TY - JOUR
T1 - Determining a priori a RANS model’s applicable range via global epistemic uncertainty quantification
AU - D. Huang, Xinyi L .
AU - Jain, Naman
AU - Abkar, Mahdi
AU - Kunz, Robert F.
AU - Yang, Xiang I. A.
PY - 2021/11
Y1 - 2021/11
N2 - Calibrating a Reynolds-averaged Navier–Stokes (RANS) model against data leads to an improvement. Determining a priori if such an improvement generalizes to flows outside the calibration data is an outstanding challenge. This work attempts to address this challenge via global epistemic Uncertainty Quantification (UQ). Unlike the available epistemic UQ methods that are local and tell us a model's uncertainty at one specific flow condition, the global epistemic UQ method presented in this work tells us also whether a perturbation of the original model would generalize. Specifically, the global epistemic UQ method evaluates a potential improvement in terms of its “effectiveness” and “inconsistency”. Any improvement can be put in one of the following four quadrants: first, high effectiveness, low inconsistency; second, high effectiveness, high inconsistency; third, low effectiveness, low inconsistency; and fourth, low effectiveness, high inconsistency. An improvement would generalize if and only if it is in the high effectiveness, low inconsistency quadrant. To demonstrate the concept, we apply the global epistemic UQ to full Reynolds stress modeling of a stratified shear layer. The global epistemic UQ results point to a model coefficient in the pressure-strain correlation closure (among others) as effective and consistent for predicting the quantity of interest of shear layer's growth. We calibrate the model coefficients such that our RANS matches direct numerical simulation data at one flow condition. We show that the calibrated model generalizes to several other test flow conditions. On the other hand, when calibrating a high inconsistency term, the model does not generalize beyond the calibration condition.
AB - Calibrating a Reynolds-averaged Navier–Stokes (RANS) model against data leads to an improvement. Determining a priori if such an improvement generalizes to flows outside the calibration data is an outstanding challenge. This work attempts to address this challenge via global epistemic Uncertainty Quantification (UQ). Unlike the available epistemic UQ methods that are local and tell us a model's uncertainty at one specific flow condition, the global epistemic UQ method presented in this work tells us also whether a perturbation of the original model would generalize. Specifically, the global epistemic UQ method evaluates a potential improvement in terms of its “effectiveness” and “inconsistency”. Any improvement can be put in one of the following four quadrants: first, high effectiveness, low inconsistency; second, high effectiveness, high inconsistency; third, low effectiveness, low inconsistency; and fourth, low effectiveness, high inconsistency. An improvement would generalize if and only if it is in the high effectiveness, low inconsistency quadrant. To demonstrate the concept, we apply the global epistemic UQ to full Reynolds stress modeling of a stratified shear layer. The global epistemic UQ results point to a model coefficient in the pressure-strain correlation closure (among others) as effective and consistent for predicting the quantity of interest of shear layer's growth. We calibrate the model coefficients such that our RANS matches direct numerical simulation data at one flow condition. We show that the calibrated model generalizes to several other test flow conditions. On the other hand, when calibrating a high inconsistency term, the model does not generalize beyond the calibration condition.
KW - Global epistemic uncertainty quantification
KW - Reynolds-averaged Navier–Stokes equation
UR - http://www.scopus.com/inward/record.url?scp=85114123675&partnerID=8YFLogxK
U2 - 10.1016/j.compfluid.2021.105113
DO - 10.1016/j.compfluid.2021.105113
M3 - Journal article
SN - 0045-7930
VL - 230
JO - Computers & Fluids
JF - Computers & Fluids
M1 - 105113
ER -