safety car situation.

 

All Race Laps

 

This plot only looks at cars 21, 22 and 27. The first thing to notice is that, as we already mentioned several times, car 21 significantly suffer a pretty slow driver in the crew, compared to Ben Hanley and Nico Lapierre. Every time the third driver is in the car, lap times increase by about three seconds: this happens namely between lap 22 and 47 and again between lap 68 and 87.
The most constant car, in term of lap times, is clearly the SMP Dallara and that seems to pay off well above all at the end of the race, when the car in second place (the G-Drive Oreca, n.22) suffers of a sensible performance reduction.
This plot also underlines once again how, if we look at pure performance, car 21 is clearly on top, with car 22 following closely. Still, Car 27 looks extremely consistent during the whole race and, also because of a better strategy (including one less pit stop), it managed to win with a pretty good margin.

Posted by: drracing | June 16, 2017

Testing suspension geometry in the simulator

Hi everybody!

As anticipated in my last post, this article will be a review of one of the project I was involved with during the winter. As for one of my latest project of 2016, also this case study is actually relative to an investigation I supported using driving simulation as a development tool, in a way I had not thought possible a few years ago. Life is full of surprises.

After during the summer I had a chance to prove myself how, even using a cheap software, a proper vehicle model could be used effectively to gain some more insight about car setup and its intricacies (helping the team I was supporting to further improve a bit their performance), this time I was asked to try to evaluate different design solutions and quantify not only their performance but also driver’s perception / feedback to each of them.
The study was focused more specifically on investigating the effects on car handling and performance of different front suspension layouts, differing sometimes pretty much one to the other (in one or more areas), with the aim to evaluated what the driver would feel driving each of them, together with their impact on the car-driver system’s performance.
The guy who asked me to support this project was initially mainly looking to understand the impact on driver steering feedback coming from different designs / geometries, but soon the study also evolved into a chance to evaluate which influence each setup would have on overall performance and why, identifying, also through data analysis, in which areas there would be the biggest differences and the reasons behind each Delta.

The model we used was actually the reliable and good validated 2016 LMP2 one, driven in Silverstone.
We starting establishing a baseline (lap time / performance and behavior) using the original design, then moving onto testing new solutions.
The parameters we worked on at the beginning were mainly scrub radius, king pin angle and caster trail, but inevitably we soon moved on also acting on camber change, roll center position and migration and caster angle and finding out how also other parameters that we underestimated at the beginning actually play an important role, at least in terms of steering feedback.

Before I dive a bit more into the details of this project, let me spend two words about the assumptions used by rFactor in terms of suspension modeling and my view about the software’s limitation.
First of all, it is clear to me, as it is probably clear to all of view, that rFactor was not though as an engineering tool and there are some limitations that are difficult to completely overcome, if not acting on the code itself (which is out of my skills, intention and interest).
I would of course be very happy to have a chance to use more “engineering” oriented and flexible software for my projects, but the budget required to buy one of them would probably be absolutely out of reach even for many small companies.
The truth is, anyway, that it is amazing how much you can test and understand, in terms of vehicle dynamics and even in terms of setup / layout even using a cheap product like rFactor, despite all of its limitations. Of course, “conditio sine qua non” is always to know exactly your assumptions and to build a model as close as possible to the real car (or at least to the data we have about it).

As we had a chance to briefly mention in one of my previous posts, rFactor simulates suspension behavior in a very advanced way, since it practically define each suspension component as a rigid body (with or without mass properties) connected to the surrounding ones through mechanical constraints, like spherical joints, hinges, etc. This means, for example, that the wheel and the “spindle” (which actually identifies the complete Upright – Hub assembly in rFactor) have their own (definable) mass and inertial properties. The links are defined as rigid elements with no mass, practically locking the distance between two points depending on their length.
There are a number of things to take care of, when modeling a suspension, including like rFactor adjust camber, caster and toe. But if these parameters remained locked or the “errors” that the software does when changing one of them are compensated in a proper way, the suspension kinematics is simulated in a very similar way to what a normal multibody package would do.
A bit of attention must be paid for layouts using a pushrod / pullrod with a rocker to activate the damper/spring unit, since the Rocker assembly and its functionality cannot be reproduced. Of course, since we are dealing with an LMP car, this is exactly the situation we find ourselves in, as we are considering a double wishbone with pushrod actuated rockers for both front and rear axle.
From a wheel rate / motion ratio perspective, this “issue” can be easily overcome producing very accurate results, as we have seen already in the past, the point here simply being to directly work with the Wheel Rates, instead of the spring rates. To do this, I am using a trick, playing with the pushrod’s hardpoints (and, consequently, with its orientation), to obtain exactly the wheel rates and wheel rates change (with respect to wheel travel) I want, independently from the rest of the kinematics.
This means that the “virtual pushrod” will have another position and another 3D orientation compared to the real one, in order to obtain the same wheel rate and wheel rate change of the real car.

From a statics perspective and, more specifically, if we want to isolate and quantify the load acting in each linkage (this is particularly important for this study, since the steering feedback was one of the parameters we would like to better understand and since rFactor and the plugins I used produce the steering wheel feedback strictly basing on the force acting on the steering tie rods), this actually can bring a difference in the results because, depending on the position and orientation of each beam, the loads acting on each of them (for a defined loadcase at the contact patch) will change. In other words, although the suspension “as a system” would still behave the same (at least considering every suspension link and the upright-hub assembly as a rigid body, so ignoring every compliance), the internal reaction forces would change.
Anyway, the good news here is that, for the suspension geometry and layout we are using, even if the absolute value of the force acting on the steering rod that could be measured in the real car is different than the one produced with our “rfactor alternative-pushrod position”, its trend and gradients are very similar: in other words, the load acting on the steering rod is changing in a similar way in both cases, for a certain change of the Fy, Fx or Fz acting at the contact patch. All of this has been checked first using a simple excel sheet I built to calculate the reaction loads in each suspension members, depending on the loadcase at the contact patch and on the pickup points position; to be absolutely sure, a second check has been done using a Multibody software and in both cases the results have shown exactly the behavior I described above.
What is important for this study is actually the trend shown by the reaction forces in the steering tie rod, since the steering forces produced by the simulator’s steering system are proportional to the steering tie rod loads through a factor that is actually what we use to tune the intensity of the steering feedback itself.

Beside the driving simulation itself, I also used some CAD and some Multibody modeling for this project; on one side, I did this to evaluate each layout before to test it with the simulator but, most often, also to tune each geometry a bit and try to isolate and change only the parameters we wanted to evaluate at each step, keeping the others as constant as possible, with the aim to limit their influence on the results (in particular the steering torques, but not only).
Anyway, in certain cases this was not actually possible because of real world physical limitations (for example parts colliding to each other) and we had to accept that, with a certain setup, some parameters that we would not want to change would indeed change (see for example Ackermann effect). It was nonetheless interesting to see how a certain set of parameters would work, both in terms of performance and driver perception.

It is probably not a case that the best performing solution was also the one which was received more enthusiastically by the driver (although, in my experience, this is unfortunately not always the case).

The first test was done using the baseline configuration, namely the front suspension geometry originally built in the car. We run some sessions with this solution and established a reference lap (and the relative logged data) to be used later as a base to evaluate the following setups both subjectively and through data analysis.

The second test was performed using a revised front suspension geometry (we will call it Exp1), mainly deviating from the baseline in the upright area. In particular, caster angle was reduced by slightly more than one degree, although the caster trail was slightly increased. The King Pin Angle was also increased by about 2 degrees, producing a smaller scrub radius. This setup also had a slightly higher Ackermann effect (more dynamic toe out) and a slightly higher roll center.
The feeling was immediately very good. The car showed a bit more understeer, above all in mid-corner and, sometimes, even in corner exit. This helps in certain situations, see for example turn 3 (a 1st gear corner, where traction is very important), but seems to be a limit in others, see for example the last chicane, above all in the exit of the second (right) corner leading to the last right tender before the box straight.
Beside showing more mid corner understeer, this setup actually also produced a more reactive behavior in corner entry, above all in quick corners.
From a driver feedback perspective, it was interesting to notice how the changes we did made the car easier to drive, giving to the driver some more confidence and also the feeling that the relationship between steering angle and front cornering forces remains more or less linear for the complete range of used angles (while the baseline had a more unpredictable behavior, above all at very high steering angles, where the cornering force seemed to drop more abruptly and in a less predictable way when exceeding with the steering angle and the driver had a feeling of “loosing front grip”), although producing more understeer.

An interesting point was the increase of steering forces, perceived by the driver and confirmed by data analysis. This seems to go somehow against the expectations of some the changes we did (see for example a reduction of caster angle).
Anyway, two factors must be considered: on one side, the caster (or longitudinal) trail was slightly longer (granting a longer lever arm to Cornering forces in producing Self aligning torques); on the other side, we noticed how, one of the side effects of the geometry change we did was a slight reduction of the length of the Steering Arm (distance between the outer tie rod point and the steering axis), thus creating a bigger force in the steering tie rod for a certain torque to be reacted.

In terms of performances, this setup produced a 3-4 tenths improvement compared to the baseline.

Here below you can see the traces relative to three logged parameters: speed, steering angle and steering force. The tested geometry is shown in red (Exp1).

Exp1 - speed

Exp1 - steering

Exp1 - steer forces

The third test (referred here as Exp2) was performed using a geometry differing from the baseline much more aggressively. Caster has been increased by about 3.5 degrees, producing also a substantially bigger caster trail (about 30% bigger than the baseline one). King Pin angle and Scrub radius were very similar to the original configuration. The Ackermann effect (or percentage or dynamic toe) was slightly lower, as was the camber gain. Finally the static roll center position was also substantially lower.

Driver’s feedback was again very good, although the car behaved very differently than in the previous test. A strong reduction of the understeering tendency was evident from the very first corners, with the front axle now having substantially more grip, above in mid-corner but also in corner exit, with a reduction of the power understeering tendency that disturbed a bit in the last chicane in the previous test. Interestingly, even showing much more front grip, the car didn’t become instable or unpredictable, nor it showed any “dangerous” oversteering behavior.
In quick corners, the vehicle was now a bit less reactive than in the previous test, but still showed very good driveability.
Steering forces increased slightly, compared to the previous test and were thus sensibly higher than the baseline setup.

The lap times we obtained were anyway very similar to the previous test, with this latest outing producing an about 0.02 seconds quicker lap time.

