期刊文献+

模式搜索算法在毒气泄漏中的源强反算 被引量:12

Back Calculation of Source Strength and Location of Toxic Gases Releasing Based on Pattern Search Method
下载PDF
导出
摘要 基于泄漏源下风向的浓度监测数据并结合大气扩散模式建立反算模型,以确定泄漏源的位置和强度。以扩散模式仿真的浓度数据与监测数据的匹配度作为目标函数,将反演问题转化为优化问题,利用模式搜索算法迭代优化。以高斯模型为例验证了算法的可行性,结果表明利用探测器提供的测量浓度值,模式搜索算法能够在较短时间内搜索到最优解,在计算复杂性或时间上较梯度型算法和智能优化算法有一定优势。该算法能够及时而准确地反算出泄漏源强度和位置,为事故的应急响应与救援提供依据。 In order to determine the location and the strength of the release source, an inversion model is constructed based on the concentrations observed in the downwind direction of the release source and a dispersion model. The simulated concentrations, obtained from the dispersion model, were compared with the observed concentrations, and the fitness of them was treated as an objective function. The objective function was iteratively optimized with the pattern search method until a given tolerance had achieved. A Gaussian puff model was employed to verify the feasibility of the pattern search method in back-calculating the parameters, and the computations indicate that this method is able to determine the optimal solution in a short time. Furthermore, this method is superior to the gradient-based algorithms and intelligent optimization algorithms in terms of computational complexity or computational time. Therefore, a timely and accurate estimation of the location and the strength is helpful to emergency rescue when the toxic gases are released.
出处 《中国安全科学学报》 CAS CSCD 北大核心 2010年第5期29-34,共6页 China Safety Science Journal
基金 国家自然科学基金资助(70502006) 教育部新世纪优秀人才支持计划(NCET-07-0056) 教育部博士点专项基金资助(20070010014)
关键词 浓度值 高斯模型 迭代次数 算法复杂性 时间 concentrations Gaussian model iterations complexity of the algorithm computational time
  • 相关文献

参考文献12

  • 1Senocak I, Hengartner N W, Short M B, et al. Stochastic event reconstruction of atmospheric contaminant dispersion using Bayesian inference[J]. Atmospheric Environment, 2008, 42(33): 7 718 -7 727.
  • 2Yee E. Theory for reconstruction of an unknown number of contaminant sources using probabilistic inference[J]. Boundary-Layer Meteorology, 2008, 127 (3) : 359 - 394.
  • 3Gilbert E J, Khajehnajafi S. Estimation of toxic substance release[P]. USP, US 6772071 B2,2004.08.03.
  • 4Elbem H, Schmidt H, Talagrand O, et al. 4D-variational data assimilation with an adjoint air quality model for emission analysis[J]. Environmental Modelling & Software, 2000, 15(6 - 7) : 539 - 548.
  • 5Yumimoto K, Uno I. Adjoint inverse modeling of CO emissions over Eastern Asia using four-dimensional variational data assimilation[J]. Atmospheric Environment, 2006, 40(35) : 6 836 -6 845.
  • 6Li F, Niu J L. An inverse approach for estimating the initial distribution of volatile organic compounds in dry building materia[J].Atmospheric Environment, 2005, 39(8) : 1 447 - 1 455.
  • 7Thomson L C, Hirst B, Gibson G, et al. An improved algorithm for locating a gas source using inverse methods[J].Atmospheric Environment, 2007, 41(6): 1 128-1 134.
  • 8Newman M, Hatfield K, Hayworth J, et al. A hybrid method for inverse characterization of subsurface contaminant flux[J]. Journal of Contaminant Hydrology, 2005, 81(1 -4): 34-62.
  • 9Haupt S E. A demonstration of coupled receptor/dispersion modeling with a genetic algorithm[J]. Atmospheric Environment, 2005, 39(37) : 7 181 -7 189.
  • 10Haupt S E, Young G S, Allen C T. Validation of a receptor-dispersion model coupled with a genetic algorithm using synthetic data[J].Journal of Applied Meteorology and Climatology, 2006, 45(3) : 476 -490.

同被引文献73

引证文献12

二级引证文献36

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部