Dispersal and fallout simulations for urban consequences management

  • Fernando F. Grinstein
  • Gopal Patnaik
  • Adam J. Wachtor
  • Matt Nelson
  • Michael Brown
  • Randy J. Bos
Part of the ERCOFTAC Series book series (ERCO, volume 15)


Hazardous chemical, biological, or radioactive releases from leaks, spills, fires, or blasts, may occur (intentionally or accidentally) in urban environments during warfare or as part of terrorist attacks on military bases or other facilities. The associated contaminant dispersion is complex and semi-chaotic. Urban predictive simulation capabilities can have direct impact in many threat-reduction areas of interest, including, urban sensor placement and threat analysis, contaminant transport (CT) effects on surrounding civilian population (dosages, evacuation, shelter-in-place), education and training of rescue teams and services. Detailed simulations for the various processes involved are in principle possible, but generally not fast. Predicting urban airflow accompanied by CT presents extremely challenging requirements (Britter and Hanna, 2003; Patnaik et al., 2007; Grinstein et al., 2009).


Strong Motion Contaminant Transport Contaminant Source Plume Rise Dispersal Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Britter, R.E., and Hanna, S.R., Ann. Rev. Fluid Mech., 35, 469–496 (2003). CrossRefGoogle Scholar
  2. 2.
    Patnaik, G., Boris, J.P., Young, T.R., Grinstein, F.F., J. Fluids Eng., 129, 1524–1532 (2007). CrossRefGoogle Scholar
  3. 3.
    Grinstein, F.F., Bos, R.J. and Dey, T.N., ERCOFTAC Bulletin, 78, 11–14 (2009). Google Scholar
  4. 4.
    Grinstein, F.F., Margolin, L.G., Rider, W.J., Editors, Implicit Large Eddy Simulation: Computing Turbulent Fluid Dynamics, Cambridge University Press (2007). zbMATHGoogle Scholar
  5. 5.
    Brown, M., “Urban Dispersion Challenges for Fast Response Modeling”, in Fifth AMS Symposium on the Urban Environment, LA-UR-04-5129, LANL, Los Alamos, NM (2004). Google Scholar
  6. 6.
    Dey, T.N. and Bos, R.J., “CASH, Version 01,” LANL computer code LA-CC-01-053 (2001). Google Scholar
  7. 7.
    Cheng, N.-S., Powder Technology, 189 (2009). Google Scholar
  8. 8.
    Pinnick, R.G., Fernandez, G., and Hinds, B.D., Appl. Optics, 22, 95–102 (1983). CrossRefGoogle Scholar
  9. 9.
    Singh, B., Hansen, B., Brown, M., Pardyjak, E., Env. Fluid Mech., 8, 281–312 (2008). CrossRefGoogle Scholar
  10. 10.
    Allwine, K.J., Flaherty, J.E., Brown, M., Coirier, W., Hansen, O., Huber, A., Leach, M. and Patnaik, G., “Urban Dispersion Program: Evaluation of six building-resolved urban dispersion models”, Official Use Only PNNL-17321 report (2008). Google Scholar
  11. 11.
    Boughton, B.A. and DeLaurentis, J.M., An Integral Model of Plume Rise from High Explosive Detonations, SAND-86-2553C. Google Scholar
  12. 12.
    Lee, M.-Y., Harms, F., Young, T., Leitl, B., and Patnaik, G., Model- and Application-Specific Validation Data for LES-Based Transport and Diffusion Models, 89th AMS Annual Conference, Phoenix AZ (January 2009). Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Fernando F. Grinstein
    • 1
  • Gopal Patnaik
  • Adam J. Wachtor
  • Matt Nelson
  • Michael Brown
  • Randy J. Bos
  1. 1.MS F644Los Alamos National LaboratoryLos AlamosUSA

Personalised recommendations