Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

Ling, Julia; Templeton, Jeremy Alan

doi:10.1063/1.4927765

Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

Journal Article · Tue Aug 04 00:00:00 EDT 2015 · Physics of Fluids

DOI:https://doi.org/10.1063/1.4927765· OSTI ID:1235329

Ling, Julia ^[1]; Templeton, Jeremy Alan ^[1]

Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. As a result, feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1235329

Report Number(s):: SAND--2015-2173J; 579403

Journal Information:: Physics of Fluids, Journal Name: Physics of Fluids Journal Issue: 8 Vol. 27; ISSN 1070-6631; ISSN PHFLE6

Publisher:: American Institute of PhysicsCopyright Statement

Country of Publication:: United States

Language:: English

References (47)

Distributed Pasting of Small Votes Chawla, N. V.; Hall, L. O.; Bowyer, K. W. Multiple Classifier Systems https://doi.org/10.1007/3-540-45428-4_5	book	January 2002
A Comparison of Ensemble Creation Techniques Banfield, Robert E.; Hall, Lawrence O.; Bowyer, Kevin W. Multiple Classifier Systems https://doi.org/10.1007/978-3-540-25966-4_22	book	January 2004
Support-vector networks Cortes, Corinna; Vapnik, Vladimir Machine Learning, Vol. 20, Issue 3 https://doi.org/10.1007/bf00994018	journal	September 1995
Development and application of a cubic eddy-viscosity model of turbulence Craft, T. J.; Launder, B. E.; Suga, K. International Journal of Heat and Fluid Flow, Vol. 17, Issue 2 https://doi.org/10.1016/0142-727X(95)00079-6	journal	April 1996
A numerical study of scalar dispersion downstream of a wall-mounted cube using direct simulations and algebraic flux models Rossi, R.; Philips, D. A.; Iaccarino, G. International Journal of Heat and Fluid Flow, Vol. 31, Issue 5 https://doi.org/10.1016/j.ijheatfluidflow.2010.05.006	journal	October 2010
Numerical analysis and modeling of plume meandering in passive scalar dispersion downstream of a wall-mounted cube Rossi, R.; Iaccarino, G. International Journal of Heat and Fluid Flow, Vol. 43 https://doi.org/10.1016/j.ijheatfluidflow.2013.04.006	journal	October 2013
Turbulent transport in an inclined jet in crossflow Coletti, F.; Benson, M. J.; Ling, J. International Journal of Heat and Fluid Flow, Vol. 43 https://doi.org/10.1016/j.ijheatfluidflow.2013.06.001	journal	October 2013
A numerical study of algebraic flux models for heat and mass transport simulation in complex flows Rossi, Riccardo International Journal of Heat and Mass Transfer, Vol. 53, Issue 21-22 https://doi.org/10.1016/j.ijheatmasstransfer.2010.06.042	journal	October 2010
Bayesian estimates of parameter variability in the k–ε turbulence model Edeling, W. N.; Cinnella, P.; Dwight, R. P. Journal of Computational Physics, Vol. 258 https://doi.org/10.1016/j.jcp.2013.10.027	journal	February 2014
Bayesian uncertainty analysis with applications to turbulence modeling Cheung, Sai Hung; Oliver, Todd A.; Prudencio, Ernesto E. Reliability Engineering & System Safety, Vol. 96, Issue 9 https://doi.org/10.1016/j.ress.2010.09.013	journal	September 2011
Turbulent Flows Pope, Stephen B. https://doi.org/10.1017/CBO9780511840531	book	July 2012
The return to isotropy of homogeneous turbulence Choi, Kwing-So; Lumley, John L. Journal of Fluid Mechanics, Vol. 436 https://doi.org/10.1017/S002211200100386X	journal	June 2001
On the non-local geometry of turbulence Bermejo-Moreno, IvÁN; Pullin, D. I. Journal of Fluid Mechanics, Vol. 603 https://doi.org/10.1017/S002211200800092X	journal	April 2008
