2016
DOI: 10.1002/nme.5234
|View full text |Cite
|
Sign up to set email alerts
|

Continuum structural topological optimizations with stress constraints based on an active constraint technique

Abstract: SUMMARYStress-related problems have not been given the same attention as the minimum compliance topological optimization problem in the literature. Continuum structural topological optimization with stress constraints is of wide engineering application prospect, in which there still are many problems to solve, such as the stress concentration, an equivalent approximate optimization model and etc. A new and effective topological optimization method of continuum structures with the stress constraints and the obj… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4

Citation Types

0
4
0

Year Published

2017
2017
2023
2023

Publication Types

Select...
6
1
1

Relationship

0
8

Authors

Journals

citations
Cited by 19 publications
(4 citation statements)
references
References 34 publications
0
4
0
Order By: Relevance
“…This algorithm solves the topology optimization to minimize the volume of the structure under stress constraints. Rong [12] established a topology optimization method for a continuum structure with stress gradient constraints, solved the problem of stress concentration, and proposed a topology optimization algorithm through P-norm and Lagrange multipliers. Fan Zhao [13] and others proposed the following.…”
Section: Introductionmentioning
confidence: 99%
“…This algorithm solves the topology optimization to minimize the volume of the structure under stress constraints. Rong [12] established a topology optimization method for a continuum structure with stress gradient constraints, solved the problem of stress concentration, and proposed a topology optimization algorithm through P-norm and Lagrange multipliers. Fan Zhao [13] and others proposed the following.…”
Section: Introductionmentioning
confidence: 99%
“…To reduce the computational complexity, local stress constraints are usually transformed into a global stress constraint or some form of cluster-based stress constraints by using a condensation function that approximates the value of the maximum local function. Currently, the two most common types of condensation functions are the Kieisselmeier-Steinhauser function(K-S) [4,[12][13][14][15]17] and the P-norm function [9,[16][17][18], and they can merge a mass of local stresses into a single constraint formula.…”
Section: Introductionmentioning
confidence: 99%
“…To reduce the computational complexity, local stress constraints are usually transformed into a global stress constraint or some form of cluster-based stress constraints by using a condensation function that approximates the value of the maximum local function. Currently, the two most common types of condensation functions are the Kreisselmeier-Steinhauser function (K-S) [4,[12][13][14][15][16] and the P-norm function [9,[16][17][18], and they can merge a mass of local stresses into a single constraint formula. 3.…”
Section: Introductionmentioning
confidence: 99%
“…Highly nonlinear nature of the stress constraint. Any change in density value of the adjacent region will significantly affect the stress level within some key regions [13,17] such as depressions and corners. This requires the optimization procedure to be able to effectively reduce or eliminate the stress concentration phenomenon, and that the solution algorithm must maintain numerical consistency with the optimization procedure in order to ensure stable convergence of the overall procedure [19,20].…”
Section: Introductionmentioning
confidence: 99%