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首页> 外文会议>International Conference on Probabilistic Safety Assessment and Management(PSAM7-ESREL'04) v.6; 20040614-20040618; Berlin; DE >Comparison of Average Transport and Dispersion Among a Gaussian Model, a Two-Dimensional Model and a Three-Dimensional Model
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Comparison of Average Transport and Dispersion Among a Gaussian Model, a Two-Dimensional Model and a Three-Dimensional Model

机译：高斯模型，二维模型和三维模型之间的平均传输和色散比较

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摘要

The Nuclear Regulatory Commission's (NRC's) code for predicting off-site consequences, MACCS2[1] (MELCOR Accident Consequence Code System, Version 2), is used for Level 3 Probabilistic Risk Analysis Consequence analyses, planning for emergencies, and cost-benefit analyses. It uses a simplified model for atmospheric transport and dispersion (ATD), that is, a straight-line Gaussian model. This model has been criticized as being overly simplistic, even for its purpose. The justification for its use has been that only average or expected values of metrics of interest are needed for planning and that a simplified model, by averaging metrics of interest obtained using numerous weather sequences one-by-one, compensates for the loss of structure in the meteorology that occurs away from the point of release. The simple model has been retained because of the desire to have short running times on personal computers covering the entire path through the environment, including the food and water pathway, and covering essentially a lifetime of exposure to a contaminated environment. The assumption about the adequacy of averaging metrics of interest over numerous weather sequences has never been tested for the Nuclear Regulatory Commission's (NRC's) purposes. Because of an increased interest in Level-3 Probabilistic Risk Assessments (PRAs), testing of the assumption has been performed. The results from MACCS2, the simplified model; LODI[2] (Lagrangian Operational Dispersion Integrator), a state-of-the-art, 3-dimensional advection-diffusion code using a Lagrangian stochastic, Monte Carlo method; and RASCAL[4] (Radiological Assessment System for consequence analysis), which uses a Lagrangian trajectory, Gaussian puff model, have been compared. RASCAL is between MACCS2 and LODI in complexity. LODI is coupled to ADAPT[3] (Atmospheric Data Assimilation and Parameterization Technique), which provides fields of mean winds, turbulence, pressure, temperature, and precipitation based on observed or model-simulated meteorology. RASCAL uses meteorological fields generated by interpolation of surface data. The objective of this study is to see if the average ATD results from these three codes are sufficiently close that a more complex model is not required for the NRC purposes of planning and cost-benefit analysis or different enough that the NRC code should be modified to provide more rigorous ATD. It would be better if MACCS2 results could be compared with measurements over the long distances and types of terrain of interest to the NRC. However, such measurements do not exist, so the less desirable comparison with a state-of-the-art code was chosen to provide input into the decision on the adequacy of the MACCS2 ATD. Comparisons of LODI/ADAPT results with intentional and unintentional releases can be found in Foster, et al[5]. These comparisons, although over shorter ranges than those of interest to the NRC, demonstrate that LODI/ADAPT is sufficiently accurate for the purposes of this study.
机译：核监管委员会（NRC）的预测场外后果的代码MACCS2 [1]（MELCOR事故后果代码系统，版本2）用于3级概率风险分析后果分析，应急计划和成本效益分析。它使用简化的大气传输和扩散模型（ATD），即直线高斯模型。有人批评该模型过于简单，即使出于其目的。使用该方法的理由是，仅需要规划感兴趣指标的平均值或期望值，并且简化模型通过对使用多个天气序列一对一获得的感兴趣指标进行平均，可以补偿结构中的损失。远离释放点发生的气象。由于希望在个人计算机上具有较短的运行时间来覆盖整个环境路径（包括食物和水的路径），并且基本上覆盖暴露于污染环境的整个生命周期，因此保留了该简单模型。从未针对核监管委员会（NRC）的目的检验过有关在多个天气序列上平均兴趣指标是否足够的假设。由于对3级概率风险评估（PRA）的兴趣增加，因此对该假设进行了测试。来自简化模型MACCS2的结果； LODI [2]（拉格朗日运算色散积分器），一种采用拉格朗日随机蒙特卡罗方法的最新3维对流扩散码；比较了使用拉格朗日轨迹，高斯吹气模型的RASCAL [4]和用于后果分析的放射学评估系统。 RASCAL的复杂度介于MACCS2和LODI之间。 LODI与ADAPT [3]（大气数据同化和参数化技术）耦合，后者根据观测到的或模拟的气象学提供平均风，湍流，压力，温度和降水场。 RASCAL使用通过对地面数据进行插值生成的气象场。这项研究的目的是查看这三个代码的平均ATD结果是否足够接近，以至于出于计划和成本效益分析的目的，无需使用更复杂的模型，还是应将NRC代码修改为提供更严格的ATD。最好将MACCS2结果与NRC感兴趣的长距离和地形类型的测量结果进行比较。但是，这种测量不存在，因此选择了与最新代码的比较不理想的比较，以提供有关MACCS2 ATD充分性的决策输入。在Foster等人的研究中，可以发现LODI / ADAPT结果与有意释放和无意释放的比较[5]。这些比较，尽管比NRC感兴趣的范围更短，但表明LODI / ADAPT对于本研究而言足够准确。

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