Quantifying turbulence model uncertainty in Reynolds-averaged Navier-Stokes simulations of a pin-fin array. I: Flow field

DOI10.1016/j.compfluid.2020.104641OpenAlexW3037195034MaRDI QIDQ2019928

Catherine Gorlé, Zengrong Hao

Publication date: 22 April 2021

Published in: Computers and Fluids (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.compfluid.2020.104641

zbMATH Keywords

turbulence modelinguncertainty quantificationrealizability constraintspin-fin array

Mathematics Subject Classification ID

Fluid mechanics (76-XX)

Cites Work

• Bayesian estimates of parameter variability in the \(k-\varepsilon\) turbulence model
• Predictive RANS simulations via Bayesian model-scenario averaging
• Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach
• A random matrix approach for quantifying model-form uncertainties in turbulence modeling
• Evaluation of the unsteady RANS capabilities for separated flows control
• Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches
• Review of the shear-stress transport turbulence model experience from an industrial perspective
• Some Recent Developments in Turbulence Closure Modeling

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