In the following plots, this test’s results are shown in orange (Exp2).

 

Exp2 - speed

Exp2 - steering

Exp2 - steer forces

 

The direct comparison between this setup and the previous one (not shown in these pictures) show slightly higher steering forces in this latest case, although the steering arm being now bigger than both the baseline and the first test.
To finally quantify the influence of this latest parameter we later did a very quick test using a very similar geometry compared to the one tested here, but reducing the steering arm to a very similar value compared to the baseline (and trying to keep the other parameters the same). This further increased the steering forces, making them substantially higher than both baseline and first test and, more important being a direct comparison, much bigger than this test 2.

I will not bore you with a description of all the other tests we did (some of them were not as successful as the first two and the following one) and I will just jump to the one that produced the best performances and the best driver feeling, also to show how much of a difference it did in terms of lap times and how good this could be felt in the simulator.
This latest test was performed using a front suspension geometry now having about 0.8 degrees more caster than in the previous one (and so about 4.3 degrees more than the baseline), a slightly bigger caster trail than test 2 (but much bigger than the baseline) and a smaller scrub radius (compared to both baseline and test 2), because of a sensibly bigger king pin angle. Roll center, camber gain, Ackermann effect and steering arm’s length were all kept practically the same as the baseline.
One of the interesting features of this geometry was how the camber evolves with the steering angle, not only on the outside tire but also on the inside. A caster increase normally always produce an increase of camber delta as a function of steering angle, but here this phenomenon seems to be amplified and probably also the contribution of an “effective” camber change on the inside tire helped.

Dirver’s feedback was immediately enthusiastic, as also proved by the lap times, that dropped by about 3 tenths of a second compared to the previous two tests (so overall about 7 tenths compared to the baseline).
The driver felt now even less understeer and the front axle, which ensures a very high grip, without generating oversteer or instability in any corner. The general feeling was actually of more grip on both the front and rear axle, but with even less understeer than the previous test.
This less understeering tendency could be felt also in corner exit, but somehow that didn’t deteriorate traction. The car was much easier to drive and allowed to push more easily, communicating always a feeling of stability and predictability. As a result, it was possible to go easier on the throttle on some corners’ exit, like the last chicane.
This was also true for the steering feedback: steering forces were now higher than any of the previous tests and the “linear” feeling we described about the first test remained, with the driver reporting that, even with very high steering angles, the front axle always seemed to produce cornering forces in a gradual manner, without abruptly loosing grip or generating oversteer or instability.
Interestingly, the steering trace data seems not always to confirms this strong grip of the front axle, at least compared to the baseline logged data. But it is also true that it still shows a more homogeneous use of the steering wheel, probably confirming how this more linear tendency in the relationship between steered angle and front grip and allowing the driver to maneuver the car more aggressively.

All of this seems is shown in the following plots relative to this test’s logged data, depicted in pink (Exp4).

 

Exp3 - speed

Exp3 - steering

Exp3 - steer forces

 

Beside finding extremely interesting all the tests and their outcome, I was once again amazed to see how much we can learn using (properly and also knowing the limitations and the turnaround to be used to compensate for some simplifications) a simple and cheap tool like rFactor, even from an engineering perspective.
The goodness of the results of this study is of course strongly dependent on how good also the vehicle model was built, in particular for all what concerns the tire model. I am pretty sure my model adheres pretty good to the data I have, but of course any model is only a model. Real testing is and will always be the best way to validate and decide on certain design decisions, but it is amazing to experience how much we can do in terms of pre-evaluation even with such a cheap tool.

By the way, it was really good fun to go through all these designs and also have a chance to see how they perform, getting a “more real” feeling of how the car would handle and being able to do 1:1 tests, with all the advantages connected to the use of a simulation (see deletion of the effects of track conditions, temperatures, rubbering, traffic etc).

Hi everybody!

Again a long time since i last wrote something, but it has been a busy winter and hopefully I will soon have a chance to write more about my latest projects. Some of them have been really exciting, including simulation sessions aimed at a new car development (front suspension) in cooperation with a very experienced engineer/designer.

This post will still be about 2017 LMP2 cars though, since they finally got the track for an official test (the ELMS and WEC prologues) and we have some first results to compare to the ones I published here back in December and to some new ones i collected, this time with a real driver (with LMP2 experience) seating at the wheel in my simulator.

During the last months I further refined my 2017 LMP2 model, basing on new data I received and some setup improvements, coming mainly from testing. Car’s behavior has improved a bit, also because of some aerodynamics effects I was previously not considering and i now included in the simulation and that seem to influence these cars’ behavior more sensibly than other kind of race cars (mainly in terms of stability).
As a side result to these updates, lap times has dropped a bit compared to the ones i mentioned back in December.

But let’s come to the facts: the week before the ELMS prologue, Fabian Schiller (a young driver with LMP2/Gt3 experience, who won in the Asian Le Mans Series at his debut in the LMP2 class and claimed a second place in his first Blancpain GT3 in Misano) came to visit me and spent a couple of hours at the sim, completing a few sessions with the LMP2 vehicle model in Monza.
This has been very useful, not only for the feedback he could give about my simulator (hardware and settings) and about the vehicle model, basing on his experience with “old” generation LMP2 cars, but also to understand how quick a real driver could be with (what should be) a representative vehicle model of a 2017 LMP2.

The first things to say (and this is something very pleasant for me) is that he felt immediately comfortable with the hardware and with the model and was able to drive naturally and fast from the very beginning.
After some laps, that he used to find the best braking points and the best line for each corner, he immediately produced very good lap times (already during the first session he landed in the 1’37” region), feeling immediately “home” with the vehicle model and its behavior.

His feedback about the car and the simulator itself was also very good. To mention his words, he said that the steering feedback the model produces is probably the best he ever felt in a simulator and that the model behaves in a very natural and predictable way, making it somehow easier to extract performance for drivers with real track experience.

To confirm all of this, he just did 3-4 sessions for a total of about 40 laps, with just some time between each session to take a look to the data logging and try to understand where he could still improve. Nonetheless, he was able to be immediately pretty quick and to probably go very close to limit of the “model + driver” system.

His best lap time was about 1’36″5, with a theoretical best lap time about two tenths quicker. As already mentioned in my previous article, the top speed at the end of the main straight was close to 308 km/h.

This compares extremely well with the best laps produced during the ELMS and WEC prologue by LMP2 cars.
During the two days ELMS test, the best lap was a 1’36″4 with more drivers running best lap times below 1’37” (results here). WEC guys produces some slightly better performance, with two drivers able to go close to a 1’36″0 , but with more drivers producing lap times in the region of 1’36″5 (results here).
Also the top speeds seem to match pretty well.
Unfortunately, we don’t have many other data to compare to, but this seems to be already a pretty good feedback, also because i am sure i can rely blindly on the quality of the track model (which has been validated already with some real world data in the past).

An important note, the model we run used a Sprint body kit in a Low Downforce configuration (Aero Map) and this is exactly what the real cars have done in Monza too, as far as i know.

Here below the main telemetry plots (you can also find them in High Res in my Flickr account).

speed and throttleLat Long gSteering

Here also a short video showing one of the first sessions Fabian run in the simulator (please apology the absence of car sound and the back noise, but for the driver himself wearing headphones is always the best solution).

Thanks a lot to Fabian for his visit and his support for this study.

I am already excited to see what will happen next weekend in Silverstone, when both the first ELMS and WEC season race will take place.
According to my results, if the weather is good and the track is in good conditions, we should see best lap times below 1’46”, probably close to 1’45″0.
Let’s see what will happen!

Posted by: drracing | December 23, 2016

2017 LMP2 – what’s the story?

Hi everybody!
This is my last post this year and i will try to make good use also of my new Youtube channel (here) to further visually (and not only through data and words) expand about the topic i am going to deal with.

As many of you probably already know, 2017 will mark the start of a new era for the LMP2 class, with new rules coming, a new spec engine built by Gibson and only four FIA mandated chassis manufacturers allowed to sell cars worldwide (Oreca, Ligier, Dallara and Riley-Multimatic).
There have been long debates about the need for a change in a class that seemed to work pretty well, with full grids pretty much in every championship, very good car variety, quick drivers and exciting races.

Anyway, the change is a fact now, so the only thing we can do in this early development stage is try to understand how exactly these new LMP2 machineries will perform.
Technical rules have changed in many aspects: first of all, 2017 cars are some 100 mm narrower than the previous generation ones (from 2000 mm overall width to 1900 mm), following the direction taken already since some years by LMP1; they also use a spec engine (provided by Gibson, as we mentioned already) producing more than 600 hp; they are also aerodynamically different: beside being narrower, they have also a wider rear wing and they are slightly longer than 2016 ones. Finally, because of a late addition of an air conditioning system, they have a slightly higher weight, moving from 900 kg to 930 kg.

All of these changes will surely lead to different performance compared to previous generation cars and there are already both speculations (sometimes pretty solidly-based ones, actually, mainly figures coming from the manufacturers themselves) and first test results suggesting that lap times will probably drop by about 3-4 seconds on a sprint track, assuming same track conditions and even more in Le Mans.

On my side, the excitement of seeing new cars hitting the track was made even higher by being these cars LMP2. Having worked previously on LMP2 vehicle models, I could not help myself but try to gather as many data I could and to build a 2017 LMP2 vehicle model, to see how it performs and behave on track and to get myself a feeling of the performance these cars will achieve this year.
I was lucky enough to be able to collect a pretty big amount of very detailed information about 2017 LMP2s, more or less in every area (engine, aero, suspensions), with the only exception being the gearbox and the tires; the gearbox itself will be this year a sensible area, with only three sets of gear ratios allowed for each chassis (including Le Mans, that will most probably use one of them just for itself), practically meaning only two set for the complete ELMS and WEC calendar; not too bad for this article purpose though, cause I think using the tire model I developed during 2016 could still be a good starting point with some minor changes and I can work out pretty easily some sets of gear ratios, to fit the engine curve and the speeds achievable based on the available data and the track the cars will run.

