Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43970327" target="_blank" >RIV/49777513:23520/23:43970327 - isvavai.cz</a>
Result on the web
<a href="https://www.tandfonline.com/doi/epdf/10.1080/10618562.2023.2260763?needAccess=true" target="_blank" >https://www.tandfonline.com/doi/epdf/10.1080/10618562.2023.2260763?needAccess=true</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/10618562.2023.2260763" target="_blank" >10.1080/10618562.2023.2260763</a>

Result language
angličtina
Original language name
Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains
Original language description
We design and implement a physics-informed convolutional neural network (CNN) to solve fluid flow problems on a parametrised domain. The goal is to compare the effectiveness of training based solely on CFD-generated training data with purely physics-informed training and training based on a combination of both. We consider the problem of incompressible fluid flow in a convergent-divergent channel with variable wall shape. A speciality of the designed neural network is the incorporation of the boundary condition directly in the CNN. A physics-informed CNN that uses a non-Cartesian mesh poses a challenge when evaluating partial derivatives. We propose a gradient layer that pproximates the first and second partial derivatives by finite differences using Lagrange interpolation. Our analysis shows that the convergence of purely physics-informed training is slow. However, using a small training dataset in combination with physics-informed training can achieve results comparable to physics-uninformed training with a considerably larger training dataset.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20302 - Applied mechanics

Project
<a href="/en/project/GA21-31457S" target="_blank" >GA21-31457S: Fast flow-field prediction using deep neural networks for solving fluid-structure interaction problems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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