A Solution-Adaptive Grid Generation Scheme for Atmospheric Flow Simulations

Science Applications International Corporation (SAIC) has developed a novel hazardous dispersion model with support from the Defense Special Weapons Agency. The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is based on an unstructured prismatic grid. OMEGA has the capability to adapt its grid statically as well as dynamically. The grid can adapt to a set of user defined criteria such as shoreline, terrain, particle locations, and velocity deformation. A considerable reduction in CPU time is achieved by dynamically adapting the grid. This paper gives an overview of the OMEGA model, and describes the grid-generation scheme used by the model. To our knowledge, OMEGA is the only fully operational atmospheric model with spatial and dynamic grid adaptation capabilities. Introduction The accurate solution of any complex computational problem depends on fine spatial discretization of the computational domain. The degree of grid refinement is usually constrained by CPU restrictions and by run time expectations. In atmospheric flow simulations, a large amount of CPU time is required by planetary boundary layer physics, cloud microphysics, and radiation transfer calculations. For real-time flow predictions, optimizing the degree of grid refinement, given the CPU constraint, becomes crucial. The use of unstructured grids simplifies this problem by adapting to regions where a high degree of resolution is required, such as shorelines and areas of large terrain gradients, (e.g., see Fig. 1). Further improvement in the solution accuracy can be made by adapting the grid dynamically. Dietachmayer and Droegemeier [1] and Fiedler et al. [2] have approached dynamic grid adaptation using structured grids. Their methodologies have been restricted to idealized problems so far and From the 6 International Conference on Numerical Grid Generation in Computational Field Simulations, July 6-9, 1998, Greenwich, UK. International Society of Grid Generation, Mississippi State, MI, 327-335.