DOI: 10.1016/j.atmosenv.2014.07.022
Scopus记录号: 2-s2.0-84904255188
论文题名: Probabilistic safety analysis for urgent situations following the accidental release of a pollutant in the atmosphere
作者: Armand P ; , Brocheton F ; , Poulet D ; , Vendel F ; , Dubourg V ; , Yalamas T
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 96 起始页码: 1
结束页码: 10
语种: 英语
英文关键词: Atmospheric transport and dispersion
; Gaussian process predictor
; Principal component analysis
; Surrogate modelling
; Uncertainty quantification
Scopus关键词: Atmospheric movements
; Computational fluid dynamics
; Design of experiments
; Face recognition
; Fuel additives
; Gaussian distribution
; Gaussian noise (electronic)
; Maps
; Monte Carlo methods
; Principal component analysis
; Uncertainty analysis
; Atmospheric transport and dispersions
; Computational fluid dynamics modeling
; Gaussian Processes
; High-performance computing resources
; Lagrangian particle dispersion model
; Probabilistic principal component analysis
; Surrogate modelling
; Uncertainty quantifications
; Computer simulation
; atmospheric modeling
; atmospheric pollution
; atmospheric transport
; decision making
; emission
; numerical model
; principal component analysis
; stakeholder
; uncertainty analysis
; air temperature
; article
; atmosphere
; atmospheric dispersion
; atmospheric transport
; meteorology
; Monte Carlo method
; pollution
; principal component analysis
; priority journal
; velocity
; wind
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: This paper is an original contribution to uncertainty quantification in atmospheric transport & dispersion (AT&D) at the local scale (1-10km). It is proposed to account for the imprecise knowledge of the meteorological and release conditions in the case of an accidental hazardous atmospheric emission. The aim is to produce probabilistic risk maps instead of a deterministic toxic load map in order to help the stakeholders making their decisions. Due to the urge attached to such situations, the proposed methodology is able to produce such maps in a limited amount of time. It resorts to a Lagrangian particle dispersion model (LPDM) using wind fields interpolated from a pre-established database that collects the results from a computational fluid dynamics (CFD) model. This enables a decoupling of the CFD simulations from the dispersion analysis, thus a considerable saving of computational time. In order to make the Monte-Carlo-sampling-based estimation of the probability field even faster, it is also proposed to recourse to the use of a vector Gaussian process surrogate model together with high performance computing (HPC) resources. The Gaussian process (GP) surrogate modelling technique is coupled with a probabilistic principal component analysis (PCA) for reducing the number of GP predictors to fit, store and predict. The design of experiments (DOE) from which the surrogate model is built, is run over a cluster of PCs for making the total production time as short as possible. The use of GP predictors is validated by comparing the results produced by this technique with those obtained by crude Monte Carlo sampling. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81150
Appears in Collections: 气候变化事实与影响
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作者单位: CEA, DAM, DIF, F-91297 Arpajon, France; NUMTECH, F-63175 Aubière, France; Sillages Environnement, F-69134 Ecully, France; Phimeca Engineering, F 63800 Cournon d'Auvergne, France
Recommended Citation:
Armand P,, Brocheton F,, Poulet D,et al. Probabilistic safety analysis for urgent situations following the accidental release of a pollutant in the atmosphere[J]. Atmospheric Environment,2014-01-01,96