scholarly journals Machine learning accelerated turbulence modeling of transient flashing jets

2021 ◽  
Vol 33 (12) ◽  
pp. 127104
Author(s):  
David Schmidt ◽  
Romit Maulik ◽  
Konstantinos Lyras
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling

Machine learning methods for turbulence modeling in subsonic flows around airfoils

2019 ◽  
Vol 31 (1) ◽  
pp. 015105 ◽  
Author(s):  
Linyang Zhu ◽  
Weiwei Zhang ◽  
Jiaqing Kou ◽  
Yilang Liu
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling ◽  
Learning Methods ◽  
Subsonic Flows ◽  
Machine Learning Methods

Modeling the pressure strain correlation in turbulent flows using deep neural networks

2021 ◽  
pp. 095440622110429
Author(s):  
Jyoti P Panda ◽  
Hari V Warrior
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Turbulent Flows ◽  
Reynolds Stress ◽  
Channel Flow ◽  
Turbulence Modeling ◽  
Critical Role ◽  
Turbulent Channel Flow ◽  
Correlation Models ◽  
Turbulence Data

The pressure strain correlation plays a critical role in the Reynolds stress transport modeling. Accurate modeling of the pressure strain correlation leads to the proper prediction of turbulence stresses and subsequently the other terms of engineering interest. However, classical pressure strain correlation models are often unreliable for complex turbulent flows. Machine learning–based models have shown promise in turbulence modeling, but their application has been largely restricted to eddy viscosity–based models. In this article, we outline a rationale for the preferential application of machine learning and turbulence data to develop models at the level of Reynolds stress modeling. As an illustration, we develop data-driven models for the pressure strain correlation for turbulent channel flow using neural networks. The input features of the neural networks are chosen using physics-based rationale. The networks are trained with the high-resolution DNS data of turbulent channel flow at different friction Reynolds numbers (Reλ). The testing of the models is performed for unknown flow statistics at other Reλ and also for turbulent plane Couette flows. Based on the results presented in this article, the proposed machine learning framework exhibits considerable promise and may be utilized for the development of accurate Reynolds stress models for flow prediction.

scholarly journals Machine learning-augmented turbulence modeling for RANS simulations of massively separated flows

2021 ◽  
Vol 6 (6) ◽  
Author(s):  
Pedro Stefanin Volpiani ◽  
Morten Meyer ◽  
Lucas Franceschini ◽  
Julien Dandois ◽  
Florent Renac ◽  
...  
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling ◽  
Separated Flows ◽  
Massively Separated Flows ◽  
Rans Simulations

scholarly journals Machine Learning for Turbulence Modeling.

2017 ◽  
Author(s):  
Julia Ling ◽  
Jeremy Templeton
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling

scholarly journals Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework.

2016 ◽  
Author(s):  
Jianxun Wang ◽  
Jinlong Wu ◽  
Julia Ling ◽  
Gianluca Iaccarino ◽  
Heng Xiao
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling ◽  
Complete Framework

The use of the Reynolds force vector in a physics informed machine learning approach for predictive turbulence modeling

2019 ◽  
Vol 192 ◽  
pp. 104258 ◽  
Author(s):  
Matheus A. Cruz ◽  
Roney L. Thompson ◽  
Luiz E.B. Sampaio ◽  
Raphael D.A. Bacchi
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling ◽  
Learning Approach ◽  
Force Vector ◽  
Machine Learning Approach

scholarly journals An iterative machine-learning framework for RANS turbulence modeling

2021 ◽  
Vol 90 ◽  
pp. 108822
Author(s):  
Weishuo Liu ◽  
Jian Fang ◽  
Stefano Rolfo ◽  
Charles Moulinec ◽  
David R Emerson
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling ◽  
Learning Framework

scholarly journals Sapsan: Framework for Supernovae Turbulence Modeling with Machine Learning

2021 ◽  
Vol 6 (67) ◽  
pp. 3199
Author(s):  
Platon Karpov ◽  
Iskandar Sitdikov ◽  
Chengkun Huang ◽  
Chris Fryer
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling

Machine Learning for Turbulence Modeling and Predictions

2020 ◽  
Author(s):  
S. Bhushan ◽  
Greg W. Burgreen ◽  
D. Martinez ◽  
Wes Brewer
Keyword(s):  
Machine Learning ◽  
Turbulence Model ◽  
Turbulence Modeling ◽  
Training Data ◽  
Relevant Parameter ◽  
Arbitrary Initial Condition ◽  
Integral Boundary ◽  
Boundary Layer Equations ◽  
Viable Approach ◽  
Flow Variables

Abstract A stand-alone machine learned turbulence model is applied for the solution of integral boundary layer equations, and issues and constraints associated with the model are discussed. The results demonstrate that grouping flow variables into a problem relevant parameter for input during machine learning is desirable to improve accuracy of the model. Further, the accuracy of the model can be improved significantly by incorporation of physics-based constraints during training. Data driven machine learning training requires trial-and-error approach, shows oscillations in a posteriori predictions, and shows unphysical results when used with arbitrary initial condition, as the query is essentially extrapolations. Physics informed machine learning addresses the above limitations, and is identified to be a viable approach for development of machine learned turbulence model.

Sign in / Sign up

Export Citation Format

Share Document