Activity inversion of radioactive source term based on convolutional neural network

This research proposes an activity inversion method of radioactive source terms based on convolutional neural network, and establishes the correlation between the radiation field flux and the source term activity.

In this method, source terms calculation models are established for waste steel barrel, concrete barrel with built-in filter, square box with built-in air filter and pippeline in primary circuit of reactor, and the samples are learned based on convolutional neural network for source term activity inversion. Compared the activity inversion results with the simulated measured values, the average error can be controlled at about 10%, which verifies the effectiveness of the proposed method.