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Machine Learning Computational Fluid Dynamics
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-2686-534X
Data Science Lab, Yeungnam University, Gyeongsan-si, South Korea.
Mechanical Engineering Department, Khalifa University of Science and Tech, Abu Dhabi, United Arb Emirates.
WINLab, Yeungnam University, Gyeongsan-si, South Korea.
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2021 (English)In: 2021 Swedish Artificial Intelligence Society Workshop (SAIS), IEEE, 2021, p. 46-49Conference paper, Published paper (Refereed)
Abstract [en]

Numerical simulation of fluid flow is a significant research concern during the design process of a machine component that experiences fluid-structure interaction (FSI). State-of-the-art in traditional computational fluid dynamics (CFD) has made CFD reach a relative perfection level during the last couple of decades. However, the accuracy of CFD is highly dependent on mesh size; therefore, the computational cost depends on resolving the minor feature. The computational complexity grows even further when there are multiple physics and scales involved making the approach time-consuming. In contrast, machine learning (ML) has shown a highly encouraging capacity to forecast solutions for partial differential equations. A trained neural network has offered to make accurate approximations instantaneously compared with conventional simulation procedures. This study presents transient fluid flow prediction past a fully immersed body as an integral part of the ML-CFD project. MLCFD is a hybrid approach that involves initialising the CFD simulation domain with a solution forecasted by an ML model to achieve fast convergence in traditional CDF. Initial results are highly encouraging, and the entire time-based series of fluid patterns past the immersed structure is forecasted using a deep learning algorithm. Prepared results show a strong agreement compared with fluid flow simulation performed utilising CFD.

Place, publisher, year, edition, pages
IEEE, 2021. p. 46-49
Keywords [en]
Machine learning, fluid-structure interaction, computational fluid dynamics, numerical analyses, flow past a cylinder
National Category
Fluid Mechanics Computer Sciences
Research subject
Machine Elements; Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-86565DOI: 10.1109/SAIS53221.2021.9483997ISI: 000855522600012Scopus ID: 2-s2.0-85111579836OAI: oai:DiVA.org:ltu-86565DiVA, id: diva2:1584441
Conference
33rd Workshop of the Swedish Artificial Intelligence Society (SAIS 2021), online, 14-15 June, 2021
Funder
The Kempe FoundationsSwedish Research Council, DNR 2019-04293
Note

ISBN för värdpublikation: 978-1-6654-4236-7

Available from: 2021-08-12 Created: 2021-08-12 Last updated: 2025-02-09Bibliographically approved

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Usman, AliAlmqvist, AndreasLiwicki, Marcus

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