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Capturing functional relations in fluid–structure interaction via machine learning
While fluid–structure interaction (FSI) problems are ubiquitous in various applications from cell biology to aerodynamics, they involve huge computational overhead. In this paper, we adopt a machine learning (ML)-based strategy to bypass the detailed FSI analysis that requires cumbersome simulations...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8984386/ https://www.ncbi.nlm.nih.gov/pubmed/35401993 http://dx.doi.org/10.1098/rsos.220097 |
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author | Soni, Tejas Sharma, Ashwani Dutta, Rajdeep Dutta, Annwesha Jayavelu, Senthilnath Sarkar, Saikat |
author_facet | Soni, Tejas Sharma, Ashwani Dutta, Rajdeep Dutta, Annwesha Jayavelu, Senthilnath Sarkar, Saikat |
author_sort | Soni, Tejas |
collection | PubMed |
description | While fluid–structure interaction (FSI) problems are ubiquitous in various applications from cell biology to aerodynamics, they involve huge computational overhead. In this paper, we adopt a machine learning (ML)-based strategy to bypass the detailed FSI analysis that requires cumbersome simulations in solving the Navier–Stokes equations. To mimic the effect of fluid on an immersed beam, we have introduced dissipation into the beam model with time-varying forces acting on it. The forces in a discretized set-up have been decoupled via an appropriate linear algebraic operation, which generates the ground truth force/moment data for the ML analysis. The adopted ML technique, symbolic regression, generates computationally tractable functional forms to represent the force/moment with respect to space and time. These estimates are fed into the dissipative beam model to generate the immersed beam’s deflections over time, which are in conformity with the detailed FSI solutions. Numerical results demonstrate that the ML-estimated continuous force and moment functions are able to accurately predict the beam deflections under different discretizations. |
format | Online Article Text |
id | pubmed-8984386 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-89843862022-04-08 Capturing functional relations in fluid–structure interaction via machine learning Soni, Tejas Sharma, Ashwani Dutta, Rajdeep Dutta, Annwesha Jayavelu, Senthilnath Sarkar, Saikat R Soc Open Sci Engineering While fluid–structure interaction (FSI) problems are ubiquitous in various applications from cell biology to aerodynamics, they involve huge computational overhead. In this paper, we adopt a machine learning (ML)-based strategy to bypass the detailed FSI analysis that requires cumbersome simulations in solving the Navier–Stokes equations. To mimic the effect of fluid on an immersed beam, we have introduced dissipation into the beam model with time-varying forces acting on it. The forces in a discretized set-up have been decoupled via an appropriate linear algebraic operation, which generates the ground truth force/moment data for the ML analysis. The adopted ML technique, symbolic regression, generates computationally tractable functional forms to represent the force/moment with respect to space and time. These estimates are fed into the dissipative beam model to generate the immersed beam’s deflections over time, which are in conformity with the detailed FSI solutions. Numerical results demonstrate that the ML-estimated continuous force and moment functions are able to accurately predict the beam deflections under different discretizations. The Royal Society 2022-04-06 /pmc/articles/PMC8984386/ /pubmed/35401993 http://dx.doi.org/10.1098/rsos.220097 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Engineering Soni, Tejas Sharma, Ashwani Dutta, Rajdeep Dutta, Annwesha Jayavelu, Senthilnath Sarkar, Saikat Capturing functional relations in fluid–structure interaction via machine learning |
title | Capturing functional relations in fluid–structure interaction via machine learning |
title_full | Capturing functional relations in fluid–structure interaction via machine learning |
title_fullStr | Capturing functional relations in fluid–structure interaction via machine learning |
title_full_unstemmed | Capturing functional relations in fluid–structure interaction via machine learning |
title_short | Capturing functional relations in fluid–structure interaction via machine learning |
title_sort | capturing functional relations in fluid–structure interaction via machine learning |
topic | Engineering |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8984386/ https://www.ncbi.nlm.nih.gov/pubmed/35401993 http://dx.doi.org/10.1098/rsos.220097 |
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