INCORPORATING PRIOR KNOWLEDGE FOR QUANTIFYING AND REDUCING MODEL-FORM UNCERTAINTY IN RANS SIMULATIONS

DOI10.1615/Int.J.UncertaintyQuantification.2016015984zbMath1498.76048arXiv1512.01750MaRDI QIDQ5052270

Heng Xiao, Jin-Long Wu, Jian-Xun Wang

Publication date: 24 November 2022

Published in: International Journal for Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1512.01750

Mathematics Subject Classification ID

Statistical turbulence modeling (76F55) Direct numerical and large eddy simulation of turbulence (76F65)

Related Items (4)

A bi-fidelity ensemble Kalman method for PDE-constrained inverse problems in computational mechanics ⋮ Determining \textit{a priori} a RANS model's applicable range via global epistemic uncertainty quantification ⋮ A random matrix approach for quantifying model-form uncertainties in turbulence modeling ⋮ Physics-informed covariance kernel for model-form uncertainty quantification with application to turbulent flows

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