Analyzing the data I got was already pretty revealing, since this has highlighted immediately some first important points.
The engine has, as we said, about 100 hp more than a 2016 one, with its torque and power curve looking significantly different than, say, an “old” Nissan. The power band has moved sensibly upward, with less torque available at lower RPMs and gearshifts happening at higher revs than before.
The cars are now narrower and that normally doesn’t help handling, reducing a bit the cornering capabilities.
All the manufacturers also went to a slightly longer wheelbase, with values now above 3m, because of a more severe application of a rule about the chassis region immediately behind the driver seat.
On the aerodynamic side, the data I got shows important differences compared to what I have seen till now. Let’s start saying the information I got could well refer to a “x” development stage that is probably not the latest one and are not coming from a 1:1 wind tunnel; according to my experience and to what some engineers with much more experience than me say, this is surely driving some “errors”, although it is difficult to evaluate exactly how big the delta could be. Moreover, some later development and track testing could bring to different numbers in terms of aeromap.
Still, analyzing the available information about 2017 cars performance during testing and the results of my simulations, these data seem to be pretty realistic.
The most significant difference compared to what I have seen previously is a significant step in efficiency, with the new car producing similar level of downforce compared to the ones I worked on before but a lower drag.
For now, I only focused on the “Sprint” package, ignoring what could come out for Le Mans.

The result is what I think being a pretty realistic representation of what 2017 LMP2 cars (or at least one of them) will finally look like in terms of performance and handling.
Basing on this assumption I performed some simulation sessions on several tracks to analyze how quick the new cars will/could be.
Since I didn’t have a driver available to help me, I drove the model myself and that sure left some margin on the final performance that this car (or vehicle model) could achieve. I am pretty sure a good driver could well be up to a second quicker than me, if not more, even in a simulator. Still, comparing the 2017 spec performance to 2016 ones (with both cars driven by who writes) can give a very good indication of what to expect next year.
Beside this, we are going to take a look to the logged data, trying to evaluate how the main metrics look like.

Before to dive into the data analysis, here is the link to a video I recently did with the vehicle model we are dealing with. It shows some laps in Silverstone. Hope you like it! Again, it is me driving, so don’t expect too much!

The very first test I have done was in Monza, since this was one of the first tracks where new LMP2 cars tested, with some lap times that leaked through the press.
In particular, according to the info I found, these new cars have run in Monza with lap times around 1:36 minutes (Ligier, in particular, who drove in Monza back in October, I think). Interestingly, I was able to drive the model with a best lap time of about 1.37.
First thing to keep in mind here is that I suspect my vehicle model (and hence the data on which it is based) is on the low side of both Downforce and Drag, probably fitting a track like Monza pretty well, since here the straight line speed is crucial in obtaining competitive performances.
Unfortunately, I don’t have any 2016 LMP2 data to compare with on this track, but it could still be interesting to take a look to the logged to have a feeling about 2017 cars performance (please note all the following pictures/plot can also be found in High-Res in my Flickr channel).

 

monza-speed

 

First thing catching the attention here is, of course, the top speed the car achieves, which is about 308 km/h. Keeping in mind this is achieved with a Sprint Aerodynamic setup, it is interesting to think that this top speed is already higher of what most teams could reach in Le Mans last year (at least without any slipstream), with a dedicated low drag configuration.
This is sure the result of the much higher power the engine can produce but, partially, also of the particular low drag that the model has.

For reference, here below the plots of some other important metrics: lateral acceleration, longitudinal acceleration and RPM.

 

monza-lat-g

 

monza-long-g

 

monza-rpm

Monza doesn’t have super quick corners, but still the car is able to reach lateral acceleration marks in the region of 2.4 – 2.5 g in some occasions (see, for example, the two Lesmo corners and the Parabolica).
Not too much to say about the longitudinal acceleration, with very similar values to the ones we saw analyzing LMP1-L cars (the difference being driven mainly by the higher weight of 2017 LMP2 compared to 2016 LMP1-L, about 80 kg).
The RPM plot helps to see how the new engine will most probably be used, with higher power band of previous Nissan motors.

The test has gone forward in Silverstone, using a slightly higher Drag/Downforce configuration and shorter gear ratios, to better fit the English track, requiring probably the highest possible setup on the downforce side.
My best lap time was a 1.46.5, which is, as I said already, surely not even close to the best lap time we could expect next year, if track conditions will be good, but is already comparable to what the LMP1-L cars did in 2016 during the race (Qualifyingtook place in wet conditions).
More interesting, this lap time is some 3-3.2 seconds quicker than what I was able to do with the 2016 LMP2 model I worked on this year (about 0.54 sec/km); it is useful to keep in mind that this vehicle had anyway a higher downforce/drag compared to the 2017 one I am testing, so probably a more suitable setup for this particular track.
The gap between 2016 and 2017 lap times seems to match quite well to what media sources communicated, following indications coming from the manufactures. Also, this is very close to the gap that a lap time simulation would produce for the changes in weight, downforce/drag and engine power (compared to 2016) we are dealing with.

Let’s take a look to the data plots for Silverstone too.

 

silv-speed

 

First thing we can see is that, with this vehicle model and the setup I used for Silverstone, the car can achieve a top speed of about 287 – 288 km/h at the end of the Hangar Straight.
Silverstone is also an interesting track because drivers have to face the challenge posed by several pretty quick corners. The first one is Abbey, where a minimum speed of about 235 km/h was registered (with the corner being driven in 5th gear), while at Copse we can see a minimum speed of about 219-220 km/h (again 5th gear).
This translates to sustained lateral acceleration marks of about 2.7 g for both Abbey and Copse, but with peaks close to 3 g.

 

silv-lat-g

 

Car’s cornering potential (or how many g the car can pull in a certain corner) is driven by some main factors: downforce, tires grip, weight and track width, to name some. We are assuming, in the absence of better data, the same tires as in 2016. Anyway, downforce (which is lower than the 2016 LMP2 I worked on), weight, and track width all plays again achieving better performance.

Again no big surprises looking at the longitudinal acceleration trace, showing similar maximum values compared to Monza.

 

silv-long-g

 

The RPM trace shows instead the shorter gear ratios I used in Silverstone, to better suite a slower track where a slightly higher downforce/drag setup was also used.

 

silv-rpm

 

First gear is used only once, at the Loop. The engine works below 5000 RPM only once, at the left corner after Vale (before Club corner), where second gear is used.
This shows anyway how the power band is mainly located on the higher side, also compared to previous generation Nissan engines, which confirms some of the feedback given by some drivers during development which suggested that new cars, even if having so much power, should be relatively easy to drive also for gentlemen drivers.
The latest track where I tested the model on was Spa, to have again a reference against previous simulations I did with a 2016 LMP2.
The final lap time (again, with me driving, so most probably not the best lap time achievable with this particular vehicle model) was a 2.03.6. Again, more important is the gap between this lap time and the best lap time I could achieve with a 2016 LMP2 vehicle model; the difference between the two is close to 3.8 seconds, again about 0.54 sec/km; as I already told about Silverstone, I would expect the best real lap times to be lower than this, above all if the track will be in good conditions.
In any case, the gap between 2016 and 2017 LMP2 performance seems to match well with the media communicated 3-4 seconds difference between the two.

Let´s take a look to the simulation results. First to come is speed plot.

 

spa-speed

 

Again, first thing catching attention is the overall top speed of about 304-305 km/h, at the end of the Kemmel straight. Even more suggesting, though, is a minimum speed in excess of 270 km/h inside Eau Rouge, with the car literally flying through this iconic corner.
Very interesting is also the minimum speed at Pouhon, another very quick and grip limited corner, very interesting to evaluate the cornering potential of the car in a high speed condition.
The data shows here a minimum speed of about 206 km/h and a maximum lateral acceleration of about 2.7 gs, as shown by the following plot.
spa-lat-g

 

The same plot shows a maximum lateral acceleration in excess of 3 g at Blanchimont, although at such a high speed we are not really in a grip limited condition.

Again no big surprises in the longitudinal acceleration plot, with peaks always in the same region of what seen in the other two circuits and highest values achieved at the bus stop braking, where some bumps on the tarmac contributes to create higher peaks in the reading.

 

spa-long-g

 

Finally, we can take a look to the RPM trace, where we can see how the car (here using the same gear ratios as in Monza) doesn’t reach the 8500 mark at the end of the kemmel straight in sixth gear and operates significantly below 5000 rpm only once, at the bus stop, where first gear is engaged.

 

spa-rpm

 

It would now be interesting to let a quick driver to test this vehicle model to fully explore the performance potential of these cars, at least taking for granted that all my assumptions are correct.

Anyone interested?

It would be cool to work on this model and on such an analysis with a real LMP2 driver.

This article, together with the video I linked above (car driven in Silverstone), should anyway give a feeling of the level of performance to expect in 2017 in the LMP2 class.
The cars will be, as we could already expect, much quicker than the old spec ones and, probably close to or quicker than 2016 LMP1-L in many occasions. It will be interesting to see how far the performance will be pushed, also considering that many teams seem to be able to include very fast professional drivers in their lineups.

Our findings seem to generally confirms what communicated to media by the manufacturers, expecting a gap between 3 and 4 seconds between 2016 and 2017 lap times, but can show more in detail how and where these gap can be built and can give a more detailed idea about 2017 LMP2 performance.

Here again a link to Silverstone’s video.

It will definitely be a very exciting season.

Posted by: drracing | November 10, 2016

My new Youtube Channel

Hi everybody,

for the first time ever (i guess), i am posting twice in less than a month!

But this one will be really short.

First of all, i can finally hold in my hand the latest issue of 24 Hour Race Technology, featuring again an article i wrote, this time about the performance gap between LMP1 privateers and hybrids. The work behind the article has been done using driving simulation to investigate where the LMP1-L cars performance should be and why it is not there.

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The second important announcement is that i finally opened a Youtube channel , where you can see some of my vehicle models in action, with me or some better driver driving them around some of the tracks i use for my studies/projects.

The first video shows me driving in Imola the latest LMP2 vehicle model i worked on.
I know i am not the best driver, so be gentle if you want to leave a comment!

I will try to post more videos soon and always keep the channel up to date with the latest projects. It will also be showing something of a list of the vehicles i worked on on the simulation side and, basically, my available portfolio.
It gives also a chance to take a look to my home simulation setup.

I am currently working on something very very interesting and i hope i can share soon something about it here and in Youtube. It is a very new car…

In the mean time, i hope you enjoy it! Here is the link!

It is always the same story: I always promise myself I will post more often here but the time is always playing against me!

Again a long time since my last post, so I thought I could maybe write some updates about my latest projects and some of the things I have done during the last months.

