How to make a digital twin of a tyre

Jon Lawson

cosin scientific software can now run its physical tyre model FTire with rFpro. Using an ultra-high resolution road model with a physically accurate tyre model in real-time significantly improves the accuracy of data and therefore correlation with real world results. The physics-based tyre model spans primary and secondary ride allowing it to be used in a variety of circumstances, including extended handling manoeuvre and active safety application scenarios. Traditionally, OEMs, Tier 1 suppliers, and AI-focused businesses have used lightweight Pacejka or extended Magic Formula style tyre models, which are not able to cover the short wave-lengths effects coming from road surface irregularities.

Prof. Dr Michael Gipser, co-founder of cosin scientific software explained, “Coupling FTire simulation with the virtual worlds that rFpro produces, means that customers can in greater detail replicate real-world driving conditions. Previously, the tyre simulations capable of running sufficiently fast to work in an rFpro environment have been less accurate models. FTire is incredibly efficient in its ability to deliver physics-based simulation, even whilst running fast enough for real-time applications within rFpro’s digital twins." 

Enabling FTire to be used with rFpro now allows the same model to be used throughout the entire tool chain and the whole development process across all departments, from the office, HiL (Hardware-in-the-Loop) testing, through to driving simulator systems.

With FTire, rFpro experiments running on HPC data centres, HiL test rigs and driving simulators can benefit from tyre contact patches that react realistically to the detailed road surface in rFpro's environments. Crucially, this means that experiments will correlate more closely with real-world tests at proving grounds, particularly in the way the car's motion affects the orientation of ADAS sensors mounted around the vehicle.

Matt Daley, Managing Director, rFpro, added, “We also have customers generating terabytes of simulated training data. There are many use cases where it is important to simulate the vibrations of the sensor platform mounted on a vehicle that is driving on rough road surfaces. This new capability will extend the validity of simulated training data, saving even more time and money for our customers, compared to traditional video and photography data sets.” 


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