Bayesian calibration of the constants of the k-ε 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-ε turbulence model and the uncertainties relating to these values. Thus we are able to narrow down the space of k-ε model constants which provide the best match with experimental data and quantify the uncertainty relating to both the k-ε 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.