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Control-rod withdrawal tests simulating reactivity insertion are carried out in the HTTR to verify the inherent safety features of HTGRs. This paper describes pre-test analysis method using a neural network (NN) to predict the changes of reactor power and reactivity. The inputs of the network are the changes of the central control rods position and other significant core parameters. The actual tests data, which were previously carried out in the HTTR, were used for leaming the model of the plant dynamics. After the learning, the network can predict the changes of reactor power and reactivity in the following tests. Furthermore, the following tests are carried out at higher initial power stage; therefore, it is needed to enhance the capability of extrapolation of the network model. In this study, we introduced new guidelines for enhancing the extrapolation capability of NN. The network model applied in this study was designed according to the guidelines. From the results with new test data which are carried out at higher power stage, it is shown that the network is able to predict the changes of reactor power and reactivity precisely and the guidelines are effective in enhancing the extrapolation capability of NN.

Pre-test Analysis Method using a Neural Network for Control-Rod Withdrawal Tests of HTTR