Predictive RANS simulations via Bayesian model-scenario averaging

DOI10.1016/j.jcp.2014.06.052zbMath1349.76106OpenAlexW2009405650MaRDI QIDQ349418

Mohammad Hasan, M. Dambrine

Publication date: 5 December 2016

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2014.06.052

zbMATH Keywords

error estimationcalibrationBayesian model averaginguncertainty quantificationmodel inadequacyBayesian model-scenario averagingboundary-layersRANS models

Mathematics Subject Classification ID

Bayesian inference (62F15) Statistical turbulence modeling (76F55) Stochastic analysis applied to problems in fluid mechanics (76M35)

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Uses Software

• BayesDA
• Spalart-Allmaras

Cites Work

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• Zero variance Markov chain Monte Carlo for Bayesian estimators
• Bayesian estimates of parameter variability in the \(k-\varepsilon\) turbulence model
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• About Boussinesq's turbulent viscosity hypothesis: historical remarks and a direct evaluation of its validity
• Bayesian Calibration of Computer Models
• A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data
• Comparison of two-equation turbulence models for boundary layers with pressure gradient
• Turbulent Flows
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates