The 3D model of the Formula Ford is from the excellent simulation netKar PRO. Buy a copy for a just €19.
As a computer scientist, I find it hard to comprehend that you cannot always quantify engineering. Computers represent, in many respects, a form of perfect engineering, free from the burdens of materials, tolerances and the infinite physics of an environment. In theory, a given operation is entirely predictable whether it is invoked once, twice or a thousand times.
The same cannot be said for most forms of motorsport engineering. Despite absolutely every possible effort being taken to minimise variance, there is no guarantee that two engines, two chassis or two sets of tyres, for example, will ever perform equally – despite being manufactured to an identical specification. And so, I was wondering, if you cannot quantify the manufacturing itself, is it possible to at least quantify the performance gain between different engines?
Formula Ford and the legend of Patch
Ever since the hey day of Formula Ford, there have been stories of engines that have dominated the formula. And the legend that is Patch demonstrates this well.
Patch was a Kent engine with remarkable pedigree. It is most well-known for powering four different drivers to successive Formula Ford Festival wins - Roberto Moreno (1980), Tommy Byrne (1981), Julian Bailey (1982) and Andrew Gilbert-Scott (1983). Additionally in 1981, it also believed to have powered a young 21-year old Ayrton Senna to the RAC and Townsend-Thoreson championships, in the same works-run Van Diemen that Tommy Byrne piloted to victory in the Festival.
However, the origins of Patch are rooted earlier with a young South African driver called Trevor Van Rooyen. In 1977, Van Rooyen piloted a semi-works run Royale RP24 with a self-built (but Minister badged) engine. That year, Van Rooyen won the RAC championship and in the process won a staggering 33 races. However, the following year (1978), Van Rooyen’s engine was destroyed in a test session and the South African returned to his native land. For the record, Van Rooyen went on to enjoy a distinguished career which included winning the 1985 South African Formula Two championship.
Leading Engine builder Graham Fuller (Minister International) subsequently repaired the broken block, welding a ‘patch’ of aluminium where the block was damaged. The engine then returned to racing during the 1980s and to claim its place in history.
Whilst many would argue that much of the latter successes of ‘Patch’ was due to powering a works Van Diemen chassis, the dominance of which would invariably attract the fastest drivers anyway, the legend of ‘Patch’ continues to be known as the greatest advantage a Formula Ford driver could possibly have.
Formula Ford engine power and torque curves
The following is the dynamometer chart from my Formula Ford (1600 Kent) engine when it is was rebuilt in January 2009.
The vital statistics are that at the time of the rebuilt, this engine produced 105.8 BHP at 5800 RPM and 148.8 NM/torque at 4400 RPM.
The problem is that with only data from a single engine, who is to say whether or not this is a strong engine? What’s more, any comparison would only be valid if it were also from the same dynamometer. However, after a year of racing in Formula Ford I would suggest that immediately after being rebuilt, this engine was average. It was not poor but it was also not great and as the season progressed and the engine ran hotter, it became an obvious disadvantage.
But my question is, what did this disadvantage really do to my laptime?
rFactor for real world simulation
Image Space Incorporated have been producing world class simulations for over a decade and the latest incarnation of which is rFactor. Consumer distribution is almost exclusively online and the success of which is demonstrated by the large, dedicated and vibrant community of contributors it has amassed. GMotor2, the 3D and physics engine behind rFactor is utilised and licensed in many other popular sim racing titles including GTR2, ARCA Sim Racing and RACE – The Official WTCC Game. What’s more, Formula One teams including Williams F1, Red Bull and Ferrari use a (albeit customised) version of rFactor (and gMotor2) for their in-house simulators.
I could have approached this problem using a headless simulation such as Bosch’s LapSim. However, as a driver, the benefits of using an interactive simulation are too attractive. None the less, in the future, I might still do this and it would be interesting to further validate the results.
A more accurate Formula Ford for rFactor
In an attempt to answer this question, I have modelled the physics of a Formula Ford 1600 that is as close to my Swift SC94 Formula Ford as possible. In the process, I have accurately modelled the suspension geometry, imported the power and torque data from my engine and re-profiled the tyre slip curve to one that resembles the Avon ACB10.
To demonstrate its potential, the following is a comparison of two similar laps (within one tenth of a second) of the Silverstone National circuit. The blue trace is the actual speed (mph) sampled from my Swift SC94 on 27 June 2009 whilst the red line is the same speed sampled from a simulated lap of the same circuit.
It would be unrealistic to assume that the two traces would perfectly overlap. This particular lap of data was sampled during race conditions and at the time I was dicing closely with two other competitors. There is also a degree of precision lost in comparing data sampled from two different loggers (one of which being virtual) as there is a small variation in actual sample frequencies. Finally and inevitably, there are small inaccuracies in both the Formula Ford physics and indeed the ISI model of the Silverstone circuit (Formula One teams will have access to much more accurate surface data, for example).
