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Author (up) X.L. Zhang; Jian Guo Chen; Guofeng Su; Hongyong Yuan
Title Study on source inversion technology for nuclear accidents based on gaussian puff model and ENKF Type Conference Article
Year 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013
Volume Issue Pages 634-639
Keywords Gaussian distribution; Information systems; Kalman filters; Nuclear power plants; Emergency response systems; Ensemble Kalman Filter; Gaussian puff model; Nuclear accidents; Source characteristics; Source inversion; Source term estimation; Source terms; Nuclear reactor accidents
Abstract For nuclear power plant (NPP) accident, the assessment of the radiation consequences plays an important role in the emergency response system. However, the source characteristics which greatly influence thhe accuracy of the assessment result is poorly known or even unknown at the early phase of accident, wich can cause poorly understanding of the situation and delay the response activities. In this paper, source inversion technology in analyzing nuclear accidents based on Gaussian puff model and ensemble Kalman filter (EnKF) is proposed. The method is validated with simulated measurements and the results show that it can give reasonable estimations of the change in release rate and height simultaneously, though the first guess of release rate is 102 larger than the true value. The investigation of the influence of sharp change in source term shows that the method is robust to capture the sharp change, but there is a delay of response when the release height increases simultaneously.
Address Institute of Public Safety Research, Tsinghua, China
Corporate Author Thesis
Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9783923704804 Medium
Track Analytical Modelling and Simulation Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 1148
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Author (up) Yan Wang; Hong Huang; Lida Huang; Minyan Han; Yiwu Qian; Boni Su
Title An Agile Framework for Detecting and Quantifying Hazardous Gas Releases Type Conference Article
Year 2017 Publication Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2017
Volume Issue Pages 42-49
Keywords Hazardous gas release; mobile sensing; data fusion; leakage detection; source term estimation
Abstract In response to the threat of hazardous gas releases to public safety and health, we propose an agile framework for detecting and quantifying gas emission sources. Emerging techniques like high-precision gas sensors, source term estimation algorithms and Unmanned Aerial Vehicles are incorporated. The framework takes advantage of both stationary sensor network method and mobile sensing approach for the detection and quantification of hazardous gases from fugitive, accidental or deliberate releases. Preliminary results on street-level detection of urban natural gas leakage is presented. Source term estimation is demonstrated through a synthetic test case, and is verified using Cramér-Rao bound analysis.
Address Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing, China; Beijing Define Technology Co., Ltd, Beijing, China; Hefei Institute for Public Safety Research, Tsinghua University, Hefei, China
Corporate Author Thesis
Publisher Iscram Place of Publication Albi, France Editor Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN Medium
Track Analytical Modeling and Simulation Expedition Conference 14th International Conference on Information Systems for Crisis Response And Management
Notes Approved no
Call Number Serial 1998
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Author (up) Yan Wang; Hong Huang; Wei Zhu
Title Stochastic source term estimation of HAZMAT releases: algorithms and uncertainty Type Conference Article
Year 2015 Publication ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2015
Volume Issue Pages
Keywords Bayesian inference; emergency response; hazardous material releases; source term estimation; uncertainty
Abstract Source term estimation (STE) of hazardous material (HAZMAT) releases is critical for emergency response. Such problem is usually solved with the aid of atmospheric dispersion modelling and inversion algorithms accompanied with a variety of uncertainty, including uncertainty in atmospheric dispersion models, uncertainty in meteorological data, uncertainty in measurement process and uncertainty in inversion algorithms. Bayesian inference methods provide a unified framework for solving STE problem and quantifying the uncertainty at the same time. In this paper, three stochastic methods for STE, namely Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC) and ensemble Kalman filter (EnKF), are compared in accuracy, time consumption as well as the quantification of uncertainty, based on which a kind of flip ambiguity phenomenon caused by various uncertainty in STE problems is pointed out. The advantage of non-Gaussian estimation methods like SMC is emphasized.
Address
Corporate Author Thesis
Publisher University of Agder (UiA) Place of Publication Kristiansand, Norway Editor L. Palen; M. Buscher; T. Comes; A. Hughes
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9788271177881 Medium
Track Analytical Modelling and Simulation Expedition Conference ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 1194
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