Computational fluid dynamics modeling for emergency preparedness & response

Computational fluid dynamics (CFD) has played an increasing role in the improvement of atmospheric dispersion modeling. This is because many dispersion models are now driven by meteorological fields generated from CFD models or, in numerical weather prediction`s terminology, prognostic models. Whereas most dispersion models typically involve one or a few scalar, uncoupled equations, the prognostic equations are a set of highly-coupled, nonlinear equations whose solution requires a significant level of computational power. Until recently, such computer power could be found only in CRAY-class supercomputers. Recent advances in computer hardware and software have enabled modestly-priced, high performance, workstations to exhibit the equivalent computation power of some mainframes. Thus desktop-class machines that were limited to performing dispersion calculations driven by diagnostic wind fields may now be used to calculate complex flows using prognostic CFD models. The Atmospheric Release and Advisory Capability (ARAC) program at Lawrence Livermore National Laboratory (LLNL) has, for the past several years, taken advantage of the improvements in hardware technology to develop a national emergency response capability based on executing diagnostic models on workstations. Diagnostic models that provide wind fields are, in general, simple to implement, robust and require minimal time for execution. Such models have been the cornerstones of the ARAC operational system for the past ten years. Kamada (1992) provides a review of diagnostic models and their applications to dispersion problems. However, because these models typically contain little physics beyond mass-conservation, their performance is extremely sensitive to the quantity and quality of input meteorological data and, in spite of their utility, can be applied with confidence to only modestly complex flows

Computational fluid dynamics modeling for emergency preparedness and response

Computational fluid dynamics (CFD) has (CFD) has played an increasing in the improvement of atmospheric dispersion modeling. This is because many dispersion models are now driven by meteorological fields generated from CFD models or, in numerical weather prediction`s terminology, prognostic models. Whereas most dispersion models typically involve one or a few scalar, uncoupled equations, the prognostic equations are a set of highly-couple equations whose solution requires a significant level of computational power. Recent advances in computer hardware and software have enabled modestly-priced, high performance, workstations to exhibit the equivalent computation power of some mainframes. Thus desktop-class machines that were limited to performing dispersion calculations driven by diagnostic wind fields may now be used to calculate complex flows using prognostic CFD models. The Release and Advisory Capability (ARAC) program at Lawrence Livermore National Laboratory (LLNL) has, for the past several years, taken advantage of the improvements in hardware technology to develop a national emergency response capability based on executing diagnostic models on workstations. Diagnostic models that provide wind fields are, in general, simple to implement, robust and require minimal time for execution. Because these models typically contain little physics beyond mass-conservation, their performance is extremely sensitive to the quantity and quality of input meteorological data and, in spite of their utility, can be applied with confidence to only modestly complex flows. We are now embarking on a development program to incorporate prognostic models to generate, in real-time, the meteorological fields for the dispersion models. In contrast to diagnostic models, prognostic models are physically-based and are capable of incorporating many physical processes to treat highly complex flow scenarios