Race car simulation, myths and reality.

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Ursula Stephens
6 years ago
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1 Race car simulation, myths and reality. The purpose of this article is to expose the myths surrounding race car simulation, to demonstrate how to use race car simulators and to show the real impact it can have on race outcomes. Race car simulation is one of the most powerful yet misunderstood areas in all of motorsports. In a world where it is now inconceivable to send a car on to the circuit without some form of data logging, simulation is barely on the radar screen of most motorsport users. The reasons that race car simulation have yet to be widely introduced is a reflection of the culture within motorsports both cultural and a lack of knowledge about what simulation is and how it works. In times past, racers from all backgrounds used to test until the wheels of their cars were ready to fall off. From club racers through to Formula 1 an endless number of options would be tested. Further, unlike in an aeroplane, if there is a problem with the race car, in most cases it is always possible to hit the brakes or ease off the throttle and live to fight another day. In this sort of environment the focus was on the empirical (the how/results) as opposed to the scientific (the why). This bias towards the how was based on the huge amount of data that was collected. When you have a large data base to draw from, what is required is the ability to sort out the information as opposed to sitting down and working out the why. Most of so called traditional race engineering, isn t actually engineering in the strictest sense. It s very broad data reduction based with minor scientific analysis. Maybe with the exception of the very highest levels of this sport, if you walk up and down and paddock and quiz the typical race car engineer of what their car is doing, you would hear a lot about what it actually did, but you wouldn t necessarily have the why that went with it. This is in stark contrast to aerospace and the vehicle dynamics testing for road cars. The traditional paradigm of testing until the wheels fall off is now outdated. Circuit availability is now very limited, and most racing series have very strict limits on testing. The consequence is that there is now a very big gap between the current skills set that most racers have (the ability to test and analyse data) and what is needed (the ability to quantify what to test so to make the best use of track time) The primary benefit of race car simulation is that it forces you to understand the race car and it will help the racer bridge this gap. In the past, simulation has provoked either bemused curiosity or disdain. With the lack of testing, however, it is now a necessity. The power of race car simulation is that it forces you to think about each component of the car at its most fundamental level. While this is a very alien process for most traditional race car engineers it goes a long way to explaining why a particular setup worked in one place, and it didn t work else where.

2 An example of the advantages of a thorough engineering approach can be drawn from a single spec formula I engineered in recently. I started with the organisation mid season, with only a limited knowledge of the car. The car was engineered in the traditional manner and as was to be expected, while the results were reasonable they were not spectacular. One of the big unknowns was the effect the roll bars had on the car. Things came to a head one race meeting and after looking at some poor test rig results, I sat down, looked at the bar and did a proper engineering analysis. At the next race meeting, testing was extremely limited and things didn t go according to plan. However I now had the tools to conduct a proper analysis of the car and formulate a plan to address the situation. This knowledge was critical in turning the weekend around because the effect of each bar change could be quantified. The other reason that race car simulators haven t gone into wide usage is the wide held belief that because some aspects of the car can t be accurately quantified they should not be used. While on the surface this may seem quite valid, a history of engineering analysis shows us that when used appropriately there are some approximations that can be quite useful, In aerodynamics, thin aerofoil theory and panel methods actually ignore air viscosity. While on the surface this seems a serious oversight, aerospace engineers have been using these tools to design Aerofoils and wings for decades. Think about that the next time you get on a commercial flight! Most structural analysis, whether it s a Free body diagram of a truss structure, or a complex Finite element model have some quite serious oversights of joints, or friction between components. If we were to apply the motorsport logic of above, we should immediately fire half the structural engineers in the aerospace and automotive industries. The point here is simply that no physical model is perfect. What approximations provide us with, however, are the tools to quickly focus on what is important. This is why we can get on a commercial flight and sleep soundly in the knowledge that the biggest risk is the dodgy sandwich you paid $10 for. Another point to consider is that our friends in the road car industry are now starting to use vehicle simulation as an integral part of the design process. All the major motor manufactures use vehicle simulation to design and validate everything from traction control, anti lock braking to suspension design. Given that most of these organisations spend billions of dollars on these programs and produce cars that are safe to drive, surely the attitude of some in the motorsport industry most come across as a tad archaic. The other reason that race car simulation hasn t taken on too widely is the fact that race car simulators are sophisticated tools as opposed to quick fixes. It is a sad reflection on human nature, that when faced with a challenge many will look for quick fixes as opposed to working through the issues. You see this reflected in our industry far too often. There s an old saying in motorsport that if the car that wins has a purple stripe, at the next race, 10 cars will have the

purple stripe. The author has seen first hand race teams who spend 20,000 on data acquisition and didn t have a clue how to use it.

Every simulation run with even elementary correlation will tell you the following, The tyre loads you can expect for each bit of the circuit. This is vital for determining things like roll understeer.

Even when the simulation is different to actual data it will still yield useful data. It will either tell you an interesting load characteristic of the tyres.

