Calibration of an extended Eddy Viscosity Turbulence Model using Uncertainty Quantification

Subbian, Gokul; Souza, Ana Carolina Botelho e; Radespiel, Rolf; Zander, Elmar; Friedman, Noémi; Moshagen, Thilo; Moioli, Matteo; Breitsamter, Christian; Sørensen, Kaare

doi:10.2514/6.2020-1031

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Zhao et al [25] analysed the parameters' uncertainty for predicting wall heat flux in hypersonic flows over the double-ellipsoid model and X-33 flight vehicle. Moreover, based on uncertainty analysis, Subbian et al [26] applied Bayesian inference to calibrated parameters in the correction terms of the SST turbulence model for complex vortical flows and Li et al [27] also utilized Bayesian inference to calibrate the parameters in the Spalart-Allmaras (SA) turbulence model in a jet flow and assessed the model's error. These studies established a firm basis for further uncertainty analysis on turbulence models and following works.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of Parameters in SST Turbulence Model for Shock Wave-Boundary Layer Interaction

Zhang

Zeng

et al. 2022

Aerospace

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Shock wave-boundary layer interactions (SWBLIs) have a tremendous influence on the performance of hypersonic vehicles. For the numerical simulation of such engineering flows, Reynolds averaged Navier-Stokes (RANS) still occupies an irreplaceable role. However, parameters of turbulence models in RANS have substantial uncertainties, which impact the reliability of simulation results. Thus, the aim of the present study is to conduct an uncertainty analysis on parameters in the shear-stress transport (SST) turbulence model for the simulation of SWBLIs. In the current work, uncertainty quantification was performed first. A surrogate model was constructed by the non-intrusive polynomial chaos (NIPC) method to propagate uncertainties from model parameters to the quantities of interests (QoIs) and quantify them. In the subsequent sensitivity analysis, the key parameters were identified for such flow by calculating the Sobol index of each parameter for various QoIs. The results indicate that uncertainties of model parameters led to non-negligible uncertainties in those QoIs, particularly in skin friction and wall heat flux. The parameters α1, σω1, β1 were identified as primary contributors through the sensitivity analysis. Moreover, the specific effects of the three parameters on the flow prediction were analyzed by changing the parameters’ values separately.

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Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of Parameters in SST Turbulence Model for Shock Wave-Boundary Layer Interaction

Zhang

Zeng

et al. 2022

Aerospace

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Spalart-Allmaras Turbulence Model Conditioning for Leading-Edge Vortex Flows

Moioli,

Breitsamter,

Sørensen-Libik

2024

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Data-driven Bayesian inference of turbulence model closure coefficients incorporating epistemic uncertainty

et al. 2021

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We introduce a framework for statistical inference of the closure coefficients using machine learning methods. The objective of this framework is to quantify the epistemic uncertainty associated with the closure model by using experimental data via Bayesian statistics. The framework is tailored towards cases for which a limited amount of experimental data is available. It consists of two components. First, by treating all latent variables (non-observed variables) in the model as stochastic variables, all sources of uncertainty of the probabilistic closure model are quantified by a fully Bayesian approach. The probabilistic model is defined to consist of the closure coefficients as parameters and other parameters incorporating noise. Then, the uncertainty associated with the closure coefficients is extracted from the overall uncertainty by considering the noise being zero. The overall uncertainty is rigorously evaluated by using Markov-Chain Monte Carlo sampling assisted by surrogate models. We apply the framework to the Spalart–Allmars one-equation turbulence model. Two test cases are considered, including an industrially relevant full aircraft model at transonic flow conditions, the Airbus XRF1. Eventually, we demonstrate that epistemic uncertainties in the closure coefficients result into uncertainties in flow quantities of interest which are prominent around, and downstream, of the shock occurring over the XRF1 wing. This data-driven approach could help to enhance the predictive capabilities of computational fluid dynamics (CFD) in terms of reliable turbulence modeling at extremes of the flight envelope if measured data is available, which is important in the context of robust design and towards virtual aircraft certification. The plentiful amount of information about the uncertainties could also assist when it comes to estimating the influence of the measured data on the inferred model coefficients. Finally, the developed framework is flexible and can be applied to different test cases and to various turbulence models.

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Calibration of an extended Eddy Viscosity Turbulence Model using Uncertainty Quantification

Cited by 5 publications

References 16 publications

Uncertainty Analysis of Parameters in SST Turbulence Model for Shock Wave-Boundary Layer Interaction

Uncertainty Analysis of Parameters in SST Turbulence Model for Shock Wave-Boundary Layer Interaction

Spalart-Allmaras Turbulence Model Conditioning for Leading-Edge Vortex Flows

Data-driven Bayesian inference of turbulence model closure coefficients incorporating epistemic uncertainty

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