Reynolds number dependence of mean flow structure in square duct turbulence Pinelli, Alfredo; Uhlmann, Markus; Sekimoto, Atsushi Journal of Fluid Mechanics, Vol. 644 https://doi.org/10.1017/S0022112009992242	journal	February 2010
Progress in the development of a Reynolds-stress turbulence closure Launder, B. E.; Reece, G. J.; Rodi, W. Journal of Fluid Mechanics, Vol. 68, Issue 3 https://doi.org/10.1017/S0022112075001814	journal	April 1975
A more general effective-viscosity hypothesis Pope, S. B. Journal of Fluid Mechanics, Vol. 72, Issue 02 https://doi.org/10.1017/S0022112075003382	journal	November 1975
On explicit algebraic stress models for complex turbulent flows Gatski, T. B.; Speziale, C. G. Journal of Fluid Mechanics, Vol. 254 https://doi.org/10.1017/S0022112093002034	journal	September 1993
An explicit algebraic Reynolds stress model for incompressible and compressible turbulent flows Wallin, Stefan; Johansson, Arne V. Journal of Fluid Mechanics, Vol. 403 https://doi.org/10.1017/S0022112099007004	journal	January 2000
The return to isotropy of homogeneous turbulence Lumley, John L.; Newman, Gary R. Journal of Fluid Mechanics, Vol. 82, Issue 1 https://doi.org/10.1017/s0022112077000585	journal	August 1977
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling Svetnik, Vladimir; Liaw, Andy; Tong, Christopher Journal of Chemical Information and Computer Sciences, Vol. 43, Issue 6 https://doi.org/10.1021/ci034160g	journal	November 2003
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides Meher, Prabina Kumar; Satpathy, Subhrajit; Rao, Atmakuri Ramakrishna Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-71381-4	journal	September 2020
Flow topologies and turbulence scales in a jet-in-cross-flow Ruiz, A. M.; Lacaze, G.; Oefelein, J. C. Physics of Fluids, Vol. 27, Issue 4 https://doi.org/10.1063/1.4915065	journal	April 2015
On predicting the turbulence‐induced secondary flows using nonlinear k ‐ε models Mompean, G.; Gavrilakis, S.; Machiels, L. Physics of Fluids, Vol. 8, Issue 7 https://doi.org/10.1063/1.868968	journal	July 1996
Direct numerical simulation of turbulent channel flow up to Reτ=590 Moser, Robert D.; Kim, John; Mansour, Nagi N. Physics of Fluids, Vol. 11, Issue 4 https://doi.org/10.1063/1.869966	journal	April 1999
Turbulent Flows Pope, Stephen B. Measurement Science and Technology, Vol. 12, Issue 11 https://doi.org/10.1088/0957-0233/12/11/705	journal	October 2001
Bayesian uncertainty quantification applied to RANS turbulence models Oliver, Todd A.; Moser, Robert D. Journal of Physics: Conference Series, Vol. 318, Issue 4 https://doi.org/10.1088/1742-6596/318/4/042032	journal	December 2011
Combination of multiple classifiers using local accuracy estimates Woods, K.; Kegelmeyer, W. P.; Bowyer, K. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, Issue 4 https://doi.org/10.1109/34.588027	journal	April 1997
Combination of multiple classifiers using local accuracy estimates Woods, K.; Bowyer, K.; Kegelmeyer, W. P. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.1996.517102	conference	January 1996
Creating ensembles of classifiers Chawla, N.; Eschrich, S.; Hall, L. O. Proceedings 2001 IEEE International Conference on Data Mining https://doi.org/10.1109/ICDM.2001.989568	conference	January 2001
Near Wall Modeling for Trailing Edge Slot Film Cooling Ling, Julia; Rossi, Riccardo; Eaton, John K. Journal of Fluids Engineering, Vol. 137, Issue 2 https://doi.org/10.1115/1.4028498	journal	September 2014
Analysis of Turbulent Scalar Flux Models for a Discrete Hole Film Cooling Flow Ling, Julia; Ryan, Kevin J.; Bodart, Julien Journal of Turbomachinery, Vol. 138, Issue 1 https://doi.org/10.1115/1.4031698	journal	October 2015
