Multi-grid reduced-order topology optimization
| Autor: | Weihong Zhang, Balaji Raghavan, Subhrajit Dutta, Piotr Breitkopf, Manyu Xiao, Dongcheng Lu |
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| Přispěvatelé: | Northwestern Polytechnical University [Xi'an] (NPU), Roberval (Roberval), Université de Technologie de Compiègne (UTC), Laboratoire de Génie Civil et Génie Mécanique (LGCGM), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), National Institute of Technology [Silchar], National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11620101002, 11972166], Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [310201911cx029], Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Multi-grid Control and Optimization Iterative design Computer science Additive manufacturing 0211 other engineering and technologies 02 engineering and technology Reduced basis [SPI]Engineering Sciences [physics] 0203 mechanical engineering Component (UML) POD Topology optimization 021106 design practice & management Compliant mechanism Process (computing) Computer Graphics and Computer-Aided Design Computer Science Applications 020303 mechanical engineering & transports Control and Systems Engineering Benchmark (computing) Reduction (mathematics) Engineering design process Software |
| Zdroj: | Structural and Multidisciplinary Optimization Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2020, 61 (6), pp.1-23. ⟨10.1007/s00158-020-02570-y⟩ Structural and Multidisciplinary Optimization, 2020, 61 (6), pp.1-23. ⟨10.1007/s00158-020-02570-y⟩ |
| ISSN: | 1615-147X 1615-1488 |
| DOI: | 10.1007/s00158-020-02570-y⟩ |
| Popis: | International audience; Additive printing allows the "single step" production of virtually any complex mechanical component. However, the manufacturing process involves a layer-by-layer deposition of material, which leads to an anisotropic mechanical behavior of the whole component. This would then entail a very fine 3D model to simulate the mechanical performance accurately. This simulation would also need to be integrated within an iterative design process in order to obtain the most efficient design. Both reasons explain the prohibitive number of calculations needed, which is currently beyond the capacities of existing software and computers. Recent research papers have opened promising pathways for integrating model reduction techniques within the overall topology optimization process. However, these approaches still present challenges such as choosing the minimum number and appropriate selection of the snapshots required to get accurate simulations. In this work, we present a methodology in the combined field of reduced-order modeling and topology optimization. The key idea consists of projecting the higher dimensional system of equations onto a lower dimensional space with the reduced basis vectors constructed using Proper Orthogonal Decomposition (POD). This reduced basis is updated in an incremental "on-the-fly" manner using alternatively costly high-fidelity and more rapid lower fidelity simulation snapshots. The variable-fidelity resolutions of successive approximations to the global system of equations are then integrated into the topology optimization process. The approaches are tested and computational savings and precision are compared, using both minimum compliance and compliant mechanism design benchmark problems. |
| Databáze: | OpenAIRE |
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