基于耦合欧拉-拉格朗日法计算的随机多颗粒冷喷涂 AI 涂层沉积特性

This thesis presents a simulation and experimental study on the deposition characteristics of cold-sprayed Al coatings on U-shaped thin-walled aluminum tube substrates. A stochastic multi-particle impact model based on the Coupled Eulerian-Lagrangian (CEL) method has been established using ABAQUS software to predict the microstructure and porosity of coatings under different carrier gas pressures and temperatures. Meanwhile, corresponding coatings have been prepared experimentally, and their microstructures were observed to validate the simulation results. The results show that under the process conditions of a carrier gas pressure of 5.1 MPa and a temperature of 500 K, the coating exhibits the optimal performance with the lowest porosity (1.78%) and the highest tensile strength (47.55 MPa). The comparison of porosity between simulation and experiment under four different parameter sets shows that the error ranges from 0.64% to 0.86%, demonstrating good agreement. This indicates that the established stochastic multi-particle deposition model has high reliability and can provide a reference for the optimization of cold spray processes on tubular component surfaces.