USNCCM17 Semi Plenary Skrypnyk

July 26, 2023
Hybrid Physics/Data Driven Modeling for Virtual Tire Development
Ihor Skrypnyk, The Goodyear Tire & Rubber Company
The automotive industry often goes through significant changes. Currently, one of the many aspects of these changes is the acceleration of new product development. This process is evolving from one heavily reliant on building and testing of physical prototypes to one that heavily relies on modeling and simulation. New advances in Machine Learning play a significant role.
Over the last three or four decades, Finite Element Analysis (FEA) and other types of structural models have been used more broadly to provide design recommendations and to reduce the number of product prototypes built. The same trend also has been present in tire manufacturing. The capabilities to predict tire performance, including tire durability, rolling resistance, treadwear, force and moment, noise, have been developed and published over the years.
These modeling capabilities target objective measures of tire performance that can easily be measured in a laboratory. Most of these methodologies have reached their maturity, and further gains in predictive accuracy are getting harder to achieve.
However, further improvements in product performance predictions can be achieved by combining physical modeling approaches with Machine Learning (ML) methodologies. With the recent progress in data technologies, such as new HPC capabilities, cheap data storage with fast access and novel ML algorithms, the idea of combining physics-based simulations and ML approaches moves beyond the concept and into engineering design practice.
This presentation will provide an outlook of a tire development process that utilizes both “classic” FEA approaches as well as novel methods where FEA is combined with new data-based modelling methods.