A model-based data-interpretation framework for improving wind predictions around buildings

• Measurements are used to improve CFD predictions at unmeasured locations.
• Time dependent predictions at unmeasured locations are obtained.
• Information about uncertainties in predictions is obtained.
• Reliable prediction ranges are obtained at unmeasured locations.

Although Computational Fluid Dynamics (CFD) simulations are often used to assess wind conditions around buildings, the accuracy of such simulations is often unknown. This paper proposes a data-interpretation framework that uses multiple simulations in combination with measurement data to improve the accuracy of wind predictions. Multiple simulations are generated through varying sets of parameter values. Sets of parameter values are falsified and thus not used for predictions if differences between measurement data and simulation predictions, for any measurement location, are larger than an estimate of uncertainty bounds. The bounds are defined by combining measurement and modeling uncertainties at sensor locations. The framework accounts for time-dependent and spatially-distributed modeling uncertainties that are present in CFD simulations of wind. The framework is applied to the case study of the CREATE Tower located at the National University of Singapore. Values for time-dependent inlet conditions, as well as values for the roughness of surrounding buildings, are identified with measurements carried out around the CREATE Tower. Results show that, on average, ranges of horizontal wind-speed predictions at an unmeasured location have been decreased by 65% when measurement data are used.