2021
DOI: 10.1002/nme.6641
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A novel primal‐dual eigenstress‐driven method for shakedown analysis of structures

Abstract: The shakedown load of elastoplastic structures under multiple variable loading is an important factor in structural design and integrity analysis. In classical plasticity shakedown analysis is an essential and challenging problem. Most existing methods are based on the solution of super large‐scale mathematical programming, basis reduction or mechanics insight method which have their own limitations in practical engineering problem. In the present study, the proposed method explores the Karush–Kuhn–Tucker (KKT… Show more

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Cited by 9 publications
(7 citation statements)
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“…In order to solve the above minimization problem (10), we state the optimality conditions characterizing the solution of the static shakedown problem, which are obtained 30 by constructing the Lagrangian functional of the constrained minimization problem (10) as L()λ,boldσs,boldα1,boldα2goodbreak=goodbreak−λgoodbreak+i=1NVj=1NEα1,italicjifji()λσjiEgoodbreak+σjsgoodbreak−boldα2Tj=1NECjTσjs,$$ L\left(\lambda, {\boldsymbol{\upsigma}}^s,{\boldsymbol{\upalpha}}_1,{\boldsymbol{\upalpha}}_2\right)=-\lambda +\sum \limits_{i=1}^{NV}\sum \limits_{j=1}^{NE}{\boldsymbol{\upalpha}}_{1, ji}{f}_{ji}\left(\lambda {\boldsymbol{\upsigma}}_{ji}^E+{\boldsymbol{\upsigma}}_j^s\right)-{{\boldsymbol{\upalpha}}_2}^T\sum \limits_{j=1}^{NE}{C_j}^T{\boldsymbol{\upsigma}}_j^s, $$ where α1$$ {\boldsymbol{\upalpha}}_1 $$ and α2$$ {\boldsymbol{\upalpha}}_2 $$ are the Lagrangian multipliers related to the yield condition and the self‐equilibrated condition respectively, which are also called the dual variables in the optimization theory. Accordingly, λ,σs$$ \lambda, {\boldsymbol{\upsigma}}^s $$ are the primal variables.…”
Section: Shakedown Theory Of Elastoplastic Modelmentioning
confidence: 99%
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“…In order to solve the above minimization problem (10), we state the optimality conditions characterizing the solution of the static shakedown problem, which are obtained 30 by constructing the Lagrangian functional of the constrained minimization problem (10) as L()λ,boldσs,boldα1,boldα2goodbreak=goodbreak−λgoodbreak+i=1NVj=1NEα1,italicjifji()λσjiEgoodbreak+σjsgoodbreak−boldα2Tj=1NECjTσjs,$$ L\left(\lambda, {\boldsymbol{\upsigma}}^s,{\boldsymbol{\upalpha}}_1,{\boldsymbol{\upalpha}}_2\right)=-\lambda +\sum \limits_{i=1}^{NV}\sum \limits_{j=1}^{NE}{\boldsymbol{\upalpha}}_{1, ji}{f}_{ji}\left(\lambda {\boldsymbol{\upsigma}}_{ji}^E+{\boldsymbol{\upsigma}}_j^s\right)-{{\boldsymbol{\upalpha}}_2}^T\sum \limits_{j=1}^{NE}{C_j}^T{\boldsymbol{\upsigma}}_j^s, $$ where α1$$ {\boldsymbol{\upalpha}}_1 $$ and α2$$ {\boldsymbol{\upalpha}}_2 $$ are the Lagrangian multipliers related to the yield condition and the self‐equilibrated condition respectively, which are also called the dual variables in the optimization theory. Accordingly, λ,σs$$ \lambda, {\boldsymbol{\upsigma}}^s $$ are the primal variables.…”
Section: Shakedown Theory Of Elastoplastic Modelmentioning
confidence: 99%
“…To solve the optimality conditions (13) of shakedown analysis, we utilize a recently novel approach called primal‐dual eigenstress‐driven method (PEM) 30 . The PEM approach proposes an efficient iterative strategy for determining the quantity of primal and dual variables λ,σs,α1,α2$$ \lambda, {\boldsymbol{\upsigma}}^s,{\boldsymbol{\upalpha}}_1,{\boldsymbol{\upalpha}}_2 $$ that do not need mathematical programming solvers and elastoplastic incremental analysis.…”
Section: Shakedown Theory Of Elastoplastic Modelmentioning
confidence: 99%
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