Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics

Abstract This work presents a machine learning based method for bi-fidelity modelling. The method, a Knowledge Based Neural Network (KBaNN), performs a local, additive correction to the outputs of a coarse computational model and can be used to emulate either experimental data or the output of a mor...

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Autores principales:	Nick Pepper, Audrey Gaymann, Sanjiv Sharma, Francesco Montomoli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3db603d25349419baf2ed4038d973d20
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spelling	oai:doaj.org-article:3db603d25349419baf2ed4038d973d202021-12-02T15:33:02ZLocal bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics10.1038/s41598-021-93280-y2045-2322https://doaj.org/article/3db603d25349419baf2ed4038d973d202021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93280-yhttps://doaj.org/toc/2045-2322Abstract This work presents a machine learning based method for bi-fidelity modelling. The method, a Knowledge Based Neural Network (KBaNN), performs a local, additive correction to the outputs of a coarse computational model and can be used to emulate either experimental data or the output of a more accurate, but expensive, computational model. An advantage of the method is that it can scale easily with the number of input and output features. This allows bi-fidelity modelling approaches to be applied to a wide variety of problems, for instance in the bi-fidelity modelling of fields. We demonstrate this aspect in this work through an application to Computational Fluid Dynamics, in which local corrections to a velocity field are performed by the KBaNN to account for mesh effects. KBaNNs were trained to make corrections to the free-stream velocity field and the boundary layer. They were trained on a limited data-set consisting of simple two-dimensional flows. The KBaNNs were then tested on a flow over a more complex geometry, a NACA 2412 airfoil. It was demonstrated that the KBaNNs were still able to provide a local correction to the velocity field which improved its accuracy. The ability of the KBaNNs to generalise to flows around new geometries that share similar physics is encouraging. Through knowledge based neural networks it may be possible to develop a system for bi-fidelity, computer based design which uses data from past simulations to inform its predictions.Nick PepperAudrey GaymannSanjiv SharmaFrancesco MontomoliNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nick Pepper Audrey Gaymann Sanjiv Sharma Francesco Montomoli Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics
description	Abstract This work presents a machine learning based method for bi-fidelity modelling. The method, a Knowledge Based Neural Network (KBaNN), performs a local, additive correction to the outputs of a coarse computational model and can be used to emulate either experimental data or the output of a more accurate, but expensive, computational model. An advantage of the method is that it can scale easily with the number of input and output features. This allows bi-fidelity modelling approaches to be applied to a wide variety of problems, for instance in the bi-fidelity modelling of fields. We demonstrate this aspect in this work through an application to Computational Fluid Dynamics, in which local corrections to a velocity field are performed by the KBaNN to account for mesh effects. KBaNNs were trained to make corrections to the free-stream velocity field and the boundary layer. They were trained on a limited data-set consisting of simple two-dimensional flows. The KBaNNs were then tested on a flow over a more complex geometry, a NACA 2412 airfoil. It was demonstrated that the KBaNNs were still able to provide a local correction to the velocity field which improved its accuracy. The ability of the KBaNNs to generalise to flows around new geometries that share similar physics is encouraging. Through knowledge based neural networks it may be possible to develop a system for bi-fidelity, computer based design which uses data from past simulations to inform its predictions.
format	article
author	Nick Pepper Audrey Gaymann Sanjiv Sharma Francesco Montomoli
author_facet	Nick Pepper Audrey Gaymann Sanjiv Sharma Francesco Montomoli
author_sort	Nick Pepper
title	Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics
title_short	Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics
title_full	Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics
title_fullStr	Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics
title_full_unstemmed	Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics
title_sort	local bi-fidelity field approximation with knowledge based neural networks for computational fluid dynamics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3db603d25349419baf2ed4038d973d20
work_keys_str_mv	AT nickpepper localbifidelityfieldapproximationwithknowledgebasedneuralnetworksforcomputationalfluiddynamics AT audreygaymann localbifidelityfieldapproximationwithknowledgebasedneuralnetworksforcomputationalfluiddynamics AT sanjivsharma localbifidelityfieldapproximationwithknowledgebasedneuralnetworksforcomputationalfluiddynamics AT francescomontomoli localbifidelityfieldapproximationwithknowledgebasedneuralnetworksforcomputationalfluiddynamics
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Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics

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