And the misery of turning up for your costly week at Nardò only to find that it’s raining.
But there’s a bump in the road: the models that inform the suspension movements on the sim rig are now so complex that they can’t keep up with real time. Which is where Kenny Motte comes in.
“A model contains hundreds of Xs, and they all need to be retrieved within the millisecond of time you have before the next simulation step,” and this is a problem, says Motte, who works on Tenneco’s Monroe sub-brand.
(If you’ve been in a McLaren 750S, AMG C63 or Volkswagen ID 4 GTX, you’ve experienced its handiwork.) There are too many variables and not enough time.
For every input that an imaginary damper receives on an imaginary road, the response is determined not just by your programmed valving, the nature of the impact and road speed but also oil character, how the gas is dissolved in the oil, whether the piston is close to full compression or fully extended, flow speed through certain valves, pressure at precise locations inside the compression chamber, temperatures and even the elastic deformation of the damper tube.
Truly, the dependencies are enough to make your brain shrivel. It’s likewise an issue for the modelling computers, whose instructions to the rig begin to lag behind the forward travel of the car that’s being simulated.
Result: unusable data and misleading feel for the driver in the loop.
Motte has thus been busy training an artificial neural network (ANN) that takes inputs into its AI black box and spits out a reaction output, ‘calculating’ a bunch of matrix multiplications to do so.
The process is rather more instantaneous than ‘solving’ for countless interlinked Xs, as the classic models need to do, although one drawback is that you can’t ‘look under the bonnet’ of an ANN to see what’s happening and why.
If speed is the key benefit, the efficacy of the ANN is down to the rigour and quality of the training, which is undertaken with massive sets of known data on how dampers respond.
