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Machine Learning Co...
Machine Learning Computational Fluid Dynamics
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- Usman, Ali (author)
- Luleå tekniska universitet,EISLAB
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- Rafiq, Muhammad (author)
- Data Science Lab, Yeungnam University, Gyeongsan-si, South Korea
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- Saeed, Muhammad (author)
- Mechanical Engineering Department, Khalifa University of Science and Tech, Abu Dhabi, United Arb Emirates
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- Nauman, Ali (author)
- WINLab, Yeungnam University, Gyeongsan-si, South Korea
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- Almqvist, Andreas (author)
- Luleå tekniska universitet,Maskinelement
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- Liwicki, Marcus (author)
- Luleå tekniska universitet,EISLAB
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(creator_code:org_t)
- IEEE, 2021
- 2021
- English.
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In: 2021 Swedish Artificial Intelligence Society Workshop (SAIS). - : IEEE. ; , s. 46-49
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Strömningsmekanik och akustik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Fluid Mechanics and Acoustics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Machine learning
- fluid-structure interaction
- computational fluid dynamics
- numerical analyses
- flow past a cylinder
- Machine Elements
- Maskinelement
- Maskininlärning
- Machine Learning
Publication and Content Type
- ref (subject category)
- kon (subject category)
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