Analysis of Turbulent Scalar Flux Models for a Discrete Hole Film Cooling Flow Ling, Julia; Ryan, Kevin J.; Bodart, Julien ASME Turbo Expo 2015: Turbine Technical Conference and Exposition, Volume 5B: Heat Transfer https://doi.org/10.1115/GT2015-42092	conference	August 2015
Diagnosing extrapolation: tree-based density estimation Hooker, Giles Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04 https://doi.org/10.1145/1014052.1014121	conference	January 2004
RANS modeling of turbulent mixing for a jet in supersonic cross flow: model evaluation and uncertainty quantification Gorle, C.; Emory, M.; Iaccarino, Gianluca Proceeding of THMT-12. Proceedings of the Seventh International Symposium On Turbulence, Heat and Mass Transfer Palermo, Italy, 24-27 September, 2012 https://doi.org/10.1615/ICHMT.2012.ProcSevIntSympTurbHeatTransfPal.1830	conference	January 2012
Tuning a RANS k-e model for jet-in-crossflow simulations Lefantzi, Sophia; Ray, Jaideep; Arunajatesan, Srinivasan https://doi.org/10.2172/1096265	report	September 2013
Estimation of k-ε parameters using surrogate models and jet-in-crossflow data Lefantzi, Sophia; Ray, Jaideep; Arunajatesan, Srinivasan https://doi.org/10.2172/1170402	report	November 2014
Turbulent channel and Couette flows using an anisotropic k-epsilon model Nisizima, Shoiti; Yoshizawa, Akira AIAA Journal, Vol. 25, Issue 3 https://doi.org/10.2514/3.9639	journal	March 1987
SIERRA/Fuego: A Multi-Mechanics Fire Environment Simulation Tool Domino, Stefan; Moen, Chris; Burns, Shawn 41st Aerospace Sciences Meeting and Exhibit https://doi.org/10.2514/6.2003-149	conference	November 2012
Quantification of Structural Uncertainties in the k -w Turbulence Model Dow, Eric; Wang, Qiqi 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference https://doi.org/10.2514/6.2011-1762	conference	June 2012
Uncertainty Quantification of Structural Uncertainties in RANS Simulations of Complex Flows Dow, Eric; Wang, Qiqi 20th AIAA Computational Fluid Dynamics Conference https://doi.org/10.2514/6.2011-3865	conference	June 2012
Validation of an FSI Modeling Framework for Internal Captive Carriage Applications Arunajatesan, Srinivasan; Ross, Michael R.; Garrett, Tyler J. 19th AIAA/CEAS Aeroacoustics Conference https://doi.org/10.2514/6.2013-2157	conference	May 2013
Application of Supervised Learning to Quantify Uncertainties in Turbulence and Combustion Modeling Tracey, Brendan; Duraisamy, Karthik; Alonso, Juan 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition https://doi.org/10.2514/6.2013-259	conference	January 2013
Bayesian calibration of a k-ε turbulence model for predictive jet-in-crossflow simulations Ray, Jaideep; Lefantzi, Sophia; Arunajatesan, Srinivasan 44th AIAA Fluid Dynamics Conference https://doi.org/10.2514/6.2014-2085	conference	June 2014
New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques Duraisamy, Karthikeyan; Zhang, Ze J.; Singh, Anand Pratap 53rd AIAA Aerospace Sciences Meeting https://doi.org/10.2514/6.2015-1284	conference	January 2015
A Machine Learning Strategy to Assist Turbulence Model Development Tracey, Brendan D.; Duraisamy, Karthikeyan; Alonso, Juan J. 53rd AIAA Aerospace Sciences Meeting https://doi.org/10.2514/6.2015-1287	conference	January 2015
Anisotropy analysis of turbulent swirl flow Radenkovic, Darko; Burazer, Jela; Novkovic, Djordje FME Transaction, Vol. 42, Issue 1 https://doi.org/10.5937/fmet1401019R	journal	January 2014