First of all, I am extremely glad to say that, as it happened in 2015, an article I wrote will be published in the “24H Race Technology” magazine this year too. As for my latest post here, it will cover some performance studies I did with the simulator about LMP1-L cars. I hope many of you will have a chance to read it without getting too bored!

Beside this, I was pretty busy lately, both on fun (technical) and private side. I moved into a new apartment, this meaning that for a certain time I could not really access my pc freely and all the simulation work had to stop. Anyway, the new apartment offers me a new room for my simulation hardware, this meaning I could even post some videos sometime in the future because I know have some space for some interesting upgrades and I should be able to have something to show on video without having to shame myself!
Luckily, before I moved, I had some time to both perform the simulations required for the above mentioned article and to support an LMP2 team I came into contact during the season.

The latter has really been a very interesting task, first of all because these cars are really amazing pieces of engineering and show some incredible performance, but also because a new project always means coming into contact with new data, acquiring new knowledge, experience and new perspectives even when looking to known problems.
What made this project even more interesting though is that, for the first time, I was involved in a study only and exclusively focused on setup optimization.
As you may know, I worked already on other LMP2 cars during the last two years, but the whole work was always focused on creating something mainly aimed to driver training. This doesn’t mean that the previous projects had no interest from an engineering perspective or that the modeling had been approached differently, since the goal is always creating a model that performs as close as possible to the real vehicle.
This basically means following some basic, important steps:

  1. analyzing the available car data
  2. creating a vehicle model that matches these data as good as possible
  3. validating the model against track logged data to be sure there are no unexpected differences
  4. using the model for training (or other) purposes

This is exactly the same thing I did this time too. What was substantially different was mainly how the fourth point of the above list was tackled, since the final goal was to help the team to improve their setup and, finally, their lap times.

Before we go on describing shortly how the project has been run and the results we obtained, I would like to focus the attention on what I consider an extremely interesting point.
As you may know, the software I currently use for this kind of projects is rFactor, since I think that its physics is still among the best available on the market in this price range.
Since some time, looking to the matching between simulation results and logged data I achieved, I started thinking that actually, up to a certain point, it must be possible to use a “simple” software like rFactor (which actually is not that simple) to perform more “engineering related” simulations, more or less what an engineer could need to improve, at least directionally, its car’s setup.
Of course, as we said thousands times, such a software have some limitations, mainly connected to rFactor being still a cheap product, which must have a much lower complexity than professional programs like the ones used by OEMs or big teams (which also require a completely different computing power); this very expensive tools surely offer much more flexibility also in terms of igniting subsystem models built up in external environment.
As I briefly mentioned in some previous articles, there are some areas where the user must be careful in order to get accurate results (see, for example, setting suspension geometry and angles, like camber and caster, or the way some strongly non-linear devices like the bump stops are modeled).
Some of these limitations can be overcome, knowing how the software works and how certain things are calculated. Paying attention to some aspects, it is possible to achieve extremely low errors, both when comparing vehicle data during the modeling phase (like tire forces and stiffness, aeromaps, suspension kinematics, etc) and when checking simulation results against real logged data.

Having the chance to use a commercial software like rFactor to perform engineering work opens to a different approach when using “home” driving simulation.
Of course, there is still a point in running multimillion simulators with extremely complex (and heavy) software and models and I am in no way saying that all of this can be simply exchanged for a much easier tool. On the other hand, it is now clear to me that, even for people/teams not able to access the expensive and complex tools that big teams have, there is still room to run productive simulator sessions with the aim to also better understand how the car works and where it can be improved.
In general there are still some phenomenon which are extremely complex if not impossible to simulate properly, mainly because there are no real data about them (see for example how much tire behavior change for a certain temperature variation, or how much aerodynamic properties change with roll angle) and conditions which are nearly impossible to replicate (we discussed already, for example, how much track conditions could change during a weekend, depending on different factors; different cars running on the track during the same day could even change the ideal driving line from one day to another). Experience surely help to replicate all these aspects in a realistic way, but perfection, of course, doesn’t exist and there is always a point behind the team being ready to do more or less everything to have more track time.
A simulator will always be a simulator, not the real thing. But being able to understand and to investigate how certain things work and (maybe more important) why something happens can be a really exciting and useful experience, at any level.

Something that further confirmed that driving simulation (even in our “simple form”) can still be an extremely useful tool even from an engineering perspective has come to me last May, in Spa, during the WEC race weekend, where I had the chance to speak to the drivers of Strakka (LMP2 team running a Gibson car in WEC this year). Strakka runs a professional simulator and they are also using rFactor (as far as I know). The simulator is mainly used by the team for his own programs (they have also cars running in the Formula Renault 2000 and in the Formula Renault 3.5 V8 series), but is also hired to private customers from time to time.
Strakka drivers confirmed to me that they not only use the simulator to evaluate setup solutions before a race weekend or a test, but also rerun the solutions they found on the track at the simulator after event, to double check them and further confirm that they went in the right direction (the simulator offers a “disturbance free” environment for testing). Moreover, a certain setup setting can thus be evaluated more in details, also looking to the effects they have on parameters that would be hard to measure and trace at the racetrack. Again, it could be an invaluable help to understand not only what happens but also why.

Of course, in order to use the simulator for setup investigations, it is even more important to have an accurate and reliable model that produces realistic results. This point cannot be stressed enough. Accurate data are key for whatever simulation, doesn’t matter if it is run on a driving simulator or with other tools.
The good thing is that building up a vehicle model and running simulation can sometimes also be a way to better understand your car and your data, even when they are affected by any sort of flaw. Sometimes, comparing simulation results with real data is the best way to identify measurements inaccuracies, or the areas where the model or the available car data could be wrong.

The project I was involved with has actually started with a study mainly related to tire data analysis, to try to understand why what was working pretty well in 2015, was not doing the same in 2016.
Analyzing purely the tire data (so ignoring a lot of other issues also connected to tires, see for example their thermal behavior and assuming the tire data were telling the truth!), was immediately evident how much different they were behaving compared to 2015 and the balance upset they could produce on the car. Even only looking to the vertical stiffness, it was already possible to identify some of the required setup changes needed to try to compensate for the different characteristics and come dynamically to similar ride heights.
We already pointed out in other articles how important ride heights are for LMP cars and how the ride heights sensitivity is for overall downforce and balance. So it is immediately evident how important even a basic chance, like the tire vertical stiffness, can be on cars final performance.

Anyway, a deeper look to the Pacejka coefficients set and to the relative plots was enlightening to understand where and how car’s balance and behavior could have changed and which performance differences to expect.
Above all at the front, 2016 tires were producing completely different friction levels and trends compared to the 2015 ones.
The team I worked with was somehow hit by these changes and wanted to confirm what they saw on the track through data analysis with some simulation.

As described in the list above, the first step has been going through all the available data and creating a vehicle model as accurate as possible. Of course, the validation phase has also played a central role about this, above all to be sure about how much exactly the tire forces needed to be scaled down to obtain realistic performance. Luckily enough, previous experiences came to help and the results were very close to the real car already at the first attempt, as confirmed comparing real and simulated data (here in Imola).

lmp2-imola

The light blue trace refers to the real car, while the orange one is the simulated one. Not too much effort was spent to fix the small difference in top speed (probably a small flaw on drag data, to go back to what we discussed above and an example of a situation where, simulating, you could spot an error on the provided car information compared to the real car logged data), since this particular issue was not going to influence how useful this study could be. Anyway, I still think that this graph confirms once more how close you can get in terms of vehicle performance to the real thing using rFactor and a good vehicle model.

Once the validation phase was over and matching between real and virtual vehicle was acceptable, I started working on the setup. The approach agreed with the team was for me to know nearly nothing about how they were setting the car, not to be influenced in anyway on the direction to be taken.
Basing also on my previous experience, I set up the car at first “on paper” (or, better said, in excel!), using some reference metrics like the roll gradient, Lateral Load Transfer Distribution, dynamic ride heights, corner and axle natural frequencies and targeting from the beginning values that, based on previous experience and on the analysis of the available data, I would expect to work.
For some “non-linear influenced” parameters, like ride heights, I did some checks calculating the load I would expect during certain maneuvers, evaluating the dynamic ride heights basing on these loads and using a certain setting for each subsystem (in this case, for example, springs, bump stops, static ride heights, etc). This allowed to also have a basic settings for parameters like the bump stops and their free gaps.

Once the car finally hit the track (or, better, the vehicle model was driven on the virtual track) the work I did was pretty much similar to the one of a “normal” race engineer, basically running sessions, evaluating logged data and driver feedback, trying to understand which areas could be improved or, simply, which differences there were between a session and another and, based on the conclusions I came to, finding and testing new solutions. And then repeat again and again, till the time I had at my disposal was over!
The interesting thing about doing something similar with a simulator is that you have perfectly repeatable conditions (track conditions, track temperatures, tires wear, engine power, atmospheric conditions, etc), so you can potentially really isolate the effect of each change on the car-driver package performance. Another important point is, of course, that, if time allows, you can test a lot of solutions and do as many back-to-back tests as you want, without having to pay anything or without having to explain to your boss about why you are testing the same thing again!
Since a driver is a human being (even professional ones) back to back tests can sometimes be a good idea to isolate driver’s effect on final performance and understand if a certain setup modification has really produced an improvement and why. It is also good to remember that race cars (and an LMP2 car in particular) are complex systems, where each parameter could potentially influence also many others. This means, it could well happen that a certain change produces a different consequence when married with a certain base setup than what it would do starting from another basic setup. Another good reason to do back-to-back tests is thus to really understand if a certain final solution, where we came after many small changes is really our optimum or not.
And now, we could start a philosophical discussion to decide if an optimum really exists, but I will avoid it.

The simulation work has focused on nearly every area of the setup, excluding the aerodynamics hardware settings (like wing angle, for example) which was basically taken as an input. The reason behind this is that the team already had a chance to perform its own evaluation and simulation (mainly lap time simulations) to define the best aero solution for the track they would be going to race in.
Effectively, saying I didn’t work on the aerodynamics at all would anyway not be true, since some mechanical settings have an influence on other parameters which directly define how much downforce and drag and which balance you have (see ride heights, for example).
Areas where we particularly focused were vertical stiffness (both in terms of corner springs and third elements, with particular focus also on the bump stop), dynamic ride heights, roll stiffness and total lateral load transfer distribution and differential settings.
There have been initially an “adaptation time” for the driver to find the most effective way of driving the car on the chosen track. As every driver probably knows, “practice makes perfect”, thus meaning that actually, nearly every driver would probably continuously improve him/herself for the whole time he/she goes on driving on the same track with the same car, but normally this happens at the beginning at a substantially higher rate than after some track time, thus allowing us to assume that after a certain practice time (its length depends strongly on driver’s skills and experience), if we log an improvement on the lap times following a setup change, that should be really a result of the car-driver package achieving a better performance and not only of the driver improving his technique.

