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Simulation of a Nuclear Cloud's Propagation Following a Nuclear Accident

机译:核事故后核云的传播模拟

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Radioactive particles leaked from a point source or a reactor are generally carried around by the wind, they are mostly concentrated in the downwind direction of their point sources when the winds blow in a particular direction continuously. For example, the chances of people being exposed to radiation particles are expected to be greater on the downwind direction of a source of nuclear contamination compared to the opposite case. If this principle of winds carrying radiation particles is the greatest most influential factor that determines the characteristics of contaminant dispersion under most weather conditions, it can be concluded that weather and wind patterns can be used as a medium to predict radiation fallout. The risk of exposure thus could be approximated using wind properties and by considering other factors that might pose as variables in the simulation. However, if this assumption is true remains unclear as the dispersion patterns and weather and wind patterns are both quite complex, making it difficult to find significant correlation factors. In this study, various machine learning-based prediction algorithms are used to clarify the dispersion patterns using the available wind patterns.
机译:从点源或反应器泄漏的放射性颗粒通常通过风携带,当风力连续地吹在特定方向上时,它们大多集中在其点源的下行方向上。例如,与相反的情况相比,预期暴露于辐射颗粒的人们暴露于辐射粒子的可能性更大。如果这种携带辐射粒子的风原理是最大的最有影响力的因素,可以决定在大多数天气条件下污染物分散的特征,可以得出结论,天气和风图案可以用作预测辐射辐射的介质。因此,可以使用风性能和考虑在模拟中变量姿势的其他因素来近似暴露风险。但是,如果这种假设是真的,随着分散模式和天气和风图案既相当复杂,仍然不清楚,难以找到显着的相关因素。在这项研究中,使用基于机器学习的预测算法用于使用可用的风图案来阐明分散模式。

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