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E.K. Bontoft¹, D. Jia^1,2, V. Toropov and Y. Zhang^1,3

¹School of Engineering and Material Science, Queen Mary University of London, London, United Kingdom
²State IJR Center of Aerospace Design and Additive Manufacturing, Northwestern Polytechnical University, Xi’an, China
³School of Aeronautics, Northwestern Polytechnical University, Xi'an, China

doi:10.4203/ccc.9.4.1

E.K. Bontoft, D. Jia, V. Toropov, Y. Zhang, "Derivative-Free Trust-Region-Guided Explicit Level Set Topology Optimisation", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 9, Paper 4.1, 2024, doi:10.4203/ccc.9.4.1

Keywords: topology optimisation, explicit level set method, derivative-free, trust region strategy, multipoint approximation method, metamodel, kriging, design of experiments, permutation genetic algorithm.

Abstract

This work investigates the use of explicit level set parameterisation for topology optimisation using a metamodel-based trust region strategy optimiser. The explicit level set parameterisation consists of building a uniform Design of Experiments using a Permutation Genetic Algorithm, followed by building the Level Set Function using Kriging. Through decoupling the parameterisation from the simulation physics, the use of sensitivity data becomes optional thus enabling computationally complex disciplines (where sensitivity data is not available, e.g. crashworthiness, electromagnetics) to be included. This is achieved through the use of a sequence of approximations to the functions of the original optimisation problems based on a trust region strategy. The method is demonstrated on a benchmark 2D topology optimisation problem to examine the effectiveness of the technique.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 9 PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: P. Iványi, J. Kruis and B.H.V. Topping Paper 4.1 Derivative-Free Trust-Region-Guided Explicit Level Set Topology Optimisation E.K. Bontoft¹, D. Jia^1,2, V. Toropov and Y. Zhang^1,3 ¹School of Engineering and Material Science, Queen Mary University of London, London, United Kingdom ²State IJR Center of Aerospace Design and Additive Manufacturing, Northwestern Polytechnical University, Xi’an, China ³School of Aeronautics, Northwestern Polytechnical University, Xi'an, China doi:10.4203/ccc.9.4.1 Full Bibliographic Reference for this paper E.K. Bontoft, D. Jia, V. Toropov, Y. Zhang, "Derivative-Free Trust-Region-Guided Explicit Level Set Topology Optimisation", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 9, Paper 4.1, 2024, doi:10.4203/ccc.9.4.1 Keywords: topology optimisation, explicit level set method, derivative-free, trust region strategy, multipoint approximation method, metamodel, kriging, design of experiments, permutation genetic algorithm. Abstract This work investigates the use of explicit level set parameterisation for topology optimisation using a metamodel-based trust region strategy optimiser. The explicit level set parameterisation consists of building a uniform Design of Experiments using a Permutation Genetic Algorithm, followed by building the Level Set Function using Kriging. Through decoupling the parameterisation from the simulation physics, the use of sensitivity data becomes optional thus enabling computationally complex disciplines (where sensitivity data is not available, e.g. crashworthiness, electromagnetics) to be included. This is achieved through the use of a sequence of approximations to the functions of the original optimisation problems based on a trust region strategy. The method is demonstrated on a benchmark 2D topology optimisation problem to examine the effectiveness of the technique. download the full-text of this paper (PDF, 3442 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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