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An Evolution-Based High-Cycle Fatigue Constraint in Topology Optimization
Linköping University, Department of Management and Engineering, Solid Mechanics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-9203-2138
Linköping University, Department of Management and Engineering, Solid Mechanics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1503-8293
Linköping University, Department of Management and Engineering, Solid Mechanics. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Management and Engineering, Solid Mechanics. Linköping University, Faculty of Science & Engineering.
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2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We develop a topology optimization method including high-cycle fatigue as a constraint. The fatigue model is based on a continuous-time approach, which uses the concept of a moving endurance surface as a function of the stress history and back stress evolution. The development of damage only occurs when the stress state lies outside the endurance surface. Furthermore, an aggregation function, which approximates the maximum fatigue damage, is implemented. As the optimization workflow is sensitivity-based, the fatigue sensitivities are determined using an adjoint sensitivity analysis. The capabilities of the presented approach are tested on numerical models where the problem is to maximize the stiffness subject to high-cycle fatigue constraints.

Place, publisher, year, edition, pages
Cham, Switzerland, 2018. p. 844-854
Keywords [en]
Endurance surface, High-cycle fatigue, Topology optimization, Adjoint sensitivity analysis, Aggregation function
National Category
Applied Mechanics
Identifiers
URN: urn:nbn:se:liu:diva-155097DOI: 10.1007/978-3-319-97773-7_73ISBN: 978-3-319-97772-0 (print)ISBN: 978-3-319-97773-7 (electronic)OAI: oai:DiVA.org:liu-155097DiVA, id: diva2:1296900
Conference
EngOpt 2018, Proceedings of the 6th International Conference on Engineering Optimization, Lisboa, Portugal, 17-19 September, 2018
Funder
Vinnova, 2016-05175EU, Horizon 2020, 738002Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-03-22Bibliographically approved

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Suresh, ShyamLindström, Stefan BThore, Carl-JohanTorstenfelt, BoKlarbring, Anders

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CiteExportLink to record
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Citation style
  • apa
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