An impact energy erosion model with an energy allocation rule for the discrete element method

Abstract

Surface erosion caused by solid particle impact frequently occurs in engineering applications involving particulate flow. With the tremendous progress in computational technology, discrete element method (DEM)-based numerical methods have become a powerful means of studying surface erosion. However, the applicability of applied erosion models remains one of the critical challenges in DEM modelling to obtain satisfactory accuracy over a wide range of materials and impact conditions. In this study, an impact energy erosion model (IEEM) that evaluates erosive wear using accumulated impact energy during the impact process is developed. In IEEM, both cutting and deformation wear are considered. A calibration method of the specific energy that combines DEM simulations and abrasive jet tests is established. To quantitatively predict the erosive wear of the target surface eroded by different particles, an energy allocation rule based on the hardness ratio of the target surface to particles is proposed. Its rationality is thoroughly evaluated before embarking on DEM simulations. Subsequently, the accuracy of DEM simulations incorporated with IEEM and the energy allocation rule is comprehensively verified by comparing them with experimental data on ductile, brittle and semi-brittle materials. The results confirm that DEM simulations can reasonably predict the dependence of erosive wear on impact velocities, impact angles and erodent particle hardness, regardless of the material involved. The calibrated specific energy reflects the properties of the material and further determines the wear mechanism. Finally, the influence of critical DEM parameters on erosion prediction is studied. Generally, DEM incorporated with IEEM and the energy allocation rule provides a quantitative erosion prediction framework applicable to different materials and impact conditions. In the future, this framework has the potential to be applied to the erosion prediction of more systems.