Liu Yun, Li Hong, Sun Sida, Fang Sheng
Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing, 100084, China.
J Environ Radioact. 2017 Sep;175-176:94-104. doi: 10.1016/j.jenvrad.2017.04.016. Epub 2017 May 6.
An enhanced air dispersion modelling scheme is proposed to cope with the building layout and complex terrain of a typical Chinese nuclear power plant (NPP) site. In this modelling, the California Meteorological Model (CALMET) and the Stationary Wind Fit and Turbulence (SWIFT) are coupled with the Risø Mesoscale PUFF model (RIMPUFF) for refined wind field calculation. The near-field diffusion coefficient correction scheme of the Atmospheric Relative Concentrations in the Building Wakes Computer Code (ARCON96) is adopted to characterize dispersion in building arrays. The proposed method is evaluated by a wind tunnel experiment that replicates the typical Chinese NPP site. For both wind speed/direction and air concentration, the enhanced modelling predictions agree well with the observations. The fraction of the predictions within a factor of 2 and 5 of observations exceeds 55% and 82% respectively in the building area and the complex terrain area. This demonstrates the feasibility of the new enhanced modelling for typical Chinese NPP sites.
为应对典型的中国核电站（NPP）厂址的建筑布局和复杂地形，提出了一种增强型大气扩散建模方案。在此建模中，加利福尼亚气象模型（CALMET）和稳态风拟合与湍流模型（SWIFT）与里瑟中尺度烟团模型（RIMPUFF）耦合，用于精确风场计算。采用建筑物尾流中大气相对浓度计算机代码（ARCON96）的近场扩散系数校正方案来表征建筑物阵列中的扩散。通过复制典型中国核电站厂址的风洞实验对所提出的方法进行评估。对于风速/风向和空气浓度，增强型建模预测结果与观测值吻合良好。在建筑区域和复杂地形区域，预测值在观测值的2倍和5倍范围内的比例分别超过55%和82%。这证明了新的增强型建模对于典型中国核电站厂址的可行性。
