Automatic optimisation to improve wind field predictions

A number of studies have reported on the application of computational fluid dynamics (CFD) models to produce high resolution data of wind flow. CFD models are based on the numerical solution of a number of partial differential equations expressing conservation of mass, momentum and energy (when buoyancy effects are not negligible) and make use of a number of simplifications (turbulence models) to reduce the range of length scales which are directly resolved. Most of these works validate the proposed approaches in simplified domains, where separated flows and recirculation regions are limited. In addition, application of CFD models requires a precise specification of both simulation domain characteristics and boundary conditions (in terms of flow direction and intensity); unfortunately, especially in areas characterised by complex terrains, measured data of wind intensity and direction collected close to the limits of the computational domain (fundamental to prescribe appropriate boundary conditions) are often not available. The aims of this work were (1) to develop a methodology for simulating wind flow in domains with complex orography, by matching data within the domain, (2) to compare wind fields predicted by different commercial or open-source software and (3) to validate the simulation results on a set of experimental data collected in different areas of the computational domain. More specifically, CFD simulations were linked to an automatic optimisation system developed by the authors based on a pattern search algorithm, which attempts to minimise two functions (deviation of wind speed and direction) at a number of prescribed locations within the domain where experimental data are available. A kriging approximation was used to accelerate convergence to a minimum. The study demonstrates the capabilities of the proposed approach in simulating wind flow in areas characterised by a complex topography and uncertainty in boundary conditions.