Data-driven turbulence modeling

1. Srivastava, V. & Duraisamy K. Generalizable Physics-constrained Modeling using Learning and Inference assisted by Feature Space Engineering, Preprint (Submitted for publication), 2021
2. Matai, R. & Durbin, P.A. Zonal Eddy Viscosity Models Based on Machine Learning, Flow, Turbulence and Combustion, Vol. 103, Issue 1 2019
3. Holland, J.R. & Baeder, J.D. & Duraisamy, K. Field Inversion and Machine Learning With Embedded Neural Networks: Physics-Consistent Neural Network Training, Proc. AIAA Aviation, Dallas, TX 2019
4. Holland, J.R. & Baeder, J.D. & Duraisamy, K. Towards Integrated Field Inversion and Machine Learning With Embedded Neural Networks for RANS Modeling, Proc. AIAA SciTech, San Diego, CA 2019
5. Singh, A.P. & Matai, R. & Duraisamy, K. & Durbin, P. A. Data-driven augmentation of turbulence models for adverse pressure gradient flows, Proc. AIAA Aviation, Denver, CO 2017
6. Singh, A.P. & Medida, S. & Duraisamy, K. Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils, AIAA Journal, Vol. 55, No. 7 (2017), pp. 2215-2227. 2017
7. Duraisamy, K. & Singh, A.P. & Pan, S. Augmentation of Turbulence Models Using Field Inversion and Machine Learning, Proc. AIAA SciTech, Grapevine, TX 2017
8. Singh, A.P. & Pan, S. & Duraisamy, K. Characterizing and Improving Predictive Accuracy in Shock-Turbulent Boundary Layer Interactions Using Data-driven Models, Proc. AIAA SciTech, Grapevine, TX 2017
9. Zhang, Z. & Duraisamy, K. & Gumerov, N. Efficient Multiscale Gaussian Process Regression using Hierarchical Clustering, 2016
10. Singh, A.P. & Duraisamy, K. Using Field Inversion to Quantify Functional Errors in Turbulence Closures, Phys. Fluids 28, 045110 2016
11. Parish, E. & Duraisamy, K., A paradigm for data-driven predictive modeling using field inversion and machine learning, Journal of Computational Physics, Volume 305, 15 January 2016, Pages 758–774 2016
12. Duraisamy, K. & Singh, A.P., Informing Turbulence Closures With Computational and Experimental Data, Proc. AIAA SciTech, San Diego, CA 2016
13. Zhang, Z.J. & Duraisamy, K., Machine Learning Methods for Data-Driven Turbulence Modeling, Proc. AIAA Aviation, Dallas, TX 2015
14. Parish, E. & Duraisamy, K., Quantification of Turbulence Modeling Uncertainties Using Full Field Inversion, Proc. AIAA Aviation, Dallas, TX 2015
15. Tracey, B. & Duraisamy, K. & Alonso, Juan J. A Machine Learning Strategy to Assist Turbulence Model Development, Proc. AIAA SciTech, Kissimmee, FL 2015
16. Duraisamy, K. & Zhang, Z.J. & Singh, A.P., New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques, Proc. AIAA SciTech, Kissimmee, FL 2015
17. Duraisamy, K. & Durbin, P.A., Transition modeling using data driven approaches, Center of Turbulence Research, Proceedings of the Summer Program 2014
18. Tracey, B. & Duraisamy, K. & Alonso, J. Application of supervised learning to quantify uncertainties in turbulence and combustion modeling, AIAA Aerospace Sciences Meeting 2013

Structure-based turbulence modeling

19. Mishra A.A. & Duraisamy, K. & Iaccarino, G. Estimating uncertainty in homogeneous turbulence evolution due to coarse-graining, Physics of Fluids, Volume 31, Issue 2 2019
20. Campos, A. & Duraisamy, K. & Iaccarino, G., Eulerian formulation of the interacting particle representation model of homogeneous turbulence, Physical Review Fluids 2016
21. Campos, A. & Duraisamy, K. & Iaccarino, G., A segregated explicit algebraic structure-based model for wall-bounded turbulent flows, International Journal of Heat and Fluid Flow 2016
22. Mishra, A. & Iaccarino, G. & Duraisamy, K., Sensitivity of flow evolution on turbulence structure, Physical Review Fluids 2016
23. Campos, A. & Duraisamy, K. & Iaccarino, G., Towards a Two-Equation Algebraic Structure-Based Model with Applications to Turbulent Separated Flows, 21st AIAA Fluid Dynamics Conference 2013
24. Pecnik, R. & Kassinos, S. & Duraisamy, K. & Iaccarino, G. Towards an accurate and robust Algebraic Structure Based Model, Proc. CTR Summer Program 2012

