A novel preconditioned conjugate gradient multigrid method for multi-material topology optimization
Nam G. Luu, Thanh T. Banh

TL;DR
This paper introduces a high-performance computing approach using a preconditioned conjugate gradient multigrid method to efficiently solve large-scale multi-material topology optimization problems, demonstrating improved computational speed.
Contribution
It integrates pCGMG into multi-material topology optimization, showcasing its effectiveness for large-scale problems with reduced computational time.
Findings
pCGMG accelerates convergence in large-scale MTO problems
Numerical examples show reduced iteration counts
Significant decrease in computational time for larger meshes
Abstract
In recent years, topology optimization has been developed sufficiently and many researchers have concentrated on enhancing to computationally numerical algorithms for computational effectiveness of this method. Along with the development of topology optimization, High Performance Computing (HPC) was marked by a strong dynamic mechanism with a continuous appearance and disappearance of manufacturers, systems, and architectures. Preconditioned conjugate gradient multigrid method (pCGMG) is the most popular in HPC due to its advantage in very large-scale problems. The idea which applies high performance computing to reduce time of running in multi-material topology optimization (MTO) problems with computational time burdens is newly proposed in this article. In multi-material topology optimization procedures, pCGMG is applied for solving linear equation arising from discretization of…
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Taxonomy
TopicsTopology Optimization in Engineering · Metaheuristic Optimization Algorithms Research
