Characterizing racing tires on a realistic indoor surface

Alexander O'Neill (GCAPS)

Tires are traditionally characterized using indoor flat-track machines with sandpaper surfaces, which generally overpredict performance relative to outdoor driving, especially for racing tires. This limits correlation and accuracy when applying such data or models in vehicle simulations. To address this, a novel flat-track surface was developed with a surface roughness much more representative of real roads. Racing tires were evaluated on the surface and benchmarked against outdoor measurements, supported by analysis of rubber material properties. The surface produced an improved correlation with outdoor behavior, thus offering a new way to enhance understanding and predict tire and vehicle performance.

Improving Tire Models via Optimized Tire Testing
-Enhancing Tire Model Accuracy Through Optimizing Methodology on Indoor Test Machines-

Anders Edward Maki (MTS Systems Corporation)

The test methodology and machine can affect the data obtained from tire testing on indoor test systems. This presentation highlights how different test methods can vary the repeatability of longitudinal force data during braking events. In addition, it will compare lateral force data from two machines to show correlation quality between indoor test systems. Optimizing the test method and machine can improves the data quality, which enables more accurate tire models.

A Predictive Thermo-Mechanical Framework for Tire Temperature and Performance Modeling

Francesco Calabrese･Manfred Baecker･Axel Gallrein･Christoph Burkhart･Tobias Ruhwedel (Fraunhofer institute for industrial mathematics)

The growing demand for sustainable products drives the industry to improve CAE methods for predicting wear and rolling resistance, both strongly temperature-dependent. The authors work enhances temperature modeling and parameterization in the thermo-mechanical CDTire model. Its finite-volume formulation separates geometry from material properties, improving physical accuracy and therefore reducing parameter identification effort due to enhanced prediction capability. A measurement-based methodology is presented and validated on multiple passenger-car tires, showing improved accuracy of standard characteristics with minimal extra effort. Temperature effects on cornering/braking stiffness, friction, and pressure are examined in details. Future work targets improved rolling-resistance and wear prediction.

Energy-Based Tire Wear Methodology: Transferring Public Road Conditions to Proving Ground Testing

Jordi Benach･Arturo Rubio･Eduardo Martano (Applus+ IDIADA)

Tire wear assessment traditionally requires extensive real-world testing, consuming significant time and resources. Accurate replication of public road wear patterns in controlled proving ground environments remains challenging. An innovative energy-based methodology was developed to characterize tire friction energy from public road driving data, quantifying longitudinal and longitudinal energy distributions across vehicle axles and wheels. Critical wear-inducing events were identified using threshold-based algorithms analyzing braking, acceleration, and cornering maneuvers. Specific proving ground test sequences successfully replicated road energy profiles obtaining relevant and comprehensive results. Energy-based characterization enables efficient transfer of real-world tire wear conditions to controlled testing environments, accelerating validation processes.