Calibration of Large Eddy Simulation parameters using Approximate Bayesian Computation
Realitzat a/ambUniversity of Colorado Boulder
Tipus de documentProjecte Final de Màster Oficial
Data2022-10-20
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Approximate Bayesian Computation (ABC) method is used to estimate posterior distributions of model parameters in subgrid-scale (SGS) closures for large eddy simulations (LES) of turbulent flows. The ABC approach avoids the need to directly compute a likelihood function during the parameter estimation, enabling a substantial speed-up and greater flexibility as compared to full Bayesian approaches. The method also naturally provides uncertainties in parameter estimates, avoiding the artificial certainty implied by many optimization methods for determining model parameters. I demonstrate the approach by estimating parameters in a four-parameter nonlinear SGS closure using reference data from a reference run that has been performed with the same model. I have validated that this approach recovers the right set of parameters, using a uniform grid formed by 2401 different combinations of sets of parameters. The ABC method is thus shown to be an effective approach for estimating unknown parameters, including their uncertainties, in SGS closure models for LES of turbulent flows.
MatèriesTurbulence -- Mathematical models, Computational fluid dynamics, Turbulència -- Models matemàtics, Dinàmica de fluids computacional
TitulacióMÀSTER UNIVERSITARI EN ENGINYERIA DE L'ENERGIA (Pla 2013)
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