Hybrid fictitious domain-immersed boundary method in CFD-based topology optimization

0560839 - ÚT 2023 CZ eng C - Conference Paper (international conference)
Kubíčková, Lucie - Isoz, Martin
Hybrid fictitious domain-immersed boundary method in CFD-based topology optimization.
Topical Problems of Fluid Mechanics 2022. Praha: Ústav termomechaniky AV ČR, v. v. i., 2022 - (Šimurda, D.; Bodnár, T.), s. 119-126. ISBN 978-80-87012-77-2. ISSN 2336-5781.
[Topical problems of fluid mechanics 2022. Praha (CZ), 16.02.2022-18.02.2022]
R&D Projects: GA MŠMT(CZ) EF15_003/0000493
Institutional support: RVO:61388998
Keywords : HFDIB * topology optimization * CFD * OpenFOAM
OECD category: Applied mechanics
http://www2.it.cas.cz/fm2015/im/admin/showfile/data/my/Papers/2022/17-TPFM2022.pdf

Advances in technological development, especially in 3D printing, allow engineers to design components with almost arbitrary shape and connectivity. Consequently, more and more attention is being directed towards a highly-specialized application-driven component design based on topology optimization (TO). In the present work, we propose a methodology enabling TO of components in contact with flowing fluids. In particular, the optimization itself is based on multi-objective evolutionary algorithms (MOEAs) with the component geometry encoded using a binary representation. The optimization criteria are evaluated via computational fluid dynamics (CFD). The main novelty of the proposed TO framework lies in its robustness and effectiveness achieved by utilizing a single computational mesh for all the tested designs and projecting the specific components shapes onto it by the means of an immersed boundary method. The new methodology capabilities are illustrated on a shape optimization of a diffuser equipped as a part of an ejector. The optimization goal was to increase the ejector energy efficiency. The newly proposed methodology was able to identify a design by roughly 9 % more efficient than an alternative one found utilizing a previously published and less general optimization approach.
Permanent Link: https://hdl.handle.net/11104/0334777