3 purple stripe. The author has seen first hand race teams who spend 20,000 on data acquisition and didn t have a clue how to use it. It didn t stop them bragging to their sponsors or potential drivers though! The real power of race car simulation is that it is the ultimate motorsport calculator you will ever have. Every simulation run with even elementary correlation will tell you the following, The tyre loads you can expect for each bit of the circuit. This is vital for determining things like roll understeer. How the suspension geometry will interact. If the simulation is dynamic, every simulation you do is the equivalent of running the car on a 7 post test rig. Even when the simulation is different to actual data it will still yield useful data. It will either tell you an interesting load characteristic of the tyres. Alternatively it will tell what the circuit is doing. This knowledge is vital going into a race meeting because it provides you with an accurate picture of what the car will do before it ever turns a wheel. To apply this, consider a number of case studies. The first case study to consider is the analysis of the dampers of a Piibeam MP93 LMP2 Sportscar that was raced at LeMans in Fig-1: MP93 LMP2 Sportscar The damping curves for the car are presented in Fig-2. The dampers we set to full hard in both bump and rebound. Over the kerbs at LeMans the drivers were complaining of abnormal understeering and very bumpy behaviour over the kerbs.

Fig-2: Damping curves for the LMP2 sportscar. This problem was investigated using the dynamic simulation program ChassisSim TM. A simulation was run of the car with the existing setup.

4 Fig-2: Damping curves for the LMP2 sportscar. This problem was investigated using the dynamic simulation program ChassisSim TM. A simulation was run of the car with the existing setup. Even though the model was quite rudimentary, the analysis of the simulation lap, yielded some interesting results, The damper curves where almost flat. However the simulated ride heights indicated excessive oscillatory behaviour. This was confirmed by reviewing the simulated tyre loads. What this indicated was that the car was over damped, meaning that the dampers were set so stiff that the bulk of the suspension was happening in the tyres as opposed to the suspension. So using ChassisSim TM, softer damping curves where applied. The simulated results of this are shown in Fig-3. The black is the original settings and the red are the softer settings. From Fig-3 it can be seen the movement of the car has been translated back to the car, and the simulated data showed much more consistent tyre loads.

Fig-3: Comparison of soft damper settings to full damper settings. When this was applied to the car the following was noted, The understeering at the chicanes was solved.

However the real power of the simulator is that all this could be done and analysed with minimal running of the car.

5 Fig-3: Comparison of soft damper settings to full damper settings. When this was applied to the car the following was noted, The understeering at the chicanes was solved. The car was much easier to drive. All of this was reflected in the pre-simulated data. However the real power of the simulator is that all this could be done and analysed with minimal running of the car. The second case study is the design of the front suspension of a car being designed for the time attack category. This is where simulation really comes to the fore because it provides the designer with the perfect 7 post test rig environment to evaluate how camber and roll centre vary as the car squats for braking and as it rolls during cornering. The base car was simulated on a particular bumpy track using ChassisSim TM. The simulated results for the standard car for the front and rear roll centre and front camber are shown in Fig-4, Fig-4: Standard car front roll centre and camber variation.

The standard car had a McPherson front suspension and multi link rear suspension. From the top graph, the red trace is the front roll centre, and the blue trace is the rear roll centre.

6 The standard car had a McPherson front suspension and multi link rear suspension. From the top graph, the red trace is the front roll centre, and the blue trace is the rear roll centre. It can be seen that the rear roll centre variation is reasonable but the front roll centre varies quite considerably. The second graph showed the front camber gain, and the camber variation was up to 4 deg in the middle of the corner. To address this situation it was decided to design a double wishbone suspension for the front. Effectively ChassisSim TM was to be used as the suspension equivalent of a 7 post rig. Lap times where not even being considered. The focus of the design was to minimise camber change and minimise roll centre variation. The results of the suspension design are shown in Figure 5 Fig-5: Initial front suspension vs Final suspension. The black is the baseline car and the red is the final design. As can be seen, the front roll centre variation is now considerably less than the base car, and the camber variation has been reduced (up to 1.5 degrees in some instances). The simulated gain was 0.5 sec a lap. However the key thing is that simulation has been used to confirm the fundamentals of the car are correct. These case studies indicate the appropriate use of race car simulation is to review simulated data as rigorously as track data. A common mistake I see in race car simulation is when it is used without reviewing the data. When preparing for a race weekend I always use the following rules, Track each simulation run as if you where testing. This can be done either using an Excel spreadsheet, or using conventional run sheets. Start by simulating the base setup. Note what the tyre loads are doing, review the damper histograms, and any other interesting information. When simulating always log and review the logged data. There are no exceptions to this. Direct the attention to what is being simulated. If the damper histograms are being tuned, then focus on that. If the lap times drift backwards, don t worry, this is probably an indication that something else in the Chassis needs to be fixed. With every simulation run always ask the question why? In summing up race car simulation is a vital tool in the armoury of every racer. The key advantage of using race car simulators is that it forces the racer to understand their car in a very fundamental way. It also forces them to

7 understand the why of their race car. This alone is priceless information that will help the race engineer to think on their feet between sessions when the pressure is on. It is also important to realise that a race car simulator is a powerful tool as opposed to a quick fix. The case studies have indicated that when used this way a simulator can highlight the fundamental interactions of the race car. When using a race simulator the simulated data must be reviewed as rigorously as actually logged data. Sun Tzu in his epic work the art of war said, It is said that one who knows his enemy and knows himself will not be endangered in a hundred engagements.,. One who knows neither his enemy nor himself will invariably be defeated in every engagement This is the advantage of race car simulation offers you. It enables the user to understand the car so that when you arrive at the circuit you are prepared for every scenario.

Data acquisition and analysis tools

Data acquisition and analysis tools Workshop Goals Introduce Data acquisition tools and Laptime simulation tools Show what to look for in logged data and what to focus on. Discuss the appropriate use of racecar simulation tools. Present