Atmospheric Dispersion In Nuclear Power Plant Spring

Author : Dongqin Zheng
Publisher : Open Dissertation Press
ISBN 13 : 9781374667112
Total Pages : pages
Book Rating : 4.6/5 (671 download)

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Download or read book Evaluation and Development of Data Assimilation in Atmospheric Dispersion Models for Use in Nuclear Emergencies written by Dongqin Zheng and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Evaluation and Development of Data Assimilation in Atmospheric Dispersion Models for Use in Nuclear Emergencies" by Dongqin, Zheng, 鄭冬琴, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled EVALUATION AND DEVELOPMENT OF DATA ASSIMILATION IN ATMOSPHERIC DISPERSION MODELS FOR USE IN NUCLEAR EMERGENCIES Submitted by Zheng Dongqin for the degree of Doctor of Philosophy at The University of Hong Kong in August 2007 When radioactive contaminants are released into the atmosphere during a nuclear accident, the consequence assessment that relies on atmospheric dispersion models plays an important role for the decision-makers in taking the correct response. In this thesis, a 3D Monte Carlo Dispersion Model (MCDM) was developed and evaluated. Data assimilation technique was in- corporated to give a real time consequence assessment in the unlikely event of a nuclear accident of the nuclear power plant (NPP) in the Daya Bay. The predictions of MCDM were compared with that of the Particle Dis- persion Model (PDM) developed by the Hong Kong Observatory. The dif- ferences in air concentrations and ground depositions predicted by the two models were found to be within one order of magnitude. MCDM predictions were also tested against the measurements of a eld dispersion of radioactivematerials from a research reactor in Mol, Belgium. Both the gamma uence rates and the plume geometry given by MCDM are comparable with the measurement results. Although the dispersion model can execute well given a known source term, meteorological data and dispersion parameters, uncertainties of these model parameters may inuence the reliability of model predictions. The ability of a model in assimilating measurement data is crucial to improve model predictions. In this thesis, a data assimilation method based on the Ensemble Kalman Filter (EnKF) and a stochastic nonlinear state space model was proposed. It presents a new method for online update of the model predictions and estimation of the model parameters, i.e. the release rate, wind direction, or turbulence intensity, by adding the measurement in- formation. The proposed method has been tested by twin experiments in which simulated ground level dose rates have been assimilated. The predic- tion errors caused false' estimations of model parameters could be reduced by more than 60%. The temporal variations of model parameters estimated by EnKF were found to follow the true' value very well. In order to test the data assimilation method with real experimental data, a wind tunnel dispersion experiment was carried out for the east and south-east wind directions over the area surrounding the Daya Bay NPP. The measurement data collected were assimilated in the dispersion model. Amethodwaspresentedbyusingrandomeldstoconstructtheerrorsin3D parameters, e.g. the wind direction and turbulence intensity. This method is very helpful in ensuring the performance of the EnKF. The data assimilation method was also tested against the Mol experi- mental data. The gamma uence rates overestimated by the MCDM were corrected by EnKF. The temporal variations of model parameters estimated by EnKF have been shown to provide useful information for tracking the major error sources. DOI: 10.5353/th_b3934603 Subjects: Radioactive fallout Particle size determination Monte Carlo method