SLOPE

Scientific consensus posits that contemporary automotive friction brakes discharge a commensurate quantity of particulate matter when compared to combustion engines. These friction brake particles present pronounced hazards to both human health and the environment, particularly in the context of escalating global urbanization. Presently, the intricate mechanisms governing particle release from the frictional surfaces remain inadequately elucidated. The nuanced roles played by nano to micro scales of the tribological circuit, meso-scale spatial contact temperature fields, and macroscopic structural vibrations are poorly comprehended. Simultaneously, emissions under dynamic loads are a) propelled by enigmatic multi-factorial and multi-physical effects within high-dimensional spaces, b) subject to transient dynamical mechanisms spanning multiple scales, c) significantly influenced by load history effects and memory effects, and d) notably emitted during specific, albeit unknown, complex loading patterns coinciding with unfavorable friction surface states. This collaborative initiative seeks to comprehensively scrutinize brake particulate emissions, at their source, through a series of sophisticated experiments, high-fidelity numerical simulations, and cutting-edge machine learning methodologies. The endeavor aims to unveil the particle emission mechanisms intricately linked to multi-scale load history effects, thermo-mechanical dynamics, and friction surface properties. The project endeavors to seamlessly integrate numerical simulations with data-driven models. An anticipated methodological breakthrough aims to disrupt the scientific impasse surrounding friction under severe conditions, considering the interplay between tribological matter flow and thermomechanical mechanisms and by combining physical models with advanced learning methods. The undertaking aspires to formulate an innovative particle reduction strategy centered on friction surface preconditioning, thereby governing the tribological circuit. The collaborative efforts between Technische Universität Berlin and the University of Lille offer a distinctive opportunity for fundamental research, leveraging complementary laboratory equipment and domain expertise. Prof. P. Dufrénoy (Lille) oversees one of Europe's most advanced tribology testing facilities. This experimental setup, encompassing structural dynamics, particulate emissions, high-resolution interface temperature measurements, and frictional surface topographies simultaneously, currently stands unrivaled in Germany. Prof. M. Stender from (Berlin) is a foremost authority in data-driven modeling of frictional processes. A pioneer in applying big data analysis and deep learning to brake system behavior modeling, he actively explores sensor data fusion techniques, image data processing, and explicating AI models within the context of mechanical systems. The collaboration benefits from the prior research synergy of the applicants, indicating their effective teamwork in previous endeavors.