Non-probabilistic reliability-based topology optimization against loading uncertainty field with a bounded field model

Zhan, Junjie; Bai, Zhonghang

doi:10.1007/s00158-022-03447-y

Cited by 6 publications

(3 citation statements)

References 53 publications

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“…[17][18][19][20] Over the years, the focus of RBTO has not only been on enhancing efficiency, [21][22][23] but also on applying advanced uncertainty models such as the convex model, 24,25 interval model, 26,27 fuzzy set model 28,29 and random field. 30,31 These studies mainly focus on the stress and reliability of compliance in purely elastic structures. Recently, the RBTO of thermoelastic structures has garnered considerable attention.…”

Section: Introductionmentioning

confidence: 99%

“…Since the pioneering work on RBTO by Kharmanda et al, 16 significant advancements have been made in probabilistic constrained RBTO 17–20 . Over the years, the focus of RBTO has not only been on enhancing efficiency, 21–23 but also on applying advanced uncertainty models such as the convex model, 24,25 interval model, 26,27 fuzzy set model 28,29 and random field 30,31 . These studies mainly focus on the stress and reliability of compliance in purely elastic structures.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty‐oriented thermoelastic topology optimization with stress constraint

Cheng,

Yang,

Wang

et al. 2024

Numerical Meth Engineering

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The material redistribution abilities of traditional deterministic topology optimization are effective in addressing stress‐related design issues in thermal elastic structures. However, uncertainties are inevitable in real‐world. The structural strength of a design, achieved through deterministic topology optimization, is highly susceptible to these uncertainties, which may result in failure. This article introduces a novel method for topology optimization in stress‐constrained thermoelastic structures, taking into account the uncertainties associated with heat sources and loads. We employ the Kieisselmeier–Steinhauser function to aggregate the stress constraint when constructing the performance function. In order to improve optimization efficiency, the sequential optimization and reliability assessment method is used to decouple the double‐layer loop reliability‐based topology optimization. Initially, we derive the derivative of stress‐based performance functions with respect to heat source and load uncertainty variables, thereby facilitating the use of modified chaos control for assessing structural reliability and imposing constraints. The adjoint method and chain rule are utilized to obtain the derivative information of the performance function with respect to density variables, guiding the topology updates. We present five design examples to demonstrate the effectiveness of the presented method. Monte Carlo simulations for the optimized results are performed to show that the presented method can obtain a structure that meets reliability requirements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty‐oriented thermoelastic topology optimization with stress constraint

Cheng,

Yang,

Wang

et al. 2024

Numerical Meth Engineering

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“…29,30 In RBTO, reliability constraints are seamlessly integrated into the TO process. It is worth noting that most existing RBTO researches mainly focus on non-stress-constrained design, [31][32][33][34][35] while only a few studies have tackled the more challenging stress-constrained design problem. One noteworthy example is the work of Dos Santos et al, 36 where they directly applied the Sequential Optimization and Reliability Assessment (SORA) method to tackle stress-constrained weight reduction design, while accounting for uncertainties in load magnitude and stress limits.…”

Section: Introductionmentioning

confidence: 99%

A decoupled probabilistic constrained topology optimization method based on the constraint shift

2024

Numerical Meth Engineering

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Topology optimization (TO) has recently emerged as an advanced design method. To ensure practical reliability in the design process, it is imperative to incorporate considerations of uncertainty. Consequently, performing reliability analysis (RA) during the design phase becomes necessary. However, RA itself constitutes an optimization problem. Combining these two optimization problems can result in inefficiency. To address this challenge, we propose a decoupled approach that integrates deterministic topology optimization (DTO) and RA cycles. The reliability‐based stress‐constrained TO (RBSCTO) problem is considered in this paper. The DTO constraint is derived based on shifting vectors derived from the previous cycle's RA outcomes, enabling low‐reliability constraint shift towards the feasible direction. The DTO is solved based on solid‐isotropic‐material‐with‐penalization (SIMP) and augmented Lagrangian method. Meanwhile, the optimization problem in RA is addressed using finite differences and the interior point method. To reduce the errors resulting from linear approximation and optimization in RA when the target reliability is very low, an outlier handling method is employed. Meantime, we utilize a probabilistic neural network to enhance the efficiency of reliability assessment. Comparative studies against traditional methods across four RBSCTO tasks are demonstrated to validate its effectiveness. Monte Carlo simulations are used to validate the reliability of results.

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