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Estimating flow fields with reduced order models

The estimation of fluid flows inside a centrifugal pump in realtime is a challenging task that cannot be achieved with long-established methods like CFD due to their computational demands. We use a projection-based reduced order model (ROM) instead. Based on this ROM, a realtime observer can be devi...

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Autores principales: Sommer, Kamil David, Reineking, Lucas, Ravichandran, Yogesh Parry, Skoda, Romuald, Mönnigmann, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623178/
https://www.ncbi.nlm.nih.gov/pubmed/37928036
http://dx.doi.org/10.1016/j.heliyon.2023.e20930
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author Sommer, Kamil David
Reineking, Lucas
Ravichandran, Yogesh Parry
Skoda, Romuald
Mönnigmann, Martin
author_facet Sommer, Kamil David
Reineking, Lucas
Ravichandran, Yogesh Parry
Skoda, Romuald
Mönnigmann, Martin
author_sort Sommer, Kamil David
collection PubMed
description The estimation of fluid flows inside a centrifugal pump in realtime is a challenging task that cannot be achieved with long-established methods like CFD due to their computational demands. We use a projection-based reduced order model (ROM) instead. Based on this ROM, a realtime observer can be devised that estimates the temporally and spatially resolved velocity and pressure fields inside the pump. The entire fluid-solid domain is treated as a fluid in order to be able to consider moving rigid bodies in the reduction method. A greedy algorithm is introduced for finding suitable and as few measurement locations as possible. Robust observability is ensured with an extended Kalman filter, which is based on a time-variant observability matrix obtained from the nonlinear velocity ROM. We present the results of the velocity and pressure ROMs based on a unsteady Reynolds-averaged Navier-Stokes CFD simulation of a 2D centrifugal pump, as well as the results for the extended Kalman filter.
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spelling pubmed-106231782023-11-04 Estimating flow fields with reduced order models Sommer, Kamil David Reineking, Lucas Ravichandran, Yogesh Parry Skoda, Romuald Mönnigmann, Martin Heliyon Research Article The estimation of fluid flows inside a centrifugal pump in realtime is a challenging task that cannot be achieved with long-established methods like CFD due to their computational demands. We use a projection-based reduced order model (ROM) instead. Based on this ROM, a realtime observer can be devised that estimates the temporally and spatially resolved velocity and pressure fields inside the pump. The entire fluid-solid domain is treated as a fluid in order to be able to consider moving rigid bodies in the reduction method. A greedy algorithm is introduced for finding suitable and as few measurement locations as possible. Robust observability is ensured with an extended Kalman filter, which is based on a time-variant observability matrix obtained from the nonlinear velocity ROM. We present the results of the velocity and pressure ROMs based on a unsteady Reynolds-averaged Navier-Stokes CFD simulation of a 2D centrifugal pump, as well as the results for the extended Kalman filter. Elsevier 2023-10-16 /pmc/articles/PMC10623178/ /pubmed/37928036 http://dx.doi.org/10.1016/j.heliyon.2023.e20930 Text en © 2023 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Sommer, Kamil David
Reineking, Lucas
Ravichandran, Yogesh Parry
Skoda, Romuald
Mönnigmann, Martin
Estimating flow fields with reduced order models
title Estimating flow fields with reduced order models
title_full Estimating flow fields with reduced order models
title_fullStr Estimating flow fields with reduced order models
title_full_unstemmed Estimating flow fields with reduced order models
title_short Estimating flow fields with reduced order models
title_sort estimating flow fields with reduced order models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623178/
https://www.ncbi.nlm.nih.gov/pubmed/37928036
http://dx.doi.org/10.1016/j.heliyon.2023.e20930
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