Predictive RANS simulations via Bayesian Model-Scenario Averaging
TypeArticles dans des revues avec comité de lecture
The turbulence closure model is the dominant source of error in most Reynolds-Averaged Navier–Stokes simulations, yet no reliable estimators for this error component currently exist. Here we develop a stochastic, a posteriori error estimate, calibrated to specific classes of flow. It is based on variability in model closure coefficients across multiple flow scenarios, for multiple closure models. The variability is estimated using Bayesian calibration against experimental data for each scenario, and Bayesian Model-Scenario Averaging (BMSA) is used to collate the resulting posteriors, to obtain a stochastic estimate of a Quantity of Interest (QoI) in an unmeasured (prediction) scenario. The scenario probabilities in BMSA are chosen using a sensor which automatically weights those scenarios in the calibration set which are similar to the prediction scenario. The methodology is applied to the class of turbulent boundary-layers subject to various pressure gradients. For all considered prediction scenarios the standard-deviation of the stochastic estimate is consistent with the measurement ground truth. Furthermore, the mean of the estimate is more consistently accurate than the individual model predictions.
Files in this item
Showing items related by title, author, creator and subject.
GOMAR, Adrien; BOUVY, Quentin; SICOT, Frédéric; DUFOUR, Guillaume; CINNELLA, Paola; FRANCOIS, Benjamin (Elsevier, 2014)The convergence of Fourier-based time methods applied to turbomachinery flows is assessed. The focus is on the harmonic balance method, which is a timedomain Fourier-based approach standing as an efficient alternative to ...
SCIACOVELLI, L.; CINNELLA, Paola (ASME, 2014)Transonic flows through axial, multi-stage, transcritical ORC turbines, are investigated by using a numerical solver including advanced multiparameter equations of state and a high-order discretization scheme. The working ...
MERLE, Xavier; CINNELLA, Paola (Elsevier, 2015)A Bayesian inference methodology is developed for calibrating complex equations of state used in numerical fluid flow solvers. Precisely, the input parameters of three equations of state commonly used for modeling the ...
CONGEDO, Pietro; CORRE, Christophe; CINNELLA, Paola (AIAA, 2007)High-performance airfoils for transonic flows of Bethe–Zel’dovich–Thompson fluids are constructed using a robust and efficient Euler flow solver coupled with a multi-objective genetic algorithm. Bethe–Zel’dovich– Thompson ...
Numerical investigation of dense gas flows through transcritical multistage axial Organic Rankine Cycle turbines SCIACOVELLI, Lucas; CINNELLA, Paola (2013)Many recent studies suggest that supercritical Organic Rankine Cycles have a great potential for lowtemperature heat recovery applications, since they allow better recovery efficiency for a simplified cycle architecture. ...