As a side note about this point, it is worth to say that, sometimes, a setup making theoretically the overall car performance envelope bigger (see, producing better laptimes in a lap time simulation) could even not be the one achieving the best performance with the driver in the loop. This happens more often with non-professional drivers than with professional ones, but could also be the case with very expert ones. These situations are exactly the ones that make having access to driving simulation (even if in a “simplified” form, as in our case) even more useful.

I will not go through every setup change we tested, since it would take too much time and too much space. But I would like to focus on some specific corners and just one setup modification, to show the effects of a certain change on car behavior, driving style and overall performance.

I will try to keep it simple and just analyze the effect of a change on the rear roll stiffness, with a difference between the two cases of 17% of the rear antiroll bar stiffness contribution (this meaning that, in our second case, the rear antiroll bar has 1.17 times the stiffness it has in case 1). This is just a very simple case which doesn’t actually show the full potential I have seen and, more important, the parameters influencing the final performance the most, but I think it is still useful to identify if and how to obtain some setup related results (even at our “easy” level) and which intricacies could come from such a simple setup change in a complex car like an LMP2.
The final lap time gap between the two setups was around 2 tenths (the stiffer being the quicker); nonetheless, as we are going to see, some corners show very different performance and some handling surprises.

The first track section on which we will focus is a slow corner, driven in second gear, which is located at the end of a straight where the car travels downhill. In this turn, the car normally exhibits understeer, above all in corner entry.
The advantage of the stiffer rear antiroll bar here is immediately clear looking at the speed trace.

corner-3-speed

In the above picture and in the following ones, the red trace refers always to the softer rear antiroll bar setup, while the blue one always to the stiffer one.
As we may immediately see, the advantages brought by the stiffer setups are here evident: the driver is able to hit the brakes a tiny bit later, let the car turn in more easily, thus having a higher minimum corner speed and still being able to go on the throttle a bit easier.
The steering trace confirms that the car has better balance, with less understeer in corner entry and at the apex. Namely, the steering angle is smaller, still being the cornering speed higher, as we saw.

corner-3-steering

Looking at the corner exit, we can also notice a very small steering correction, underlining how the car tends to now have even some on-power oversteer in corner exit. In this particular event, hitting a curb in corner exit was also destabilizing the rear end of the vehicle a bit.
Also the throttle trace, here below, seems to confirm what we saw. The driver get away from the throttle a bit later coming to the braking zone and goes again on the throttle a tiny bit before, with only a small hesitation at the same instant when the steering correction takes place.

corner-3-throttle

On a more technical side, it is interesting to take a look at how the front and rear roll (here calculated as a difference in mm between left and rear wheel travel) changed because of our setup modification.
Looking at the Front Roll trace, we see a small difference, practically only driven by the overall roll stiffness being changed because of the different rear antiroll bar setting.

corner-3-roll-front

At the rear the difference is more evident:

corner-3-roll-rear

Regarding the roll angle, it is worth to mention that the simulations don’t consider any chassis flexibility and all the components are simulated as ideally stiff.

Let’s now take a look at what happens in two left, mid speed corners, one following another and with cornering speeds in the 140-170 km/h range.
In this case, the softer roll antiroll bar (red trace) seems to work better, with the driver carrying more speed inside both of the two corners.

corner-45-speed

The difference seems to be even more accentuated in the second turn, where the driver even brakes a bit later and with less energy, carrying more speed during the complete corner duration.
In the first one, which is the slower one between the two, we see the stiffer rear setting producing an effect somehow similar to what shown in the previous example in corner entry, with the driver being able to brake later; but here it happens at the cost of a lower apex speed.
In the second corner, as we mentioned, the driver seems to simply have more confidence (as per his subjective feedback) and/or grip with the lower roll stiffness setup, being able to brake later, decelerate less and carry more speed.
In general, the lower rear roll stiffness solution seems to work much better in these two corners, improving significantly driver’s confidence and overall performance.
The steering trace, anyway, reserves some surprises: we would expect the driver using a lower steering angle with the stiffer antiroll bar, confirming a less understeering tendency. But actually the steering angle doesn’t differ too much in its magnitude between the two cases, with actually the softer bar setup showing a slightly lower steering angle in both corners.
corner-45-steering
This seems to show a car with a less pronounced understeer in these two corners when using the lower rear antiroll bar stiffness setup. How is it possible?
This is confirmed by the following picture too, showing a channel I always use as a kind of “understeer-oversteer index” and which compares the actual steering angle to the ideal one, normalizing the difference between the two on lateral acceleration. When this channel is positive, we have “understeer” (or, anyway, a steering angle bigger than the ideal one).

corner-45-undover

All what we see here seems to go against what we would theoretically expect, when reducing the rear axle roll stiffness (namely, more understeer because of a more front biased Lateral Load Transfer Distribution).
The reason for this is probably to search in the higher speed that the driver carries into the corner with the softer rear antiroll bar, as a consequence of “trusting” the car more: this means, among other things, more downforce (ignoring how much downforce changes with ride heights, more speed means normally more downforce, as aero loads depend on the square of speed) and, because of different dynamic ride heights, a slightly different Aero balance (downforce distribution between front and rear axle), as shown in the following picture (where we show the front downforce distribution as a ratio between front vertical loads and overall vertical loads: higher values mean an higher portion of downforce acting on the front axle). Please focus your attention on the areas highlighted by the green circles, since these are the points corresponding, more or less, to the two corners apexes.

corner-45-aero-balance

Beside this, the driver is also driving slightly differently, as we may see not only looking to the braking points and to the steering trace, but also to the instant cornering radius of the path he is traveling on:

corner-45-radius

Above all in the first corner, it is evident how the cornering radius reduces quicker and a bit before with the lower rear antiroll bar stiffness setup. This means the driver closes the line, pointing the car to the apex, a bit before, than with the stiffer solution. As a consequence, he tends to release the car a bit before, leaving it sliding toward the outside of the corner.
As a sanity check, the two pictures below depict the front and rear roll respectively, calculated, as already explained, as the difference between left and right wheel travel.

corner-45-roll-front

corner-45-roll-rear

As we already saw analyzing the previous corner, both plots show an higher roll angle in the “lower stiffness” case, as we would expect, with the difference between the two cases being bigger at the rear, where we actually decrease the antiroll bar stiffness.

What all of this show is that, while in the first corner the stiffer bar seems to work better and the car has less understeer, as we would think, in these two particular corners more factors are contributing together leading to exactly the opposite reaction than what we would expect on paper, when setting a softer/stiffer rear antiroll bar, at least from a handling perspective. Not only the vehicle is quicker with a setup that produces overall slower lap times, but we actually identify a handling reaction which seems to go against basic vehicle dynamics theory.
As you may have seen, also the driver is playing a key role here in defining how the car reacts and this is something that a session in a simulator can show at best, while such a thing would be difficult to predict or simulate with a different kind of software. How the car behaves and performs on track is always influenced by how the driver can manage certain handling features and how he reacts to them (we have seen in this case how our driver was most probably using slightly different lines with the two setups).
At this stage, as we said, we are also not really simulating complex phenomenon like aerodynamics roll sensitivity, just to say one. Still, even such a simple setup change has shown a reaction that we would “normally” not expect.
Still, there is an objective change in how the car handles depending directly on vehicle dynamics and aerodynamics reasons, which not only is somehow counterintuitive basing on a simple reasoning focused on lateral load transfer distribution, but also can only really be identified in a simulation environment where also the driver is playing a role.

The conclusion is that, even with a cheap software, we could still extract a lot of useful indications about car setup and we can work also on technical aspects, not only on driver training.
For the record, the setup we came to using the simulation was extremely close to the one the team finally used on track, even after the “day specific” tuning.
As I said probably more than a thousand times, it is absolutely key to have an accurate, reliable and well validated vehicle model, to be sure to be capturing as much as possible what the car really does on track. For a study involving setup investigations, this is even more important than when working on driver training “only”.

What I find amazing after conducting this study is that it proved (to me in the first place) that, at least up to a certain point, a cheap simulation software like rFactor can even be used for pretty useful setup investigations, leading to results which are both close to the real world and helpful for the preparation/development work. Of course, track time is always the best a driver and an engineer could wish, but when this is not possible and/or when certain phenomenon needs to be analyzed in more “controlled condition”, the simulation can be a real deal. In general, it always helps to understand not only what happens but, even more important, why.

I am now even more convinced than before that a similar tool could be of real benefit for every team, not only for driver training but also from an engineering perspective.

Posted by: drracing | April 12, 2016

Perrinn MyP1, LMP1-L and driving simulation

Hi everybody!

This post will be about a project i actually worked on last year, but never found the time to write about. And, finally, will be again a bit about something technical.
This will be a long (but hopefully not too boring) entry, so take some time, some drinks and seat comfortably!

As I already wrote in some of my previous posts, some time ago I decided to create a vehicle model of the Perrinn MyP1, the open source project created by Nicolas Perrin with the intention to share with the community much of what is connected to the design of an LMP1 car; Perrin’s dream was to find the funding to finally build the car and bring it to Le Mans, using a new and exciting approach where a whole community could potentially be involved. Unfortunately, this dream didn’t come true yet and, most probably, will sadly never do: the project is now already two years old and, beside the funding collected through the chance given to people to subscribe to it and to access all the data available, I am not aware of any big backers who materialized till now.

This is for sure sad, but takes nothing out from the goodness of the initiative from Mr. Perrin which, if nothing else, gave to many curious and passionate people a chance to access some very professional material, that would normally be kept and guarded very secretly.
This includes a lot of detailed CAD 3D models, aero data (coming from CFD simulations), suspensions data, something about the tires (although this was probably the least useful provided data) and still much more. Basically, what is still missing is only what should be provided by external supplier and cannot be shared for legal reasons; this unfortunately includes the engine, which is a pretty sensitive element to define any car performances.

