提高核事故后果评价预测准确性的辐射监测数据解耦与同化方法研究

Nowadays, nuclear energy has become one of the most important energy resources relied on for the development of human society, while the probability of nuclear accident can still not be completely eliminated. Once a nuclear accident occurs, significant impacts can possibly be made on the environment, population, economy and society. By the consequence assessment for nuclear accident, classification and scale of the accident can be decided and the accident consequences can be calculated, predicted or evaluated, so that technological support can be offered for the emergency response decision. However, the predictions of the existing consequence assessment techniques tend to be not accurate enough for practical application. Monitoring data assimilation is one of the key directions of the research for promoting the predictive accuracy. In this project, we use a four-dimensional data assimilation method based on data registration to correct the prediction for the environmental distribution of radioactive materials in the consequence assessment. By this method, the predictive accuracy in consequence assessment will be improved to offer practicable technological support to the decision makers for nuclear emergency response. It will be of great significance for mitigating the radiological consequences of nuclear accidents.

核能现已成为人类社会发展所依赖的重要能源之一，但核事故发生的概率依然无法完全排除。核事故一旦发生，会对环境、人口、经济、社会运行产生重大影响。通过核事故后果评价，可以判断已经发生的事故类别和事故规模，计算、预测或估计事故的后果，从而为采取应急防护行动的决策提供技术依据。然而现有的后果评价技术普遍存在预测结果不准以至于难以实用的问题。监测数据同化是目前提高预测准确度的重点研究方向之一。本项目利用基于图像配准思想的四维数据同化方法，结合辐射环境监测数据，对后果评价中放射性物质在环境中分布的预测结果进行校正，从而提高后果评价预测结果的准确性，向决策者提供实际可行的核应急决策技术支持，对减轻核事故放射性后果具有重要意义。

核能现已成为人类社会发展所依赖的重要能源之一，但核事故发生的概率依然无法完全排除。核事故一旦发生，会对环境、人口、经济、社会运行产生重大影响。通过核事故后果评价，可以判断已经发生的事故类别和事故规模，计算、预测或估计事故的后果，从而为采取应急防护行动的决策提供技术依据。然而现有的后果评价技术普遍存在预测结果不准以至于难以实用的问题。监测数据同化是目前提高预测准确度的重点研究方向之一。本项目利用基于图像配准思想的四维数据同化方法，结合辐射环境监测数据，对后果评价中放射性物质在环境中分布的预测结果进行校正，从而提高后果评价预测结果的准确性，向决策者提供实际可行的核应急决策技术支持，对减轻核事故放射性后果具有重要意义。