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AN IMPROVED ALGORITHM FOR POTENTIAL ACCIDENT CONSEQUENCE ASSESSMENT AT NPPS BASED ON PAVAN

机译:基于PAVAN的NPPS潜在事故后果评估的改进算法。

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PAVAN is an atmospheric dispersion program for evaluating design basis accidental releases of radioactive materials from nuclear power plant. It was developed by Pacific Northwest Laboratory on the basic of the atmospheric dispersion models described in RG 1.145 by NRC (U.S. Nuclear Regulatory Commission). Using the joint frequency of wind direction, wind speed and atmospheric stability, the atmospheric relative concentration values for the exclusion area boundary and outer LPZ boundary of nuclear power plant are calculated and given by the program. Once the program was introduced, it has been widely used in the radioactive accident consequence assessment, especially in the FSAR (Final Safety Analysis Report) and Report of EIA (Environmental Impact Assessment) of NPPs in China. The theory basis and general method of PAVAN is introduced in this paper. And specialty of the X/Q points based on joint frequency data is discussed. The envelope algorithm of PAVAN is also introduced and discussed. The paper presents an improved algorithm based on PAVAN which uses the hourly meteorological data as input instead of joint frequency data. In this algorithm, the size of X/Q points is related to the quantity of the hourly meteorological data. When the quantity is large enough, e.g. 17520 sets of hourly meteorological data in two years, the envelope curve for X/Q points fit more exactly than PAVAN. Using the observed meteorological data, the improved algorithm is compared with PAVAN. The result proves that the former is more accurate. In general, the improved algorithm is relatively conservative. In some situation, the conservativeness is not certain. The factors which result in the uncertainty are deeply discussed. Further optimized are performed by the algorithm. The number of points to seek in envelope curve fitting is set to be dynamic and be a quarter of total number of X/Q points to be fitted. The result shows that increasing the number of points to seek in the iteration process of envelope curve fitting will lead to more conservative X/Q values. Additionally, the optimized algorithm provides X/Q value of 50% probability level for overall site. The value is not relatively conservative. From the standpoint of statistical probability, it is more realistic and is acceptable for potential accident consequence assessment. Especially, when X/Q value of 95% probability level for overall site is too conservative to accept, the value of 50% probability level can be used to replace the conservative value.
机译:PAVAN是一种大气扩散程序,用于评估设计基准的核电站放射性物质的意外释放。它是由西北太平洋实验室根据美国国家核监管委员会(NRC)RG 1.145中描述的大气弥散模型开发的。利用风向,风速和大气稳定度的联合频率,计算出核电站禁区边界和LPZ外边界的大气相对浓度值,并通过程序给出。该程序一经推出,便已广泛用于放射性事故后果评估中,特别是在中国核电厂的FSAR(最终安全分析报告)和EIA(环境影响评估)报告中。介绍了PAVAN的理论基础和一般方法。并讨论了基于联合频率数据的X / Q点的特性。还介绍了PAVAN的包络算法。本文提出了一种基于PAVAN的改进算法,该算法使用小时气象数据作为输入而不是联合频率数据。在该算法中,X / Q点的大小与每小时气象数据的数量有关。当数量足够大时,例如两年内有17520套每小时的气象数据,X / Q点的包络曲线比PAVAN更精确。利用观测到的气象数据,将改进算法与PAVAN进行比较。结果证明,前者更为准确。通常,改进算法比较保守。在某些情况下,不确定性是不确定的。深入讨论了导致不确定性的因素。通过算法执行进一步优化。包络曲线拟合中要寻找的点数设置为动态的,并且是要拟合的X / Q点总数的四分之一。结果表明,在包络线拟合的迭代过程中增加要寻找的点数将导致更保守的X / Q值。此外,优化算法为整个站点提供了50%概率水平的X / Q值。该值不是相对保守的。从统计概率的角度来看,它是更现实的,对于潜在事故后果评估是可以接受的。特别是,当整个站点的95%概率水平的X / Q值过于保守而无法接受时,可以使用50%概率水平的值代替保守值。

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