The whole project has been developed planning to build a car to compete against the big manufacturers (Toyota, Audi and Porsche) thus the original idea was to include also a Hybrid system, targeting the 8MJ class.
Anyway, my idea have been from the very beginning to use it as a base to evaluate how quick an “non hybrid” LMP1 (like the privateers are, see Rebellion and ByKolles) could be: Perrin data and general design would serve as a realistic foundation for a LMP1-like design, about which would otherwise be impossible to find any detailed information.

Assuming that Perrin did a job at least as good as Oreca did for Rebellion and Adess engineering did for Kolles, my assumption is that MyP1 “platform” can be used to understand, at least on paper (or maybe we should say in a simulation) how quick such a car (LMP1-non hybrid) could approximately be.
As I will show later on, I reworked a bit some of the settings provided by Perrin with the goal to optimize vehicle’s performance/driveability, when something was looking not perfect to me (of course not “cheating”, like artificially increasing aerodynamics efficiency, but only working on setup parameters, like springs, antiroll bars, gear ratios, ride heights, etc) and I tried to approximate the best I could all the missing data: as I said, the complete powertrain system is a very important example (not only the engine was not shown in CAD, discussed or described, but also important parameters like gear ratios or differential setup were not available at the time I built my vehicle model).
The model and the final results are anyway probably still not really on the edge, in terms of performance, but could still form a good base to get a feeling about how such a car behaves.

But let’s start from the very beginning. How the car designed by Perrin looks like and which parameters are available about it?

As I initially said, a pretty big amount of CAD data has been shared with the “users”, including the complete bodywork, suspensions, cockpit items, wings and diffusers.
As soon as it has been presented, a couple of years ago, it was immediately noticeable how the design took a “different path” (compared to the other manufacturers) in some areas, like the engine cover and the front aero package: the first is very much “Bubbly”, with a shape that doesn’t resemble any of the Manufacturers design and looks, for somebody not too much into aero design like me, a bit strange and apparently too big; Perrin ensures anyway that this has no real bad effect in terms of performance.
The later shows a high nose, with a clean front wing design and with the nose itself being smaller than the one of other cars using a similar front end concept, see Audi and Porsche.
Interesting is also the position of the two air intakes immediately beside the nose, but a bit more rearward; it is a solution partially similar to the one proposed also by the unraced HPD LMP2 car at the beginning of 2015, although in that case the intakes were at the very front of the car, just beside the nose itself.

MyP1 Iso view

MyP1 Front viewMyP1 side aero

MyP1 side view

 

Perrin’s creature features a 2950 mm long wheelbase, with front and rear track widths being very similar to each other and in the region of 1545 mm (the four tires have the same dimensions, although the rims are different front to rear, as also seen in other LMP1 cars; the main difference is normally a different wheel offset).
According to the info provided by Perrin himself, in a non-hybrid configuration the car should be able to stick to the allowed minimum weight of 850 kg, of which about 48% should act on the front axle statically (also with the driver on board).
According to the designer, the CG height should be around 280 mm higher than the reference plane, which is not the lowest point of the car, as we will see shortly analyzing the front and rear suspensions.
Below the floor find in fact place a wood plank, as mandated by the rules; in its thickest point, it is 25 mm thick. This means that the CG should be more or less 305 mm higher than the lower point of the car body, assuming an idealized surface running through the two thickest points of the plank, one at the front and one at the rear.
MyP1 Iso low view

MyP1 Front aero

 

A very important point regarding dimensions and inertial properties is also linked to the moments of inertia of the complete vehicle that, as i mentioned in some older entries, play a very important role in defining how the care behaves dynamically. Since these data are normally not available, I use a simplified approach to estimate them, which consists in dividing the car in several blocks with simple shapes, calculate each block moments of inertia (basing on the mass assigned to it, which is calculated to finally achieve the “desired” static weight distribution, also basing on each block position) and then in “moving” each block’s effect to the CG using parallel axel theorem, to finally have the Moments of Inertia of the complete vehicle referred to the car’s CG (as required by rFactor).
Going “under the skin”, it is evident how the design was driven by the aerodynamics, including all what concerns the front suspension. Here, another important element is surely the maximum lateral width allowed by the rules, which forces to use relatively shorts control arms, making it harder to really optimize the geometry.
The front suspension employs a typical double wishbone scheme, with unequal length arms, pushrods and torsion bars. The antiroll bar is connected to the rocker through (more or less) vertical rods and it seats on the lower portion of the chassis, attaching to the front bulkhead. Although not shown, two linear dampers are meant to be used (this is one of the contents belonging to suppliers and that cannot be shown in CAD).

I analyzed it using a very well known Multibody software, focusing only on the pure control arms/Steering geometry (not on the motion ratios, for example, since the wheel rate contribution of each corner spring and of the antiroll bar was provided “separately” by Perrin’s data).
The results show a very high kinematic roll center (much higher than the rear), a pretty sensible track width variation with respect to heave motions, a relatively big scrub radius and a pretty small caster angle.
Also, bump steer is not really optimal, although not being out of sight. This underlines once again some of the compromises accepted in the design phase for the front axle for the sake of a higher overall benefit, probably.
Camber gain is also not too high, but this is not necessarily bad: for sure, together with the low caster angle, it doesn’t help to have an “optimal” tire vertical inclination for the outside tire in cornering situations, but it is also probably not a performance killer.

To be honest, if I had to wish something for “my ideal front suspension”, it would definitely not look like this. But this is only my opinion and I am sure there were reasons to justify the decisions taken, including (as we already mentioned) maybe freeing some space on air’s way through the front aerodynamics devices and to the sidepods. And anyway, this is only my personal opinion, which comes from a very different experience than the one of Mr. Perrin and surely not at such a high level.
In any case, some of the compromises connected to front suspension geometry are strictly linked to the short control arms in use, which are a direct consequence of the rules in place (maximum track width, chassis width in the driver’s leg zone) and also of the intention to design the front end leaving the chance to package a front electric engine to power the front wheels (not shown in the pictures).

MyP1 Front susp - Front w chassis

MyP1 Front susp - Front

MyP1 Front susp - Iso

 

MyP1 Front susp - Top
Beside the aero and package driven compromises we mentioned, it is easy to recognize a certain “F1 style” on the complete front suspension concept, see for example a very high attachment of the lower wishbone to the upright (relatively close to wheel center), to free air’s way in the region where it exits the front wing/diffuser (but surely not helping overall stiffness in cornering situations) and, also, the upward oriented front wishbones.

 

MyP1 Front susp - Front Upright - ISO

 

 

 

Here below a summary of front suspension’s most important features. Please note that the front ride height (plank) is referred to the lowest point of the car, namely the lowest point of the wood plank sitting below the floor and mandated by the rules (please see notes above about this part). It actually doesn’t extend up to the front axle, but, being its front edge pretty close to it, this approximation should not drive a big error.

 

Front Susp summary
I didn’t perform any calculation about the anti-effects, but it looks like there is something going on in this regard. A picture tells more than a thousand words!

 

MyP1 Front corner side view - Anti

 

Looking at the rear end of the car, Perrin provides the CAD models of the complete suspension (again with the exception of dampers and springs, probably third parties properties), the bell housing, the rear end of the gearbox and a solid block working as a placeholder of the main portion of the gearbox itself.
As for the front, the car employs a double wishbone scheme with pushrods, rockers and a general layout that could potentially allow both torsion bars acting directly on the rocker or coil springs mounted coaxially to the damper (I suspect the latter being actually the planned solution). The system allows the use of a third spring-damper unit, seating between the two rockers (not shown in the pictures) and activated by a traditional T antiroll bar, connected to the third element on the middle of the upper arm.

Again, it is evident how much of an influence the aerodynamics had on the general layout: this becomes clear, for example, looking at how low the rear top wishbone is attached to the upright, with its outer attachment z coordinate seating extremely close to the wheel center. This choice, which surely has a strong impact on suspension overall camber stiffness, had most probably a very beneficial effect in allowing the rear engine cover to be as low as possible. As far as I have seen, something very similar should also be used by the “big guys”, Porsche, Audi and Toyota.
Another interesting point is also the pushrod attachment to the upright being very low, probably (but surely not only) also to have a better kinematic alignment with the rocker plane.

 

MyP1 Rear corner side view - Anti

 

MyP1 Rear corner iso view - upright

Talking about suspension, also the rear one seem to be partially compromised for the sake of a bigger overall benefit, although the longer control arms undoubtedly helps to improve the situation.
I analyzed it again using the same Multibody software I used for the front one, again focusing on the pure control arms driven effects, since the wheel rates were anyway provided separately.
The results show a better bump steer, compared to the front axle, a much lower rear roll center (and also more in the region where I am used to see it, from my previous experience), a higher camber gain (which is, in my view, beneficial at the rear) and still a pretty high track width change, although not as high as on the front axle.

A summary of what I found is shown here below:

Rear Susp summary

 

For the ride height, what I said about the front is of course still valid also at the rear, with the plank value referring to the lowest point of the car, below the plank thickest area.

 

MyP1 Rear Iso view

 

MyP1 Rear Iso view 2

 

MyP1 Rear top view

 

In general, the design looks very neat, although any particularly new solutions have been deployed, compared to the standards visible of F1 cars or other LMP1 vehicles.
It is interesting to notice how all the rods (pushrods or tie rods) that need to have an adjustable length use a spacer system with plates in between, which is (or at least was) a standard in Formula 1. I am not totally sure of what the other LMP1 manufactures do, about this particular feature. For sure this is not a standard in LMP2.
It is also interesting to notice how the front upright is mounted to the upper control arms where the “clevis” on the arm side, while for all the other connections between control arms and upright (front and rear), the opposite solution is in place (clevis on the upright/chassis side).
Staying on the suspensions side, the vertical stiffness (wheel rates, spring contributions) suggested by Perrin in the provided datasheets was pretty high, with a stiffer setting at the front than at the rear (270 N/mm at the front, 230 at the rear).
According to the unsprung mass provided by Nicolas, this lead to a pretty high suspension natural frequency, both at the front and at the rear (in the region of 6Hz at the front, 5.4 at the rear).
As we will see later on, I had to work on the setup a bit, because the car was really hard to drive. More on this later, but I also tried to reduce the corners spring stiffness using third springs, with positive results.
A role about this issue was surely played by the tire model, which was basically carried over from my LMP2 project, but using four rear tires, since in LMP1 all the tires seem to be the of the same size (31/71-R18). For intellectual honesty, I have to say that, according to the information I have, LMP2 tires are vertically stiffer than many Michelin tires with the same dimensions (Michelin is used by all LMP1 work teams) and this could play a role, increasing the overall wheel rate; but, since in 2016 all the non-hybrid LMP1 teams should switch to the brand now producing LMP2 tires, this approach was (accidentally) useful to have a better picture of how the car should behave or could be set. Of course, we cannot know if the new LMP1 tires will be the same (or have similar features) as the LMP2 ones, but this was the only trustable data I had, so I had to stick to it.

