Bayesian uncertainty quantification and reduction in turbulence model

  • RANS model-form uncertainty estimation

    Random matrix, Max-entropy theory; Physics-based perturbation; Multi-model uncertainty propagation

  1. H. Xiao, J.-X. Wang and Roger 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. [Arxiv, DOI, bib]
  2. J.-X. Wang, C. J. Roy and H. Xiao. Propagation of Input Uncertainty in Presence of Model-Form Uncertainty: A Multi-fidelity Approach for CFD Applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 4 (1), 01100, 2017.  [Arxiv, DOI, bib]
  3. J.-X. Wang, R. Sun, H. Xiao. Quantification of uncertainty in RANS models: a comparison of physics-based and random matrix theoretic approaches.  International Journal of Heat and Fluid Flow, 62 (B): 577-592, 2016. [Arxiv, DOI, bib]

  • Bayesian RANS model-form uncertainty reduction

  1. 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. [Arxiv, DOI, bib]
  2. J.-X. Wang, J.-L. Wu, and H. Xiao. Incorporating prior knowledge for quantifying and reducing model-form uncertainty in RANS simulations. International Journal of Uncertainty Quantification, 6 (2): 109-126, 2016. [Arxiv, DOI, bib]
  3. J.-L. Wu, J.-X. Wang, and H. Xiao. A Bayesian calibration-prediction method for reducing model-form uncertainties with application in RANS simulations. Flow, Turbulence and Combustion, 97, 761-786, 2016. [Arxiv, DOI, bib]