This repository contains scattered codes and utilties that are used in the following papers on Quantifyng and Reducing Model Uncertainties in RANS Smulatoins.
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H. Xiao, J.-L. Wu, J.-X. Wang, R. Sun, and C. J. Roy. Quantifying and reducing model-form uncertainties in Reynolds averaged Navier–Stokes equations: A data-driven, physics-informed Bayesian approach. Journal of Computational Physics, 324, 115-136, 2016. DOI: 10.1016/j.jcp.2016.07.038 Also available at arxiv:1508.06315
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H. Xiao, J.-X. Wang and R. G. Ghanem. A random matrix approach for quantifying model-form uncertainties in turbulence modeling. Computer Methods in Applied Mechanics and Engineering. 313, 941-965, 2017, DOI: 10.1016/j.cma.2016.10.025. Also available at arxiv:1603.09656
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J.-X. Wang, J.-L. Wu, and H. Xiao. Physics informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data. Physical Review Fluids, 2(3), 034603 (21 pages), 2017. DOI: 10.1103/PhysRevFluids.2.034603 Also available at: arxiv: 1606.07987
This repository is not well-organized, but you may find some codes helpful.