Quantification of model uncertainty in RANS simulations: A review

Bayesian Uncertainty Quantification Applied to RANS Turbulence Models

A Bayesian uncertainty quantification approach is developed and applied to RANS turbulence models of fully-developed channel flow. The approach aims to capture uncertainty due to both uncertain parameters and model inadequacy. Parameter uncertainty is represented by treating the parameters of the turbulence model as random variables. To capture model uncertainty, four stochastic extensions of f...

Uncertainty Quantification in Irreversible Electroporation Simulations

One recent area of cancer research is irreversible electroporation (IRE). Irreversible electroporation is a minimally invasive procedure where needle electrodes are inserted into the body to ablate tumor cells with electricity. The aim of this paper is to investigate how uncertainty in tissue and tumor conductivity propagate into final ablation predictions used for treatment planning. Two dimen...

Epistemic uncertainty quantification of RANS modeling for an underexpanded jet in a supersonic cross flow

review of radiographic methods in dental implantology

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Epistemic uncertainty quantification for RANS modeling of the flow over a wavy wall

While Reynolds-averaged Navier-Stokes (RANS) simulations remain the most affordable technique for simulating complex turbulent flows, the inability of linear eddy viscosity models to correctly predict flow separation and reattachment limits the reliability of the simulation outcome. A methodology to quantify the uncertainty in the simulation outcome related to the form of the turbulence model u...