Computational fluid dynamics modeling for emergency preparedness and response

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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 ... continued below

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14 p.

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Lee, R.L.; Albritton, J.R.; Ermak, D.L. & Kim, J. February 1, 1995.

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Description

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.

Physical Description

14 p.

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OSTI as DE95013432

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  • International conference and workshop on modeling and mitigating the consequences of accidental releases of hazardous materials, New Orleans, LA (United States), 26-29 Sep 1995

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  • Other: DE95013432
  • Report No.: UCRL-JC--120469
  • Report No.: CONF-950927--4
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 83867
  • Archival Resource Key: ark:/67531/metadc780713

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  • February 1, 1995

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  • Dec. 3, 2015, 9:30 a.m.

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  • Feb. 16, 2016, 5:11 p.m.

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Lee, R.L.; Albritton, J.R.; Ermak, D.L. & Kim, J. Computational fluid dynamics modeling for emergency preparedness and response, article, February 1, 1995; California. (digital.library.unt.edu/ark:/67531/metadc780713/: accessed August 16, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.