However, for the purposes of answering my question, it is sufficient and accurate somewhere in the region of about 2-5% at terminal speed (comparing speed and RPM).
The following video is two sample laps of Silverstone National circuit using the simulator (one of which produced the simulated data shown above).
One final note before I move on. Whilst I would love to contribute this work to the rFactor community and make it available for download, I can only take credit for remodelling the physics of the car. The artwork and 3D model itself originates from the impressive netkar PRO and doing so would be a violation of their copyright and intellectual property. The original conversion of the car encountered similar problems and for those reasons I will continue to respect the rights of these parties but thank them for their hard work.
Building three more powerful engines – artificially, that is
I have remodelled the engine power and torque curves three times – with two, five and ten more horse power. This is obviously theoretical; a real engine producing more peak power would do so with a entirely different power curve. If anybody has dynamometer data from their own Formula Ford engine please drop me an e-mail.
Running the experiment
I have run four separate 20-minute simulations of Silverstone. In order to minimise driver inconsistency I will be using the worlds best test driver – the computer. This enables me to run four near identical sessions. Internally the rFactor AI uses predefined way-points on the circuit to drive each lap.
A summary of each session is shown in the table below.
| Engine |
Fastest |
Avg. |
Fastest Diff. |
Avg. Diff. |
| Standard |
63.731 |
63.895 |
|
|
| +2 bhp |
63.548 |
63.693 |
-0.183 |
-0.202 |
| +5 bhp |
63.298 |
63.432 |
-0.433 |
-0.463 |
| +10 bhp |
62.626 |
62.782 |
-1.105 |
-1.113 |
The following chart shows the individual lap times for each session run.
Finally, the following two charts show the speed and RPM traces respectively, for the fastest lap in each session.
Making sense of it all
I don’t think there is any particular surprises in the results. The fastest overall lap time was set using the most powerful engine. Ten extra horse power yielded a lap time of 62.626 seconds or 1.105 seconds faster than the best lap time set with the standard engine. Similarly, the average lap time was slightly faster still at 1.113 seconds.
An extra five horse power produced a fastest lap time of 63.298 seconds, 0.433 seconds faster than the standard. On average, it was 0.463 seconds faster.
With just two more horse power, the best lap time was 0.183 seconds faster than standard and on average 0.202 seconds faster.
Broadly speaking in this experiment, one horse power equates to roughly a 0.1 second per lap performance. Obviously, this will vary from circuit to circuit.
Back to reality
During the National Formula Ford race weekend in June I qualified in 7th position with a best time of 63.986. This was 1.216 seconds off the pole position time of 62.770, set by Rory Butcher. For the record, I did not test before this weekend and this qualifying session was the first time I had driven a Formula Ford 1600 around the Silverstone National circuit. I was also driving with a left-rear slow puncture caused by a broken tyre valve core.
If we assume that there was a couple of tenths to be found through familiarisation with the circuit, that brings us close to the fastest simulated time of 63.731. Likewise, Butcher’s pole position time of 62.770 is equally close to the best simulated lap time of 62.626, set with ten extra horse power. However, I would suggest this is probably more coincidence than it is scientific fact. An interesting coincidence, none the less.
Concluding with the caveats
Ultimately, there are many caveats to the conclusions drawn from this experiment. I will reiterate that the remodelled power curves are artificial and in many ways, unrealistic.
There is no allowance for engine wear – at the Silverstone race weekend my engine had run for around 400 miles and probably no longer produced 105.8 BHP at 5800 RPM. However, this works both ways and it could actually mean a horse power differential closer to ten, after all.
The same gear ratios were used for each run (2.40, 1.74, 1.43 and 1.17). These are also the same ratios used during the race weekend. With ten extra horse power, I would expect to run longer gear ratios and suggest this would see a further reduction in lap time. The flat line at the end of the straights on the RPM trace confirms that fourth gear is indeed too short.
Ideally, this experiment would need to be repeated a high number of times before the data is collated and analysed. Unfortunately, one reality of using a real-time simulator such as rFactor is that each 20-minute simulation takes 20-minutes to complete. A headless simulator, such as LapSim, would be able to repeat simulations at a much faster rate and as such produce more reliable results.
As far as I am concerned, this experiment has demonstrated that despite its caveats, a small horse power advantage offers a quantifiable and not insignificant reduction in lap time on the Silverstone National circuit. But even more, it has demonstrated what a brilliant and accurate simulation rFactor really is.
You can now read this article in print form online with Racecar Engineering for Zinio.
