Bayesian calibration of the constants of the k k ? ? turbulence model for a CFD model of street canyon flow
In this paper we carry out a Bayesian calibration for uncertainty analysis in Computational Fluid Dynamics modelling of urban flows. Taking the case of airflow in a regular street canyon, and choosing turbulent kinetic energy (TKE) as our quantity of interest, we calibrate 3-D CFD simulations against wind tunnel observations. We focus our calibration on the model constants contained within the standard RANS k k ? ? turbulence model and the uncertainties relating to these values. Thus we are able to narrow down the space of k k ? ? model constants which provide the best match with experimental data and quantify the uncertainty relating to both the k k ? ? model constants in the case of street canyon flow and the TKE outputs of the CFD simulation. Furthermore, we are able to construct a statistical emulator of the CFD model. Finally, we provide predictions of TKE based on the emulator and the estimated bias between model and observations, accompanied with uncertainties in these predictions.
turbulence modelling computational fluid dynamics emulation uncertainty quantification k k ? ? model k k k k k ? ? ? ? ?