Dynamic self-adaptive ANP algorithm and its application to electric field simulation of aluminum reduction cell

• Published in: Journal of Central South University Vol. 22; no. 12; pp. 4731 - 4739
• Main Authors: Wang, Ya-lin, Chen, Dong-dong, Chen, Xiao-fang, Cai, Guo-min, Yang, Chun-hua
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.12.2015
• Subjects: Engineering; Metallic Materials; electric field simulation; finite element parallel computing (FEPC); aluminum reduction cell (ARC); region partition (RP); dynamic self-adaptive ANP (DSA-ANP) algorithm
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-015-3025-5

Region partition (RP) is the key technique to the finite element parallel computing (FEPC), and its performance has a decisive influence on the entire process of analysis and computation. The performance evaluation index of RP method for the three-dimensional finite element model (FEM) has been given. By taking the electric field of aluminum reduction cell (ARC) as the research object, the performance of two classical RP methods, which are Al-NASRA and NGUYEN partition (ANP) algorithm and the multi-level partition (MLP) method, has been analyzed and compared. The comparison results indicate a sound performance of ANP algorithm, but to large-scale models, the computing time of ANP algorithm increases notably. This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration. To obtain the satisfied speed and the precision, an improved dynamic self-adaptive ANP (DSA-ANP) algorithm has been proposed. With consideration of model scale, complexity and sub-RP stage, the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight, and then dynamically adds these connected elements. The proposed algorithm has been applied to the finite element analysis (FEA) of the electric field simulation of ARC. Compared with the traditional ANP algorithm, the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s. This proves the superiority of the improved algorithm on computing time performance.