Purves Murray, Parkes David
*AWE-Aldermaston, Reading RG74PR, United Kingdom.
Health Phys. 2016 May;110(5):481-90. doi: 10.1097/HP.0000000000000463.
Three atmospheric dispersion models--DIFFAL, HPAC, and HotSpot--of differing complexities have been validated against the witness plate deposition dataset taken during the Full-Scale Radiological Dispersal Device (FSRDD) Field Trials. The small-scale nature of these trials in comparison to many other historical radiological dispersion trials provides a unique opportunity to evaluate the near-field performance of the models considered. This paper performs validation of these models using two graphical methods of comparison: deposition contour plots and hotline profile graphs. All of the models tested are assessed to perform well, especially considering that previous model developments and validations have been focused on larger-scale scenarios. Of the models, HPAC generally produced the most accurate results, especially at locations within ∼100 m of GZ. Features present within the observed data, such as hot spots, were not well modeled by any of the codes considered. Additionally, it was found that an increase in the complexity of the meteorological data input to the models did not necessarily lead to an improvement in model accuracy; this is potentially due to the small-scale nature of the trials.
三种复杂度各异的大气扩散模型——DIFFAL、HPAC和HotSpot——已针对在全尺寸放射性散布装置(FSRDD)现场试验期间获取的见证板沉积数据集进行了验证。与许多其他历史放射性扩散试验相比,这些试验的小规模性质为评估所考虑模型的近场性能提供了独特机会。本文使用两种图形比较方法对这些模型进行验证:沉积等值线图和热线剖面图。所有测试模型的评估结果都显示表现良好,尤其是考虑到先前的模型开发和验证一直集中在更大规模的场景。在这些模型中,HPAC通常产生最准确的结果,特别是在距离GZ约100米范围内的位置。观测数据中存在的特征,如热点,在所考虑的任何代码中都没有得到很好的模拟。此外,还发现增加输入模型的气象数据的复杂度并不一定会提高模型精度;这可能是由于试验的小规模性质所致。