Data-driven optimization study of the multi-relaxation-time lattice Boltzmann method for solid-liquid phase change

Sharp phase interfaces and accurate temperature distributions are important criteria in the simulation of solid-liquid phase changes. The multi-relaxation-time lattice Boltzmann method (MRT-LBM) shows great numerical performance during simulation; however, the value method of the relaxation parameters needs to be specified. Therefore, in this study, a random forest (RF) model is used to discriminate the importance of different relaxation parameters to the convergence, and a support vector machine (SVM) is used to explore the decision boundary of the convergent samples in each dimensional model. The results show that the convergence of the samples is consistent with the sign of the decision number, and two types of the numerical deviations appear, i.e., the phase mushy zone and the non-physical heat transfer. The relaxation parameters chosen on the decision boundary can further suppress the numerical bias and improve numerical accuracy.