Polymer composites modeling in the tire industry

Polymer composites are extensively used in the tire industry. They consist of a polymeric matrix, typically natural rubber, combined with inorganic fillers such as carbon black or silica. These fillers improve mechanical properties, such as hardness and tear resistance, while also modifying thermal and electrical conductivities. The polymeric matrix forms a complex network of chains that vary in size from millimeters to centimeters, and relaxation times range from seconds to years. At smaller scales, typically, polymer chains have a radius of gyration of 10 to 100 Angstroms and relaxation times of 10 to 100 nanoseconds [65]. Primary filler particles are about 20-50 nm in diameter but can aggregate within the polymer network. Hence, the prediction of polymer materials' physical and mechanical properties, essential for their development, presents a huge challenge both theoretically and computationally, demanding advanced multiscale modeling approaches. This chapter discusses the main materials in tires, the state-of-the-art in multiscale modeling, and the use of machine learning to address current challenges in characterizing and optimizing these materials.