Regarding the Antiroll bars stiffness, the material provided was pretty unclear, showing actually a negative stiffness for the rear, probably because of calculations done to achieve a certain roll gradient.
The reference value for front antiroll bar stiffness at ground (so its wheel rate contribution) was 380 N/mm. Even with no real antiroll bar, this would produce already a pretty high roll gradient, mainly because of the high roll axis and the relatively low CG. Anyway, rear roll stiffness was an important study parameter in my simulations, more on this later.

Moving away from the suspensions, a very important area, where no data was provided, as I said, is the powertrain.
I tried my best to replicate a Turbo engine with more or less the same features of the AER used by both Rebellion and byKolles, the two teams currently competing in LMP1-non hybrid class. Probably it is more correct to say that I tried to replicate the known features of this engine, since I found few or no data about it.
As far as I know, it should rev pretty low, with a shifting point around 7000 rpms, but I could not find any useful data about the overall power. I tried to enquire Mr. Perrin himself about a realistic power figure for an LMP1 engine and it came out he also had no trustable info to provide. I also had no direct contact to people working on the AER engines and all the people I could talk to were not really able to help or could only provide figures flying everywhere between 500 and 900 hp.
Sticking to the fuel flows (updated in September 2015) allowed by the FIA (106.5 kg/h, for the LMP1 non-hybrid class) and to what I think (hope) could be a realistic BSFC figure (219.5 g/kWh), at least for the peak power, I derived something in the region of 485 kW (640-650 horsepower, depending on the definition of hp/ps you want to use).
To draw a realistic power curve, I based on an engine produced by a tuner with more or less the same features (6 cylinders, low revs, high torque and about the same power) and came to something like this:

 

Engine

 

The gear ratios were not provided and I tried to build up something realistic, basing on engine power, drag and rolling resistance. For this first study, I only considered the sprint aerodynamic configuration, assuming a fixed rear wing angle and, thus, just one possible aero setup; it made it easy to define a single set of gear ratios that would more or less fit every track (not optimal, I know, and probably not completely realistic, but still enough to get a directional performance estimation, in my opinion).
The gear ratios I used at the beginning are listed here below and are based on the input from Nicolas, stating that the plan is to go with a 7 gears solution:

 

1st: 14/34
2nd: 16/31
3rd: 19/31
4th: 21/29
5th: 21/26
6th: 20/23
7th: 23/25

Bevel: 22/21
Crown and Pinion: 15/43

 

As we will better see later on, I had to work a bit on the ratios to allow a better use of engine power even at the exit of low speed corners, using lower gears, without relying too strongly on traction control.
As you have seen, the engine is pretty strong in terms of torque.

We will cover the setup work more in details a bit later, not only regarding the gear ratios.
Before we move to the next topic, though, I think we need to spend a couple of words about a pretty important missing input, which is again a part of the powertrain system: the differential. It has a very sensible influence on how the car handle and it is normally a tuning parameter whose effect than can be felt very clearly by experienced drivers.
Unfortunately, there were no info about it in MyP1 server, as the gearbox itself was not yet really defined in details.
To be honest, I don’t even know exactly what kind of differential setup do the other LMP1 cars use, being them hybrid or non-hybrid.
For my simulations, I have assumed a Salisbury Type limited slip differential, with ramps and clutches to set the desired locking effect and with a tunable internal preload.
Without going into details about how such a differential works (this could require an article in itself and is anyway very well explained in many other articles on the web), it can suffice to say that its tuning was initially based on the experience I had with other LMP cars and was finally brought to slightly different settings, with a pretty high locking percentage in braking (around 80%), an average one on power side (about 30%) and some 100 Nm of preload. All of this has made the car pretty stable, maybe even increasing a bit too much the understeering tendency on corner entry and “helping” in showing some other small issues, mainly connected to the front suspension. On the other hand, I guess no endurance drivers would probably love an understeering car.

Another very important performance area is, of course, aerodynamics.
All the available Perrinn MyP1 data was derived, as far as I know, performing CFD simulations, since no real car or wind tunnel model has been built so far.
According to the shared information, Perrin and his team worked mainly on the low drag configuration, meant for Le Mans, including testing the virtual model at different ride heights, to have at least a feeling about ride height sensitivity.
Regarding the Sprint configuration, only minor data was provided, giving baseline figures of drag and downforce/balance. The data sheet provided also included the effects of increasing or decreasing both front and rear wing inclination in terms of drag, downforce and balance shift.
To derive a complete aeromap (downforce, drag and balance depending on front and rear ride height), I used a combination of the data provided about the low drag setup (scaled up) and my previous experience with similar cars.
The reason behind this decision, instead of simply relying on Perrin data (appropriately scaled to match with the downforce and drag levels provided for the sprint configuration), is that, as far I could see for other projects, the low and high downforce setup of an LMP car behaves completely differently not only in terms of absolute drag and downforce, but also from a ride height sensitivity perspective. The reason behind this is that, if the rules allow it, the team will try to also optimize diffusers shapes; they are the parts creating probably the strongest ride height dependence.
One thing to keep in mind is that, anyway, we cannot say anything about the accuracy of the data provided, also considering the complete design was developed aerodynamically using “only” CFD simulations.
I personally have no direct experience in this field and I cannot say much about the accuracy that can be achieved and I heard very different opinions about it. What is for sure is that, if big LMP1 and F1 teams invest so heavily in wind tunnel testing, there must be a reason.
Data accuracy is anyway an extremely sensitive topic, for anybody doing simulations, no matter in which field. Every measurement is subject to an error and race cars are no exception: my experience taught me also how much of an influence the “human factor” could have, for example calibrating a sensor poorly or simply ignoring other issues in the measurement chain.
Beside this, every data derived through lab measurements, see for example a wind tunnel test, is something that refers to more or less ideal conditions, which are practically impossible to replicate on track.
With this, I don´t want to say that measurements are always wrong and that we should not rely on the data provided. I personally strongly believe that data are the key element distinguishing engineers by other professionals and, as long as I have access to reliable sources, I heavily rely on them for my models.
Simply, I know that sometimes it is not easy at all to identify and quantify error sources. That’s the reason why, for this study, which aimed mainly to produce directional results about Perrin’s car performance and behavior and, maybe, more in general, about an LMP1-L like vehicle, I felt I could took the freedom to “shape” the input data a bit, basing on my previous experience, more than simply relying on what was provided.
After this (probably boring) excursus about data in general, you can find here below the aeromap that could be derived from Perrin’s data for the Low Drag configuration:

Aeromap Low Drag

 

As we have seen talking about the suspensions, the ride heights refers to the wood plank below the car, so they are trying to represent the lowest point of car’s body.
As you may see, the efficiency is mainly between 4 and 4.5, showing the highest values when the downforce is higher, mainly because drag seems to be less sensitive than downforce to ride heights.
One very interesting point is the general effect of pitch/ride heights on Downforce and balance shift. We can get a better feeling about it looking to the only situation where we have two different rear ride heights value (25 mm and 31 mm) for the same front one (15 mm). Although 6 mm could seem a very small delta for the rear ride height, it is interesting to observe how downforce balance (portion acting on the front wheels) moves from 43.4% to 44.9%: a shift of 1.5% is in general something producing effects relatively big effects, easily detectable by an engineer when looking to telemetry data and normally clearly felt by the driver.
If you think that this shift was produce by “only” 6 mm difference in rear ride height (or pitch, in this case), you can immediately get a feeling of how important ride height (and pitch) control could be for such a car. This phenomenon goes hand in hand with an increase in overall downforce (CzA moves from 3.36 to 3.43, more than 2% difference).
The aerodynamic efficiency is, in general, pretty high, to underline once again, if needed, how effective could an LMP prototype be from this point of view. Anyway, keep in mind that, as far as I know, the efficiency obtained with Perrin’s design is not even close to what the “big guys” achieve with their cars.
Regarding the Sprint configuration, I took into account only a single point of the map, using it to determine a sort of scale factor, as I explained already.
In particular, for the base Sprint Setup, the data provides the following values:

 

aeromap sprint

 

As we may see, some interesting things are happening here: beside the expected increase in downforce and drag, we have a slight increase of the aerodynamic efficiency and very pronounced shift in terms of front downforce distribution, which saw a relative increase of more than 4%.

Before to dive into the simulations results, I think it could be worth to spend some words about the initial setup proposed by Perrin and the modifications I did in order to achieve a somewhat satisfying handling. We will mainly focus here on the mechanical settings, as we have seen how the aero was basically fixed. Please keep in mind, I will always refer to wheel rates from now on, when talking about stiffness.

According to the suspension setup proposed by the designer, the car should have a corner wheel rate of 270 N/mm at the front and 230 N/mm at the rear. The antiroll bar rate shown was of 380 N/mm at the front, with a negative value provided for the rear. Assuming, as a starting point, a contribution of 150 N/mm of the rear antiroll bar and bringing the front one to 400, we get a Total Lateral Load Transfer Distribution of about 60.8% on the front axle and an overall roll gradient (including tires) of about 0.22 deg/g, at the ride heights considered (15 mm front, 25 mm rear).
The TLLTD is, even with this base setup, pretty aggressively moved toward the front; if we consider that Perrin note seemed to suggest (see the strange negative roll stiffness contribution of the rear antiroll bar) the need to have an even bigger portion of the overall weight transfer acting at the front axle (with this being also partially driven by the suspension geometry features we saw at the beginning), you can already foresee some of the handling features of this car and some of the problems it may have or the designer was trying to anticipate.
After some testing, I came to a setup solution where some of the vertical stiffness was removed from the corners and partially transferred to the third elements, both at the front and at the rear. For this first evaluation stage, I didn’t use any bump stop, although it would probably make very much sense for a car like this, with pretty high downforce and high ride height sensitivity. It is something that could probably be further and investigated in a second step.
The final corner wheel rate I have used is of about 190 N/mm at the front and 160 N/mm at the rear, with both front and rear third elements using a 50 N/mm spring. Roll stiffness has been further decreased at the rear, coming to 100 N/mm and slightly increased at the front (420 N/mm) thus coming to a final Total Lateral Load Transfer Distribution (in static/setup conditions) of about 65% and to a roll gradient equal to about 0.24 deg/g.
As you may also see, the final corners vertical stiffness has been reduced, compared to the levels suggested by Perrin itself, making the car definitely much easier to drive also on kerbs and bumpy surfaces and, in general, more predictable.
We can generally say that the vehicle, because of its design and of the setup in use, showed a pretty marked understeering tendency in corner entry, probably making even more evident the compromises accepted on the suspension design side, above all in slow speed corners. The front end is not so easy to handle, producing sometimes pretty strange reactions and generally a worse feeling than the one you could get driving an LMP2, for example (where suspension design is much less compromised). On the other hand, this has probably help to give some confidence to the driver in the corner exit phase and in fast corners.

