Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation

Rathakrishnan Bhaskaran; Ramakrishnan Kannan; Brian Barr; Stephan Priebe

doi:10.1109/BigData52589.2021.9671436

Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation

Rathakrishnan Bhaskaran, Ramakrishnan Kannan, Brian Barr, Stephan Priebe

Discrete Algorithms

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

A deep learning algorithm is trained to predict wall-shear stress based on flow in the outer region of transitional and turbulent boundary layer flow. Flow variables sampled from the boundary layer region of eight high quality wall-resolved Large Eddy Simulations of transonic compressor cascades in the presence of shock-boundary layer interaction effects are used to train the model. About 1.1 TB of data stored in compressed text format generated from the LES calculations is used to perform the model training. The model is shown to be generic and able to predict complex boundary layer physics including laminar to turbulent transition caused by the shock-boundary layer interaction.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
Editors	Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1809-1816
Number of pages	8
ISBN (Electronic)	9781665439022
DOIs	https://doi.org/10.1109/BigData52589.2021.9671436
State	Published - 2021
Event	2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: Dec 15 2021 → Dec 18 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference	2021 IEEE International Conference on Big Data, Big Data 2021
Country/Territory	United States
City	Virtual, Online
Period	12/15/21 → 12/18/21

Funding

This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The United States Government retains copyright and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Funders	Funder number
U.S. Department of Energy
UT-Battelle	DE-AC05-00OR22725

Keywords

boundary layer
deep learning
large eddy simulation
wall-modeling

Access to Document

10.1109/BigData52589.2021.9671436

Cite this

Bhaskaran, R., Kannan, R., Barr, B., & Priebe, S. (2021). Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation. In Y. Chen, H. Ludwig, Y. Tu, U. Fayyad, X. Zhu, X. T. Hu, S. Byna, X. Liu, J. Zhang, S. Pan, V. Papalexakis, J. Wang, A. Cuzzocrea, & C. Ordonez (Eds.), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 (pp. 1809-1816). (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData52589.2021.9671436

Bhaskaran, Rathakrishnan ; Kannan, Ramakrishnan ; Barr, Brian et al. / Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. editor / Yixin Chen ; Heiko Ludwig ; Yicheng Tu ; Usama Fayyad ; Xingquan Zhu ; Xiaohua Tony Hu ; Suren Byna ; Xiong Liu ; Jianping Zhang ; Shirui Pan ; Vagelis Papalexakis ; Jianwu Wang ; Alfredo Cuzzocrea ; Carlos Ordonez. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1809-1816 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

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title = "Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation",

abstract = "A deep learning algorithm is trained to predict wall-shear stress based on flow in the outer region of transitional and turbulent boundary layer flow. Flow variables sampled from the boundary layer region of eight high quality wall-resolved Large Eddy Simulations of transonic compressor cascades in the presence of shock-boundary layer interaction effects are used to train the model. About 1.1 TB of data stored in compressed text format generated from the LES calculations is used to perform the model training. The model is shown to be generic and able to predict complex boundary layer physics including laminar to turbulent transition caused by the shock-boundary layer interaction.",

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author = "Rathakrishnan Bhaskaran and Ramakrishnan Kannan and Brian Barr and Stephan Priebe",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",

year = "2021",

doi = "10.1109/BigData52589.2021.9671436",

language = "English",

series = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",

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editor = "Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama Fayyad and Xingquan Zhu and Hu, {Xiaohua Tony} and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez",

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Bhaskaran, R, Kannan, R, Barr, B & Priebe, S 2021, Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation. in Y Chen, H Ludwig, Y Tu, U Fayyad, X Zhu, XT Hu, S Byna, X Liu, J Zhang, S Pan, V Papalexakis, J Wang, A Cuzzocrea & C Ordonez (eds), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1809-1816, 2021 IEEE International Conference on Big Data, Big Data 2021, Virtual, Online, United States, 12/15/21. https://doi.org/10.1109/BigData52589.2021.9671436

Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation. / Bhaskaran, Rathakrishnan; Kannan, Ramakrishnan; Barr, Brian et al.
Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. ed. / Yixin Chen; Heiko Ludwig; Yicheng Tu; Usama Fayyad; Xingquan Zhu; Xiaohua Tony Hu; Suren Byna; Xiong Liu; Jianping Zhang; Shirui Pan; Vagelis Papalexakis; Jianwu Wang; Alfredo Cuzzocrea; Carlos Ordonez. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1809-1816 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation

AU - Bhaskaran, Rathakrishnan

AU - Kannan, Ramakrishnan

AU - Barr, Brian

AU - Priebe, Stephan

PY - 2021

Y1 - 2021

N2 - A deep learning algorithm is trained to predict wall-shear stress based on flow in the outer region of transitional and turbulent boundary layer flow. Flow variables sampled from the boundary layer region of eight high quality wall-resolved Large Eddy Simulations of transonic compressor cascades in the presence of shock-boundary layer interaction effects are used to train the model. About 1.1 TB of data stored in compressed text format generated from the LES calculations is used to perform the model training. The model is shown to be generic and able to predict complex boundary layer physics including laminar to turbulent transition caused by the shock-boundary layer interaction.

AB - A deep learning algorithm is trained to predict wall-shear stress based on flow in the outer region of transitional and turbulent boundary layer flow. Flow variables sampled from the boundary layer region of eight high quality wall-resolved Large Eddy Simulations of transonic compressor cascades in the presence of shock-boundary layer interaction effects are used to train the model. About 1.1 TB of data stored in compressed text format generated from the LES calculations is used to perform the model training. The model is shown to be generic and able to predict complex boundary layer physics including laminar to turbulent transition caused by the shock-boundary layer interaction.

KW - boundary layer

KW - deep learning

KW - large eddy simulation

KW - wall-modeling

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M3 - Conference contribution

AN - SCOPUS:85125316965

T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

SP - 1809

EP - 1816

BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

A2 - Chen, Yixin

A2 - Ludwig, Heiko

A2 - Tu, Yicheng

A2 - Fayyad, Usama

A2 - Zhu, Xingquan

A2 - Hu, Xiaohua Tony

A2 - Byna, Suren

A2 - Liu, Xiong

A2 - Zhang, Jianping

A2 - Pan, Shirui

A2 - Papalexakis, Vagelis

A2 - Wang, Jianwu

A2 - Cuzzocrea, Alfredo

A2 - Ordonez, Carlos

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Big Data, Big Data 2021

Y2 - 15 December 2021 through 18 December 2021

ER -

Bhaskaran R, Kannan R, Barr B, Priebe S. Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation. In Chen Y, Ludwig H, Tu Y, Fayyad U, Zhu X, Hu XT, Byna S, Liu X, Zhang J, Pan S, Papalexakis V, Wang J, Cuzzocrea A, Ordonez C, editors, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1809-1816. (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). doi: 10.1109/BigData52589.2021.9671436

Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation

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