Improving computational predictions of wind field by automatic optimization

Wildland fire models and simulators developed in the last twenty years are increasingly applied in different ecosystems and countries of the world in order to predict fire behaviour and effects, and to support fire prevention and management programs. In addition to the simplifications and preliminary assumptions of fire spread models, which are mainly based on a semi-physical approach, criticisms of fire simulators frequently concern the need of high resolution environmental data (fuel model maps, canopy cover, etc.), and in particular data on environmental variables that quickly change in space and time (e.g. wind speed and direction).

The rapid increase in computational resources and numerical methods of the last years helps to provide high temporal and spatial resolution data on wind fields in order to improve the accuracy of simulations at both local and regional scale.

Several studies report 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 circulation 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 characterized 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 optimization system developed by the authors based on a pattern search algorithm, which attempts to minimize 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 characterized by a complex topography and uncertainty in boundary conditions.