Hybrid RANS-LES modeling

25. Durbin, P.A. & Yin, Z. & Jeyapaul, E. Adaptive Detached Eddy Simulation of Three-Dimensional Diffusers , Journal of Fluids Engineering, 2016
26. Yin, Z. & Durbin, P.A. An adaptive DES model that allows wall-resolved eddy simulation , International Journal of Heat and Fluid Flow, 2016
27. Yin, Z. & Durbin, P.A. Passive Scalar Transport Modeling for Hybrid RANS/LES Simulation, Flow, Turbulence and Combustion pp 1-18, 2016

Subgrid scale closures

28. Gouasmi, A. & Parish, E. & Duraisamy, K. Characterizing Memory Effects In Coarse-Grained Nonlinear Systems Using The Mori-Zwanzig formalism, Under revision, Proc. Roy. Soc. Ser A., 2017
29. Parish, E. & Duraisamy, K. Non-Markovian Closure Models for Large Eddy Simulations using the Mori-Zwanzig Formalism, Physical Review Fluids, 2017
30. Parish, E. & Duraisamy, K. A Dynamic Subgrid Scale Model for Large Eddy Simulations Based on the Mori-Zwanzig Formalism, Journal of Computational Physics, 2017

Turbulence Physics & Applications

31. Matai, R. & Durbin, P.A. Large-eddy simulation of turbulent flow over a parametric set of bumps, J. Fluid Mech., Vol. 866 2019
32. Ryu, S. & Emory, M. & Iaccarino, G. & Campos, A. & Duraisamy, K. Large-Eddy Simulation of a Wing–Body Junction Flow AIAA Journal 2016
33. Morgan, B. & Duraisamy, K. & Lele, S.K. Large-eddy simulations of a normal shock train in a constant-area isolator AIAA Journal 2014
34. Morgan, B. & Duraisamy, K. & Nguyen, N. & Kawai, S. & Lele, S.K. Flow physics and RANS modelling of oblique shock/turbulent boundary layer interaction Journal of Fluid Mechanics 2013
35. Morgan, B. & Duraisamy, K. & SK Lele Large-eddy and RANS simulations of a normal shock train in a constant-area isolator Proc. 50th AIAA Aerospace Sciences Meeting 2012
36. Aranake, A.C. & Lakshminarayan, V.K. & Duraisamy, K. Assessment of transition model and CFD methodology for wind turbine flows Proc. 42nd AIAA Fluid Dynamics Conference 2012
37. Duraisamy, K. & Lele, S.K. Evolution of isolated turbulent trailing vortices Physics of Fluids 2008
38. Duraisamy, K. & Lele, S.K. Turbulent transport in isolated trailing vortices Proc. 5th Turbulence and Shear Flow Phenomena 2007
39. Revell, A. & Duraisamy, K. & Iaccarino, G. Advanced turbulence modelling of wingtip vortices Proc. 5th Turbulence and Shear Flow Phenomena 2007
40. Duraisamy, K. & Iaccarino, G. Curvature Correction and Application of the v2-f Turbulence Model to Tip Vortex Flows Center for Turbulence Research, Annual Research Briefs 2005

Review/Commentary

1. Duraisamy, K. Perspectives on Machine Learning-augmented Reynolds-averaged and Large Eddy Simulation Models of Turbulence, Preprint (Accepted in Phys. Rev. Fluids), 2021
2. Bush, R.H. & Chyczewski, T. & Duraisamy, K. & Eisfeld, B. & Rumsey, C.L. & Smith, B.R. Recommendations for Future Efforts in RANS Modeling and Simulation, Proc. AIAA Scitech 2019
3. Duraisamy, K. & Iaccarino, G. & Xiao. H Turbulence Modeling in the Age of Data, Annual Review of Fluid Mechanics, Volume 51 2019
4. Duraisamy, K. & Spalart, P.R. & Rumsey, C.L. Status, Emerging Ideas and Future Directions of Turbulence Modeling Research in Aeronautics, NASA Technical Report 2017
5. Durbin, P. A. Some Recent Developments in Turbulence Closure Modeling, Annual Review of Fluid Mechanics, Vol 50, 2017
6. Duraisamy, K. Data-enabled, Physics-constrained Predictive Modeling of Complex Systems, SIAM News, July/August 2017