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An improved four-dimensional variation source term inversion model with observation error regularization

Defence Technology（防务技术） 2023年第6期24卷 349-360页

作者： Chao-shuai Han Xue-zheng Zhu Jin Gu Guo-hui Yan Xiao-hui Gao Qin-wen Zuo Institute of NBC Defence BeijingChina State Key Laboratory of NBC Protection for Civilian BeijingChina

Aiming at the Four-Dimensional Variation source term inversion algorithm proposed earlier,the observation error regularization factor is introduced to improve the prediction accuracy of the diffusion model,and an improved Four-Dimensional Variation source term inversion algorithm with observation error regularization(OER-4DVAR STI model)is formed.Firstly,by constructing the inversion process and basic model of OER-4DVAR STI model,its basic principle and logical structure are studied.Secondly,the observation error regularization factor estimation method based on Bayesian optimization is proposed,and the error factor is separated and optimized by two parameters:error statistical time and deviation degree.Finally,the scientific,feasible and advanced nature of the OER-4DVAR STI model are verified by numerical simulation and tracer test data.The experimental results show that OER-4DVAR STI model can better reverse calculate the hazard source term information under the conditions of high atmospheric stability and flat underlying surface.Compared with the previous inversion algorithm,the source intensity estimation accuracy of OER-4DVAR STI model is improved by about 46.97%,and the source location estimation accuracy is improved by about 26.72%.

关键词： source term inversion Four dimensional variation Observation error regularization factor Bayesian optimization SF6 tracer test

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Research on inversion method for complex source-term distributions based on deep neural networks

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Nuclear Science and Techniques 2023年第12期34卷 159-176页

作者： Yi‑Sheng Hao Zhen Wu Yan‑Heng Pu Rui Qiu Hui Zhang Jun‑Li Li Department of Engineering Physics Tsinghua UniversityBeijing 100084China Key Laboratory of Particle and Radiation Imaging of Ministry of Education Beijing 100084China Nuctech Company Limited Beijing 100084China

This study proposes a source distribution inversion convolutional neural network (SDICNN), which is deep neural network model for the inversion of complex source distributions, to solve inversion problems involving fixed-source distributions. A function is developed to obtain the distribution information of complex source terms from radiation parameters at individual sampling points in space. The SDICNN comprises two components:a fully connected network and a convolutional neural network. The fully connected network mainly extracts the parameter measurement information from the sampling points,whereas the convolutional neural network mainly completes the fine inversion of the source-term distribution. Finally, the SDICNN obtains a high-resolution source-term distribution image. In this study, the proposed source-term inversion method is evaluated based on typical geometric scenarios. The results show that, unlike the conventional fully connected neural network, the SDICNN model can extract the two-dimensional distribution features of the source terms, and its inversion results are better. In addition, the effects of the shielding mechanism and number of sampling points on the inversion process are examined. In summary, the result of this study can facilitate the accurate assessment of dose distributions in nuclear facilities.

关键词： source term inversion Monte Carlo Artificial intelligence Neural network

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