An interpretable framework of data-driven turbulence modeling using deep neural networks-Reference-Cited by-同舟云学术

An interpretable framework of data-driven turbulence modeling using deep neural networks

Published:2021-05 Issue:5 Volume:33 Page:055133
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Jiang Chao¹²³^ORCID,Vinuesa Ricardo⁴^ORCID,Chen Ruilin¹²,Mi Junyi¹²,Laima Shujin¹²³^ORCID,Li Hui¹²³^ORCID

Affiliation:

1. Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China

2. Key Lab of Structural Dynamics and Control of Ministry of Education, Harbin 150090, China

3. Guangdong-Hong Kong-Macao Joint Laboratory for Data-driven Fluid Mechanics and Engineering Applications, Southern University of Science and Technology, Shenzhen 518055, China

4. SimEx/Flow, Engineering Mechanics, KTH Royal Institute of Technology and Swedish e-Science Research Centre (SeRC), Stockholm 100 44, Sweden

Funder

National Natural Science Foundation of China

Guangdong Science and Technology Department

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0048909

Reference125 articles.

1. Grid-point requirements for large eddy simulation: Chapman’s estimates revisited

2. Grid-point and time-step requirements for direct numerical simulation and large-eddy simulation

3. Computing high-Reynolds-number turbulence: will simulations ever replace experiments?

4. J. Slotnick , A. Khodadoust , J. Alonso , D. Darmofal , W. Gropp , E. Lurie , and D. Mavriplis , “ CFD vision 2030 study: A path to revolutionary computational aerosciences,” Report No. NASA/CR-2014-218178, 2014.

5. K. Duraisamy , P. R. Spalart , and C. L. Rumsey , “ Status, emerging ideas and future directions of turbulence modeling research in aeronautics,” Report No. NASA/TM-2017-219682, 2017.

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