Periodica polytechnica. Civil engineering. Bauingenieurwesen, 2019-01, Vol.63 (1), p.278
2019
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Details
- Autor(en) / Beteiligte
- Titel
- Topology Optimization Under Uncertainty by Using the New Collocation Method
- Ist Teil von
- Periodica polytechnica. Civil engineering. Bauingenieurwesen, 2019-01, Vol.63 (1), p.278
- Ort / Verlag
- Budapest: Periodica Polytechnica, Budapest University of Technology and Economics
- Erscheinungsjahr
- 2019
- Quelle
- Free E-Journal (出版社公開部分のみ）
- Beschreibungen/Notizen
- In this paper, a robust topology optimization method presents that insensitive to the uncertainty in geometry. Geometric uncertainty can be introduced in the manufacturing variability. This uncertainty can be modeled as a random field. A memory-less transformation of random fields used to random variation modeling. The Adaptive Sparse Grid Collocation (ASGC) method combined with the geometry uncertainty models provides robust designs by utilizing already developed deterministic solvers. The proposed algorithm provides a computationally cheap alternative to previously introduced stochastic optimization methods based on Monte Carlo sampling by using the adaptive sparse grid method. The method is demonstrated in the design of a minimum compliance Messerschmitt-Bölkow-Blohm (MBB) and cantilever beam as benchmark problems.
- Sprache
- Englisch; Deutsch; Russisch
- Identifikatoren
- ISSN: 0553-6626eISSN: 1587-3773DOI: 10.3311/PPci.13068Titel-ID: cdi_proquest_journals_2187261540
- Format
- –
- Schlagworte
- Adaptive sampling, Algorithms, Cantilever beams, Cantilevers, Collocation methods, Computer simulation, Fields (mathematics), Genetic transformation, Grid method, Learning, Mathematical models, Modelling, Monte Carlo simulation, Robust design, Solvers, Statistical methods, Stochastic processes, Stochasticity, Topology, Topology optimization, Uncertainty