减小辐射场剂量率预测不确定度的数据同化研究

发布时间：2018-11-04 19:35

【摘要】：为提高核事故早期辐射场剂量率评估的准确性和可靠度,提出一种基于数据同化理论的评价方法。根据数据同化理论,定义辐射场状态空间,建立了适合辐射场剂量率预测模型的状态转移和误差协方差矩阵。利用同化算法,综合考虑核事故辐射场剂量率预测模型与实测数据,实现辐射场剂量率的最优化表达。在Matlab软件平台上,运用数值模拟实验和福岛实测数据,对该方法进行了双重验证。实验结果表明,数值模拟实验条件下,当放射性物质空气释放率高估1个数量级时,同化后,辐射场剂量率相对均方根误差从1个数量级降低至50%左右。利用福岛核事故监测数据对同化系统进行验证,同化后的结果与实际测量结果相近,相对均方根误差在20%左右。以上结果表明,通过合理运用数据同化方法,可有效减小辐射场剂量率预测的不确定度。
[Abstract]:In order to improve the accuracy and reliability of dose rate assessment of radiation field in the early stage of nuclear accident, an evaluation method based on data assimilation theory is proposed. According to the theory of data assimilation, the state space of radiation field is defined, and the state transfer and error covariance matrix suitable for dose rate prediction model of radiation field are established. The optimal expression of radiation field dose rate is realized by using assimilation algorithm, considering the dose rate prediction model of nuclear accident radiation field and the measured data. On the platform of Matlab software, the method is verified by using numerical simulation experiment and the measured data of Fukushima. The experimental results show that when the air release rate of radioactive material is overestimated by one order of magnitude, the relative RMS error of radiation field dose rate decreases from 1 order of magnitude to about 50% after assimilation. The assimilation system is verified by the data of Fukushima nuclear accident monitoring. The result of assimilation is similar to that of the actual measurement, and the relative root mean square error is about 20%. The above results show that the uncertainty of dose rate prediction of radiation field can be effectively reduced by using the data assimilation method reasonably.
【作者单位】：防化学院核防护系;
【分类号】：TL732

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