Multi-fidelity sensitivity analysis using locally calibrated RANS model

Forward propagation of uncertainty can be estimated through the sensitivity of a quantity of interest to uncertain variables. Multi-fidelity sensitivity analysis combines the high accuracy of high-fidelity models (DNS/LES) with the low computational cost and non-chaotic nature of low-fidelity models (RANS). In this study, we increase the model-complexity of the RANS closure model by allowing its coefficients to vary spatially. This model, with spatially varying coefficients, is calibrated using high-fidelity data at a base condition. The calibration is performed using a gradient-based optimization process. An adjoint solver is used to expedite the calibration process. The calibrated model is then used for sensitivity estimation.