Numerical prediction of the sliding wear rate of deep-sea polymetallic nodules against metals based on CFD-BDEM coupled simulations

Artikel i vetenskaplig tidskrift, 2026

Deep-sea mining is expected to be at commercial scale shortly. Accurate prediction of sliding wear rate is essential for estimating the lifespan of handling equipment in deep-sea mining. However, existing prediction methods rely on measured wear volume from experiments, which are time-consuming and challenging due to limited access to polymetallic nodule samples. This study establishes a Computational Fluid Dynamics (CFD)–Bonding Discrete Element Method (BDEM) coupled model (CFD-BDEM) to predict the wear rate of polymetallic nodules against four metals (Q235, 304 stainless steel, H62 brass, 6061-T6 aluminum alloy). Innovatively, debris generation during sliding wear was mimicked by the bonding fracture process in the CFD-BDEM model. Simulations under both dry and wet conditions were conducted. Wet wear simulations exhibited an oscillatory trend of wear rate, while dry wear simulations showed a linear increase. Furthermore, experiments showed that the BDEM model achieved a 9.83% prediction error under dry wear, and the CFD-BDEM model showed a 9.36% error under wet wear. Thus, the proposed models for wear rate prediction were verified. This study provides an efficient and reliable simulation tool for predicting wear rates between minerals and metal materials without extensive wear tests.