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A new Lagrangian particle model for the simulation of dense gas dispersion
Authors:D Anfossi  G Tinarelli  S Trini Castelli  M Nibart  C Olry  J Commanay
Institution:1. Institute of Atmospheric Sciences and Climate – ISAC/C.N.R., Corso Fiume 4, 10133 Torino, Italy;2. ARIANET Srl, Milano, Italy;3. ARIA Technologies, Boulogne-Billancourt, France;4. APSYS-EADS, Suresnes, France;1. University of Eastern Piedmont, Viale Teresa Michel 11, Alessandria, Italy;2. ISAC-CNR, Corso Fiume 4, 10133, Torino, Italy;3. Air Quality Control, Government of Styria, Landhausgasse 7, 8010, Graz, Styria, Austria;1. Hanna Consultants, 7 Crescent Ave., Kennebunkport, ME, USA;2. Homeland Security Studies and Analysis Institute, Falls Church, VA, USA;3. University of Delaware, Newark, DE, USA
Abstract:A Lagrangian stochastic model (MicroSpray), able to simulate the airborne dispersion in complex terrain and in presence of obstacles, was modified to simulate the dispersion of dense gas clouds. This is accomplished by taking into account the following processes: negative buoyancy, gravity spreading and the particle's reflection at the bottom computational boundary. Elevated and ground level sources, continuous and instantaneous emissions, time varying sources, plumes with initial momentum (horizontal, vertical or oblique in any direction), plumes without initial momentum are considered. MicroSpray is part of the model system MSS, which also includes the diagnostic MicroSwift model for the reconstruction of the 3-D wind field in presence of obstacles and orography. To evaluate the MSS ability to simulate the dispersion of heavy gases, its simulation performances are compared in detail to two field experiments (Thorney Island and Kit Fox) and to a chlorine railway accident (Macdona). Then, a comprehensive analysis considering several experiments of the Modelers Data Archive is presented. The statistical analysis on the overall available data reveals that the performance of the new MicroSpray version for dense gas releases is generally reliable. For instance, the agreement between concentration predictions and observations is within a factor of two in the 72% up to 99% of the occurrences for the case studies considered. The values of other performance measures, such as correlation coefficient, geometric mean bias and geometric variance, mostly set in the ranges indicated as good-model performances in the specialized literature.
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