Efficient Multimodes Monte Carlo Method for Structural Topology Optimization With Random Uncertainties

ABSTRACT Structural topology optimization with random material or boundary load requires significant computational costs in solving numerous random elastic systems, particularly in 3D. Based on the multimode representation of displacement and the Monte Carlo method for sampling the probability space, a phase field model is proposed for robust topology optimization, where Young's modulus and the applied loading are characterized as random fields. The optimization algorithm is proposed based on the gradient flow scheme and the multimode Monte Carlo method. The well‐posedness and truncation error estimates of the multimode Monte Carlo method have been rigorously discussed. Numerical examples of compliance minimization and compliant mechanisms in both 2D and 3D show the proposed algorithm's accuracy, effectiveness, and efficiency for robust topology optimization.

Authors

Institutions

Publication Details

Journal
International Journal for Numerical Methods in Engineering
Published
2025-11-02
DOI
https://doi.org/10.1002/nme.70172
Primary Topic
Topology Optimization in Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Efficient Multimodes Monte Carlo Method for Structural Topology Optimization With Random Uncertainties

Shengfeng Zhu, Hui Yang, Jiajie Li
International Journal for Numerical Methods in Engineering
Topology Optimization in Engineering
article

Efficient Multimodes Monte Carlo Method for Structural Topology Optimization With Random Uncertainties

Shengfeng Zhu, Hui Yang, Jiajie Li
article en

Abstract

ABSTRACT Structural topology optimization with random material or boundary load requires significant computational costs in solving numerous random elastic systems, particularly in 3D. Based on the multimode representation of displacement and the Monte Carlo method for sampling the probability space, a phase field model is proposed for robust topology optimization, where Young's modulus and the applied loading are characterized as random fields. The optimization algorithm is proposed based on the gradient flow scheme and the multimode Monte Carlo method. The well‐posedness and truncation error estimates of the multimode Monte Carlo method have been rigorously discussed. Numerical examples of compliance minimization and compliant mechanisms in both 2D and 3D show the proposed algorithm's accuracy, effectiveness, and efficiency for robust topology optimization.

International Journal for Numerical Methods in EngineeringVol. 126(21)
Shanghai Jiao Tong University (CN), East China Normal University (CN)
Openalex Percentile: Top 66%
Topology Optimization in Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.