Cited By (41)

Hydrodynamic object identification with artificial neural models Lakkam, Sreetej; T., Balamurali B.; Bouffanais, Roland arXiv https://doi.org/10.48550/arxiv.1903.00828	text	January 2019
Uncertainty and global sensitivity analysis of bladed disk statics with material anisotropy and root geometry variations Rajasekharan, Rahul; Petrov, Evgeny Engineering Reports, Vol. 1, Issue 3 https://doi.org/10.1002/eng2.12043	journal	October 2019
Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning Wang, Jian-Xun; Huang, Junji; Duan, Lian Theoretical and Computational Fluid Dynamics, Vol. 33, Issue 1 https://doi.org/10.1007/s00162-018-0480-2	journal	December 2018
A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling Wu, Jin-Long; Wang, Jian-Xun; Xiao, Heng Flow, Turbulence and Combustion, Vol. 99, Issue 1 https://doi.org/10.1007/s10494-017-9807-0	journal	March 2017
Zonal Eddy Viscosity Models Based on Machine Learning Matai, R.; Durbin, P. A. Flow, Turbulence and Combustion, Vol. 103, Issue 1 https://doi.org/10.1007/s10494-019-00011-5	journal	February 2019
The Current State of High-Fidelity Simulations for Main Gas Path Turbomachinery Components and Their Industrial Impact Sandberg, Richard D.; Michelassi, Vittorio Flow, Turbulence and Combustion, Vol. 102, Issue 4 https://doi.org/10.1007/s10494-019-00013-3	journal	February 2019
Progress Variable Variance and Filtered Rate Modelling Using Convolutional Neural Networks and Flamelet Methods Nikolaou, Z. M.; Chrysostomou, C.; Vervisch, L. Flow, Turbulence and Combustion, Vol. 103, Issue 2 https://doi.org/10.1007/s10494-019-00028-w	journal	April 2019
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance Ling, Julia; Kurzawski, Andrew; Templeton, Jeremy Journal of Fluid Mechanics, Vol. 807 https://doi.org/10.1017/jfm.2016.615	journal	October 2016
A neural network approach for the blind deconvolution of turbulent flows Maulik, R.; San, O. Journal of Fluid Mechanics, Vol. 831 https://doi.org/10.1017/jfm.2017.637	journal	October 2017
Subgrid modelling for two-dimensional turbulence using neural networks Maulik, R.; San, O.; Rasheed, A. Journal of Fluid Mechanics, Vol. 858 https://doi.org/10.1017/jfm.2018.770	journal	November 2018
Deep learning of vortex-induced vibrations Raissi, Maziar; Wang, Zhicheng; Triantafyllou, Michael S. Journal of Fluid Mechanics, Vol. 861 https://doi.org/10.1017/jfm.2018.872	journal	December 2018
Sub-grid scale model classification and blending through deep learning Maulik, Romit; San, Omer; Jacob, Jamey D. Journal of Fluid Mechanics, Vol. 870 https://doi.org/10.1017/jfm.2019.254	journal	May 2019
Construction of reduced-order models for fluid flows using deep feedforward neural networks Lui, Hugo F. S.; Wolf, William R. Journal of Fluid Mechanics, Vol. 872 https://doi.org/10.1017/jfm.2019.358	journal	June 2019
Prediction of turbulent heat transfer using convolutional neural networks Kim, Junhyuk; Lee, Changhoon Journal of Fluid Mechanics, Vol. 882 https://doi.org/10.1017/jfm.2019.814	journal	November 2019
Hydrodynamic object identification with artificial neural models Lakkam, Sreetej; Balamurali, B. T.; Bouffanais, Roland Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-47747-8	journal	August 2019
Using field inversion to quantify functional errors in turbulence closures Singh, Anand Pratap; Duraisamy, Karthik Physics of Fluids, Vol. 28, Issue 4 https://doi.org/10.1063/1.4947045	journal	April 2016
Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder Jin, Xiaowei; Cheng, Peng; Chen, Wen-Li Physics of Fluids, Vol. 30, Issue 4 https://doi.org/10.1063/1.5024595	journal	April 2018
Machine learning methods for turbulence modeling in subsonic flows around airfoils Zhu, Linyang; Zhang, Weiwei; Kou, Jiaqing Physics of Fluids, Vol. 31, Issue 1 https://doi.org/10.1063/1.5061693	journal	January 2019
Fast flow field prediction over airfoils using deep learning approach Sekar, Vinothkumar; Khoo, Boo Cheong Physics of Fluids, Vol. 31, Issue 5 https://doi.org/10.1063/1.5094943	journal	May 2019
Application of two-branch deep neural network to predict bubble migration near elastic boundaries Ma, Xiaojian; Wang, Chen; Huang, Biao Physics of Fluids, Vol. 31, Issue 10 https://doi.org/10.1063/1.5111620	journal	October 2019
Direct Numerical Simulation of Turbulent Flow Inside a Differentially Heated Composite Cavity Martínez, Javier; Merzari, Elia; Acton, Michael Nuclear Technology, Vol. 206, Issue 2 https://doi.org/10.1080/00295450.2019.1595312	journal	May 2019
Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach Chang, Chih-Wei; Fang, Jun; Dinh, Nam T. Nuclear Science and Engineering, Vol. 194, Issue 8-9 https://doi.org/10.1080/00295639.2020.1712928	journal	February 2020
Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence Xie, Chenyue; Wang, Jianchun; Li, Ke Physical Review E, Vol. 99, Issue 5 https://doi.org/10.1103/physreve.99.053113	journal	May 2019
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data Wang, Jian-Xun; Wu, Jin-Long; Xiao, Heng Physical Review Fluids, Vol. 2, Issue 3 https://doi.org/10.1103/physrevfluids.2.034603	journal	March 2017
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework Wu, Jin-Long; Xiao, Heng; Paterson, Eric Physical Review Fluids, Vol. 3, Issue 7 https://doi.org/10.1103/physrevfluids.3.074602	journal	July 2018
Perspective on machine learning for advancing fluid mechanics Brenner, M. P.; Eldredge, J. D.; Freund, J. B. Physical Review Fluids, Vol. 4, Issue 10 https://doi.org/10.1103/physrevfluids.4.100501	journal	October 2019
Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network Xie, Chenyue; Li, Ke; Ma, Chao Physical Review Fluids, Vol. 4, Issue 10 https://doi.org/10.1103/physrevfluids.4.104605	journal	October 2019
A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows Milani, Pedro M.; Ling, Julia; Saez-Mischlich, Gonzalo Journal of Turbomachinery, Vol. 140, Issue 2 https://doi.org/10.1115/1.4038275	journal	December 2017
A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows Milani, Pedro M.; Ling, Julia; Saez-Mischlich, Gonzalo ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition, Volume 5A: Heat Transfer https://doi.org/10.1115/gt2017-63299	conference	August 2017
Deep Learning of Vortex Induced Vibrations Raissi, Maziar; Wang, Zhicheng; Triantafyllou, Michael engrXiv https://doi.org/10.17605/osf.io/fnwjy	text	January 2018
Machine-Learning-Augmented Predictive Modeling of Turbulent Separated Flows over Airfoils Singh, Anand Pratap; Medida, Shivaji; Duraisamy, Karthik AIAA Journal, Vol. 55, Issue 7 https://doi.org/10.2514/1.j055595	journal	July 2017
Kernel-Smoothed Proper Orthogonal Decomposition–Based Emulation for Spatiotemporally Evolving Flow Dynamics Prediction Chang, Yu-Hung; Zhang, Liwei; Wang, Xingjian AIAA Journal, Vol. 57, Issue 12 https://doi.org/10.2514/1.j057803	journal	December 2019
Modal Analysis of Fluid Flows: Applications and Outlook Taira, Kunihiko; Hemati, Maziar S.; Brunton, Steven L. AIAA Journal, Vol. 58, Issue 3 https://doi.org/10.2514/1.j058462	journal	March 2020
Future Directions in Computational Fluid Dynamics Witherden, Freddie D.; Jameson, Antony 23rd AIAA Computational Fluid Dynamics Conference https://doi.org/10.2514/6.2017-3791	conference	June 2017
Towards Integrated Field Inversion and Machine Learning With Embedded Neural Networks for RANS Modeling Holland, Jonathan R.; Baeder, James D.; Duraisamy, Karthik AIAA Scitech 2019 Forum https://doi.org/10.2514/6.2019-1884	conference	January 2019
A neural network approach for the blind deconvolution of turbulent flows Maulik, Romit; San, Omer arXiv https://doi.org/10.48550/arxiv.1706.00912	text	January 2017
Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework Wu, Jin-Long; Xiao, Heng; Paterson, Eric arXiv https://doi.org/10.48550/arxiv.1801.02762	text	January 2018
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning Wang, Jian-Xun; Huang, Junji; Duan, Lian arXiv https://doi.org/10.48550/arxiv.1808.07752	text	January 2018
Sub-grid scale model classification and blending through deep learning Maulik, Romit; San, Omer; Jacob, Jamey D. arXiv https://doi.org/10.48550/arxiv.1812.11949	text	January 2018
Construction of Reduced Order Models for Fluid Flows Using Deep Feedforward Neural Networks Lui, Hugo F. S.; Wolf, William R. arXiv https://doi.org/10.48550/arxiv.1903.05206	text	January 2019
Modal Analysis of Fluid Flows: Applications and Outlook Taira, Kunihiko; Hemati, Maziar S.; Brunton, Steven L. arXiv https://doi.org/10.48550/arxiv.1903.05750	preprint	January 2019

Similar Records

Adaptive downstream tensorial eddy viscosity for hybrid large‐eddy simulations‐Reynolds‐averaged‐Navier–Stokes simulations

Journal Article · Mon Feb 01 23:00:00 EST 2021 · International Journal for Numerical Methods in Fluids · OSTI ID:1804595

Data-Driven Machine Learning for Wind Plant Flow Modeling

Journal Article · Tue Jun 19 00:00:00 EDT 2018 · Journal of Physics. Conference Series · OSTI ID:1463077

Simulation of Wingtip Vortex Flows with Reynolds-Averaged Navier–Stokes and Scale-Resolving Simulation Methods

Journal Article · Thu Jan 10 23:00:00 EST 2019 · AIAA Journal · OSTI ID:1529537

Related Subjects

97 MATHEMATICS AND COMPUTING
Eddy viscosity closure
Navier Stokes equations
Reynolds stress modeling
databases
viscosity

Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

Citation Formats

References (47)

Cited By (41)

Similar Records

Related Subjects