Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach
type: Item
instance of: scholarly article
title: Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach
zbMATH Open document ID: 1371.76082
published in: Journal of Computational Physics
publication date: 2017-05-08T00:00:00Z
full work available at URL: https://arxiv.org/abs/1508.06315
Mathematics Subject Classification ID: 76F55
Mathematics Subject Classification ID: 62F15
zbMATH DE Number: 6712222
zbMATH Keywords: uncertainty quantification
zbMATH Keywords: ensemble Kalman filtering
zbMATH Keywords: turbulence modeling
zbMATH Keywords: Reynolds-averaged Navier-Stokes equations
author: Heng Xiao
author: Jin-Long Wu
author: Jian-Xun Wang
author: Rui Sun
author: Christopher J. Roy
describes a project that uses: UQTk
describes a project that uses: EnKF
MaRDI profile type: Publication
OpenAlex ID: W2490045648
arXiv ID: 1508.06315
cites work: Q4296871
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DOI: 10.1016/J.JCP.2016.07.038
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description: scientific article