When applying large eddy simulation (LES) for wind-load assessment, simulating inflow turbulence characteristics in the atmospheric boundary layer (ABL) is crucial for achieving accurate results. Advances in divergence-free synthetic turbulence generators for ABL conditions have made LES more computationally affordable. However, empty-domain tests reveal systematic deviations between the synthetic turbulence and the prescribed profiles that can impact downstream loads. This study introduces a gradient-based iterative calibration workflow that simultaneously adjusts the mean velocity, turbulence intensities and integral length scales to reduce such discrepancies. Unlike approaches that calibrate individual components, the proposed method accounts for the interactions of turbulence quantities and corrects discrepancies caused by divergence-free and mass-flux corrections and turbulence dissipation, leading to more control over the incident flow. The method is applied to a tall-building case from the Tokyo Polytechnic University aerodynamic database for wind angles 0° and 45°. By calibrating the inflow at different locations, the effects of correctors and convection are quantified. For both wind angles, the effect of calibration is most pronounced on the windward pressure and drag coefficients. It substantially reduces the coefficient of variation of root-mean-square error (CVRMSE) of the standard deviation (STD) of windward pressure coefficients (e.g. 8 % to 1 % at 0°and 17 % to 1 % at 45°) and improves drag moment predictions. At 0°, the percentage error in the STD of drag moment coefficient changes from −26 % to +4 % and to −6 % for the respective calibrations. At 45°, the change is from −26 % to +19 % and −3 %, respectively. • An iterative workflow is proposed to calibrate inflow turbulence for ABL conditions. • Simultaneous calibration of mean velocity, turbulence intensities and length scales. • Calibration improves agreement of wind loads with wind tunnel measurements. • Inlet calibration reduces CVRMS error of pressure fluctuation from 8 % to 5 %. • Downstream calibration further reduces CVRMS error to 1 %.
Building similarity graph...
Analyzing shared references across papers
Loading...
J. Tze‐Fei Wong
City of Hope
Oya Mercan
University of Toronto
Paul J. Kushner
University of Toronto
Journal of Wind Engineering and Industrial Aerodynamics
University of Toronto
Building similarity graph...
Analyzing shared references across papers
Loading...
Wong et al. (Thu,) studied this question.
synapsesocial.com/papers/69a75deec6e9836116a283e8 — DOI: https://doi.org/10.1016/j.jweia.2026.106362