Why the tread pattern is one of the most difficult tire elements to model virtually

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In previous columns I’ve discussed some of the challenges that come with modeling the behavior of tires. Expanding on that, one of the single most challenging areas to model is the tread pattern. It’s also one of the most critical areas, as it affects the tire’s handling ability – the single most important performance aspect to get right.

Ultimately, cars are machines for moving stuff about, which requires the generation and control of lateral force – exactly what handling is. Other performance factors are important, but they come afterward. Good ride characteristics, for example, are important in making the driver more comfortable, but that’s less important than actually getting them where they’re going. Other critical factors, including braking, accelerating and steering, all come under handling, since as far as a tire is concerned they are the same thing, just acting in different directions. A vehicle needs good handling characteristics not just in the dry, but also in the wet. This is challenging because predicting how a tire will behave in the wet is really hard.

One reason for this is that while ‘dry’ is clearly defined as a yes/no variable, ‘wet’ is harder to define, and the performance of any given tire in a wide range of conditions is equally variable. The tire could have adhesion, or it could not; it could be aquaplaning, or it could not. It’s all subject to the classic engineering get-out clause of ‘It depends’. It depends on the amount of rain, type of road surface, forward speed, and many other variables.

Ideally, tires would be tested in every one of these conditions and, indeed, tire and vehicle companies go to great expense to do exactly that. However, such ‘great expense’ is problematic when trying to run a business. So the complete tire assessment is usually run only at the end of a development program, to fully check and sign-off the final design. This means that, at final sign-off, the manufacturer must already be very confident that the tire will pass all the safety checks before spending the money to confirm it.

During the development program it would be useful to be able to accurately predict the wet weather performance of a tire without going through the great expense of having to build and test it. That’s where virtual design and simulation tools come in. It’s also where virtual design and simulation engineers age five years in five minutes just from thinking about it.

The reason for this reaction is that for dry handling there is usually no need to explicitly simulate the tread pattern – we don’t typically need to know how much grip each individual tread block is generating. It can be assumed that all the tread blocks are the same and simply model the net result of the whole tread pattern, dramatically reducing the complexity of the simulation.

But this assumption is not valid for wet handling. Aquaplaning is an example. At low speed, all the tread blocks are in contact with the road, but as speed increases, water starts to lift some of the blocks from the road. As speed increases further, all the blocks lift and the tire loses traction almost entirely. This makes it necessary to simulate the behavior of each individual tread block, which adds complexity. In addition to running an already highly complex rotating finite element model, it’s now necessary to run a full fluid dynamics model at the same time! That is really, really hard, so ultimately wet handling simulation remains heavily constrained by available computational resources. Even with advances in computing power we can expect this constraint to continue in the future.

Gregory Smith has worked extensively across the automotive and tire industries, while based in Europe and North America. More information can be found here.


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Gregory’s career in tires began at Jaguar Land Rover, where he was the first at the company to build tire models. This led to him founding the Tire CAE and Modeling team which was responsible for tire testing, modeling and technical development. In 2016 Gregory obtained four patents and won the Tire Technology Young Scientist prize for his PhD work on GS2MF, an efficient tire testing procedure. Following this Gregory moved to America to work with Goodyear on virtual tire submissions, where he worked to develop a process to design tires using virtual modeling techniques.

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