The source term information of radioactive release in a nuclear accident is important for nuclear accident classification, radiological consequences evaluation and emergency response. Two major categories of source term estimation techniques are forward method based on the status data of the nuclear reactor and backward method based on environmental monitoring data. Although the forward method is more widely used, it may introduce large uncertainties into the source term estimate due to its subjective parameters and low quality of reactor status data in a severe accident. To reduce these uncertainties, a coupled source term estimation method that combines both forward and backward models, is proposed in this study. The forward part provides a source term forecast model based on Response Technical Manual RTM-96. Under the framework of sequential data assimilation, the backward part iteratively reduces the uncertainties in the source term estimate using environmental monitoring data. Numerical experiments with different uncertainties are performed using the reactor and monitoring point information of real Chinese nuclear power plant and its atmospheric tracer experiments.
ANALYSIS TECHNOLOGY OF ENVIRONMENTAL MONITORING DATA BASED ON INTERNET OF THINGS ENVIRONMENT AND IMPROVED NEURAL NETWORK ALGORITHM