As we already mentioned, gear ratios were also slightly changed during the “development” phase, to make the engine output more usable in corner exit in slow corners. After testing a bit, I finally came to a solution with slightly longer 2nd, 3rd, 4th and 5th gears, ending up with the following solution:

 

1st: 14/34
2nd: 16/32
3rd: 18/31
4th: 17/26
5th: 21/29
6th: 19/23
7th: 23/25

Bevel: 22/21
Crown and Pinion: 15/43

 

The vehicle model, built and set up as described, was tested in Silverstone using a very detailed track model that should eliminate the fidelity/confidence issue on this side pretty completely. The results are shown here below with graphs relating to speed, RPM, lateral acceleration and longitudinal acceleration versus driven distance.
Now, before to comment on them, I want to share a word of advice, also to avoid anybody shooting at me because of sharing wrong results.
These results refer to a simulation which, as any simulation, was performed in ideal conditions (although using a real human driver), thus excluding a long list of disturbing factors that do exist in real life (see track conditions, traffic, etc) and other important factors as tire wear, all of which have a significant influence on lap times and, more in general, on the performance of the vehicle.
Also, the data I have used to generate this model are actually derived from a car (Perrinn MyP1 project) which doesn’t exist in real life and, unfortunately, probably never will; in some cases, as we have seen, I had to derive some missing inputs, sometimes related to performance critical subsystems, see for example the engine. For other critical areas, see for example aerodynamics, the designer has only performed simulations but never tested (in a lab or on the road) any part.
This means, on one side, that the input we have given could well be wrong or, at least, different from any other existing car; this depends, as we said, on many potential data errors and, on the other side, on having no means to check how good or close all of this data is compared to, say, a real existing LMP1-L vehicle, see for example the one used by Rebellion Oreca.
Nonetheless, I think it has been an interesting exercise, also to understand how close (or how much closer) an LMP1-L car could go to the manufacturers in terms of performance and, also, which issues (setup, handling, balance, etc) such a vehicle could present.

The final lap times obtained was about 1’42’’5. Let´s look at the speed trace first.

 

speed

 

First thing to notice is that the car reaches a top speed close to 300 km/h (about 297) on the Hanger Straight and is able to drive at very high speed through the fast sections of the track (Abbey, or first right corner, where we are travel at close to 265 km/h and nearly flat out; Stowe, with a minimum speed of about 215 km/h and finally through the Maggots-Beckett section, where the relative stability helps to carry high speeds through all of the corners).
We can take a look to the lateral acceleration trace, to further understand car’s overall grip potential:

 

lat g.jpg

 

Here we can see that the vehicle is able to carry about 3gs in the two quickest corner of the track, to underline how important the aero effects are. Interesting also to notice how also in lower speed corners the car can sustain accelerations over 2gs, see for example through the Maggots-Beckett complex.
The longitudinal acceleration trace seems to confirm more or less the same trends, also giving an idea about the trusting force that our engine could produce, additionally also showing the grip potential in braking.

 

long g

 

Finally, we can take a look to the RPMs trace, at least to have an idea about the engine usage and gear ratios and to try to stimulate somebody to come back and say if and how wrong my assumptions are. As I said several times, the engine is probably the biggest question mark of the whole article and, together with aerodynamics and tires, surely a very strong performance driver.

 

rpm

 

Taking into account all the error sources we already mentioned, I even dared to compare our data and results with what happened last year in Silverstone during the first WEC Race. Pole position was signed by Porsche with a stunning 1’39’’7 average (of two drivers), but with an overall best lap of 1’39’’5; the second best non-Porsche car was the Audi, with an overall best lap of 1’40’’2 circa.  It makes no sense to analyze the LMP1-L lap times, since Rebellion was absent for the first race and the ByKolles team was surely not yet in top form; also, the fuel regulations has been changed slightly at the end of the season, as we said, probably freeing some performance potential for the non-hybrid cars. Anyway, we could try to deduce a reasonable gap, looking to the typical lap times difference between LMP1-L and LMP1-H at the end of the year: LMP1-L were typically still about 6-7 seconds slower, if we look at qualifying results of USA, Japan, China and Bahrein races; this means the typical gap between LMP1-L and the manufacturers is normally bigger than what we have obtained with our study.
In general, we should surely allow an error window to my results, to take into account effects not easy to simulate (as the ones we already mentioned, see traffic, track conditions and conditions variation, etc). In any case, also assuming a +/- 1 second tolerance for the simulation results, we still have a pretty sensible difference to what shown in 2015 by the two LMP1-L teams, in terms of performance.
This opens some possible scenarios that we can analyze: the first one is that, simply, my model is not really representative of any existing LMP1-L car: the data I used are not coming from any of these cars and there are anyway some open questions about some critical areas, like the powertrain one.
Another explanation could be that only some of the subsystem are not really performing as we would think, basing on my simulation results: my first suspect lies, again, on the engine: to be honest, I still believe that, although being realistic when looking to the BSFC values we used, the power output I assumed was probably optimistic. This could also open another discussion about the pace difference between LMP2 and LMP1-L to be expected in 2017, when LMP2 should actually get an engine with a power output of about 600hp (much more than the 500hp c.a they have now).
But we also don’t have to forget that last year LMP1-L and LMP1-H teams were using the same tire brands (Michelin, which I doubt was providing the same tires to everyone), while all of my simulations refers to a tire model based on lmp2 tire data. Incidentally, the same brand will be used in 2016 by all of the LMP1-L cars, which moved away from Michelin. We could argue that any tire model used for simulation will never really give a precise picture about the final performances of its real counterpart, but I am pretty curious to see which pace the LMP1-L cars will have in 2016 in Silverstone. Assuming my simulations are anywhere close to being correct, LMP1-L teams should be able to free some performance potential, sooner or later.
For now, what we can say is that, looking to the only 2016 lap timing results available so far (namely the results of the Prologue, held in Paul Ricard at the end of March), we see already gap reduction, with the LMP1-L cars now being closer to the LMP1-H ones, with Porsche and Rebellion being some 4 seconds away from each other and this difference further reducing to about 3 seconds if we consider Toyota and Audi.

To better understand if and how our results are somehow trustable, we can look in more details at the performances registered by the LMP1 cars in Silverstone in 2015 comparing them with our simulations and to the results seen by LMP1-L teams at the end of the year.
The first “quick and dirty” comparison we can do regards the maximum top speed reached in our simulation (about 297 km/h), that is pretty close to the one achieved by Porsche (300-302 km/h) but pretty much quicker than the ones of Audi and Toyota (285 km/h c.a for the Japanese and something below 280 for the Germans). This should be no surprise if we assume that, even being probably more aerodynamically efficient, the LMP1-H has less power coming from the IC engine; this should mean that, after the boost of the hybrid out of the corners is over, the car is only pushed by a less powerful engine than the one we used. A comparison between our model and real LMP1-H performances could anyway be useful to understand which delta we could expect, in order to compare it with the top speed delta between LMP1-H and LMP1-L cars in other tracks.
So, without further ado, if we look at Fuji results, we can see that, while Porsche was topping at 309 km/h c.a, the best Rebellion was able to achieve a maximum speed of about 302 km/h. We would have here a 7 km/h difference, which compares relatively good to the 5 km/h difference we have seen in Silverstone with during our simulation.
If we then look to the USA results, we even see a Rebellion on top of the best speed charts, with a maximum speed of about 301 km/h, against the 300 km/h achieved by Audi.
This shows that, at least in terms of top speed, we should be not too far off from a realistic picture.

What about other sections of the track? The only source I found to gather some information are the onboard videos you can find in Youtube about and showing portion of the WEC races: sometimes, if you are lucky enough, some of them show some telemetry data which can help to identify (at least very generally) cars performance.
Before diving into details into this, again, please keep in mind that some aspects, like for example tire wear, were not simulated during this exercise and that they could play a role in defining small differences. Moreover, some other parameters like fuel load are unknown. Finally, our model represents a car that should be slightly lighter than a LMP1-H.
Watching this video, at the 1:21:00 mark c.a, we can seat for some time onboard of the #7 Audi beside the driver Marcel Fassler. At the very beginning of this video section, we see the minimum speed inside the last chicane (Vale), being about 90 km/h (in another section of the video, around the 1:55:00 mark, the minimum speed is around 94 km/h): this compares pretty good to the 92/93 km/h we see in our simulation; a few seconds after, the Audi goes through the first very fast right corner (Abbey), with a minimum speed of about 260 km/h, which is again very close to the 262-263 km/h shown in our speed trace at the same point. Again, Fassler drives through the first right hairpin (Arena track section) with a minimum speed of about 90 km/h, which is not too far off from the 95 km/h shown by our simulation. The following left hairpin allows a minimum speed of about 83 km/h to our model, against the 80 km/h c.a of the Audi.
Afterward, the displayed data seem to stop working, not allowing for a check on the following corners. This very rough sanity check tells, anyway, that our results should not be completely off compared to the real cars.

Closing, this exercise allowed us, on one side, to explore more into details the very interesting MyP1 project, from Nicolas Perrin, analyzing a bit how the car looks like and trying to identify some key aspects, at least in some areas. Afterward, basing also on my previous experience with other similar cars, we tried to build up a simulation model of the car, assuming it was running in an LMP1-L configuration. The results we obtained has shown that, assuming the data we have used are realistic, the LMP1-L car could actually have some potential, in terms of performance, that for some reason was not yet explored. The results we obtained were also (roughly) compared to available data about LMP1 vehicle performances to double check if anything was completely off.

Hope you enjoyed it and you did it to the end without falling asleep!

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