Novel methodology of non-probabilistic reliability-based topology optimization (NRBTO) for multi-material layout design via interval and convex mixed uncertainties

This paper proposes an efficient topology optimization strategy for seeking the optimal layout of multi-material structures with mixed uncertainties of interval and convexity. In conjunction with the safety criterion for local displacement and the extended power-law interpolation scheme for the mate...

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Main Authors: Wang, Lei, Liu, Dongliang, Yang, Yaowen, Hu, Juxi
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/150735
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1507352021-06-08T03:59:08Z Novel methodology of non-probabilistic reliability-based topology optimization (NRBTO) for multi-material layout design via interval and convex mixed uncertainties Wang, Lei Liu, Dongliang Yang, Yaowen Hu, Juxi School of Civil and Environmental Engineering Engineering::Mechanical engineering Multi-material Structures Reliability-based Topology Optimization This paper proposes an efficient topology optimization strategy for seeking the optimal layout of multi-material structures with mixed uncertainties of interval and convexity. In conjunction with the safety criterion for local displacement and the extended power-law interpolation scheme for the material property, the task of the optimization problem is formulated as to minimize the total volume of different materials while satisfying reliability requirement. By introducing the set-theoretical convex method, uncertainty quantification analysis under mixture of interval and convex uncertainties is firstly conducted for exploring boundary rules of considered responses. Combined with the area-ratio principle and the shortest distance judgment, a novel reliability index with favorable mathematical characteristics is then defined. Moreover, for guarantee the computational efficiency as well as the iterative convergence, the adjoint-variable model is also established and the investigated minimization issue can be solved by a gradient-based optimization algorithm. Eventually, several numerical applications are given to demonstrate the validity and reasonability of the present topology optimization methodology. 2021-06-08T03:59:08Z 2021-06-08T03:59:08Z 2019 Journal Article Wang, L., Liu, D., Yang, Y. & Hu, J. (2019). Novel methodology of non-probabilistic reliability-based topology optimization (NRBTO) for multi-material layout design via interval and convex mixed uncertainties. Computer Methods in Applied Mechanics and Engineering, 346, 550-573. https://dx.doi.org/10.1016/j.cma.2018.11.035 0045-7825 0000-0003-0300-1423 https://hdl.handle.net/10356/150735 10.1016/j.cma.2018.11.035 2-s2.0-85058945667 346 550 573 en Computer Methods in Applied Mechanics and Engineering © 2018 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
Multi-material Structures
Reliability-based Topology Optimization
spellingShingle Engineering::Mechanical engineering
Multi-material Structures
Reliability-based Topology Optimization
Wang, Lei
Liu, Dongliang
Yang, Yaowen
Hu, Juxi
Novel methodology of non-probabilistic reliability-based topology optimization (NRBTO) for multi-material layout design via interval and convex mixed uncertainties
description This paper proposes an efficient topology optimization strategy for seeking the optimal layout of multi-material structures with mixed uncertainties of interval and convexity. In conjunction with the safety criterion for local displacement and the extended power-law interpolation scheme for the material property, the task of the optimization problem is formulated as to minimize the total volume of different materials while satisfying reliability requirement. By introducing the set-theoretical convex method, uncertainty quantification analysis under mixture of interval and convex uncertainties is firstly conducted for exploring boundary rules of considered responses. Combined with the area-ratio principle and the shortest distance judgment, a novel reliability index with favorable mathematical characteristics is then defined. Moreover, for guarantee the computational efficiency as well as the iterative convergence, the adjoint-variable model is also established and the investigated minimization issue can be solved by a gradient-based optimization algorithm. Eventually, several numerical applications are given to demonstrate the validity and reasonability of the present topology optimization methodology.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Wang, Lei
Liu, Dongliang
Yang, Yaowen
Hu, Juxi
format Article
author Wang, Lei
Liu, Dongliang
Yang, Yaowen
Hu, Juxi
author_sort Wang, Lei
title Novel methodology of non-probabilistic reliability-based topology optimization (NRBTO) for multi-material layout design via interval and convex mixed uncertainties
title_short Novel methodology of non-probabilistic reliability-based topology optimization (NRBTO) for multi-material layout design via interval and convex mixed uncertainties
title_full Novel methodology of non-probabilistic reliability-based topology optimization (NRBTO) for multi-material layout design via interval and convex mixed uncertainties
title_fullStr Novel methodology of non-probabilistic reliability-based topology optimization (NRBTO) for multi-material layout design via interval and convex mixed uncertainties
title_full_unstemmed Novel methodology of non-probabilistic reliability-based topology optimization (NRBTO) for multi-material layout design via interval and convex mixed uncertainties
title_sort novel methodology of non-probabilistic reliability-based topology optimization (nrbto) for multi-material layout design via interval and convex mixed uncertainties
publishDate 2021
url https://hdl.handle.net/10356/150735
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