kulkarnisp/machine_learning_fluid_dynamics

A curated list of awesome Machine Learning projects in Fluid Dynamics

Machine Learning in Fluid Dynamics

A curated list of awesome Machine Learning projects in Fluid Dynamics

Overview

• Review
• Turbulence Closure
• Flow Field Approximation
• Particle Image Velocimetry
• Others
• Mutliphase flow

Review

• [Deep learning in fluid dynamics] Kutz 2017 JFM, [link]
• [Turbulence modeling in the age of data] K Duraisamy, G Iaccarino, H Xiao - Annual Review of Fluid 2018, [link]

Turbulence Closure

• [Reynolds averaged turbulence modelling using deep neural networks with embedded invariance] J Ling, A Kurzawski, J Templeton - Journal of Fluid Mechanics, 2016 [link]

• [Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty] J Ling, J Templeton - Physics of Fluids, 2015, [link]

• [Machine learning strategies for systems with invariance properties] J Ling, R Jones, J Templeton - Journal of Computational Physics, 2016 [link]

• [A neural network approach for the blind deconvolution of turbulent flows] R Maulik, O San - Journal of Fluid Mechanics, 2017 [link]

• [Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data ] JX Wang, JL Wu, H Xiao - Physical Review Fluids, 2017 [link]

• [Reynolds-Averaged Turbulence Modeling Using Type I and Type II Machine Learning Frameworks with Deep Learning] CW Chang, NT Dinh 2018 [link]

• [Machine learning methods for data-driven turbulence modeling] - Zhang K Duraisamy 2015 [link]

• [Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils] AP Singh, S Medida, K Duraisamy - AIAA Journal, 2017 [link]

• [Deep Neural Networks for Data-Driven Turbulence Models] AD Beck, DG Flad, CD Munz 2018 [link]

• [Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures] A. Vollant, G. Balarac & C. Corre[link]

Flow Field Approximation

• [Convolutional neural networks for steady flow approximation] -Guo, X., Li, W., & Iorio, F. 2016 KDD [link]

• [Data‐driven projection method in fluid simulation] -C Yang, X Yang, X Xiao - Computer Animation and Virtual, 2016 [link]

• [Accelerating eulerian fluid simulation with convolutional networks] J Tompson, K Schlachter, P Sprechmann, 2016 - [link]

• [Well, how accurate is it? A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations] N Thuerey, K Weissenow, H Mehrotra 2018 [link]

Particle Image Velocimetry

• [Machine Learning Control for Experimental Turbulent Flow Targeting the Reduction of a Recirculation Bubble] Camila Chovet, Marc Lippert, Laurent Keirsbulck, Bernd R. Noack and Jean-Marc Foucaut 2017 [link]

• [PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry] Y Lee, H Yang, Z Yin - Experiments in Fluids, 2017 [link]

Others

Lift and drag prediction

• [Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient] Yao Zhang, Woong-Je Sung, Dimitri Mavris 2017, [link]

• [Identification of aerodynamic coefficients using computational neural networks] DJ Linse, RF Stengel - Journal of Guidance, Control, and Dynamics, 1993, [link]

• [Performance predicting of 2D and 3D submerged hydrofoils using CFD and ANNs], H Nowruzi, H Ghassemi, M Ghiasi - Journal of Marine Science and …, 2017, [link]

Flow Control

• [Closed-loop separation control using machine learning] N Gautier, JL Aider, T Duriez, BR Noack… - Journal of Fluid Mechanics 2015 [link]

Multiphase

[Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system] M Ma, J Lu, G Tryggvason - Physics of Fluids, 2015 [link]

-[Sharp interface approaches and deep learning techniques for multiphase flows] F Gibou, D Hyde, R Fedkiw - Journal of Computational Physics, 2019 [link]