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Subekti, M.*; Kudo, Kazuhiko*; Nabeshima, Kunihiko; Takamatsu, Kuniyoshi
Atom Indonesia, 43(2), p.93 - 102, 2017/08
Reactor kinetics based on point kinetic model have been generally applied as the standard method for neutronics codes. As the central control rod (C-CR) withdrawal test has demonstrated in a prismatic core of HTTR, the transient calculation of kinetic parameter, such as reactivity and neutron fluxes, requires a new method to shorten calculation-process time. Development of neural network method was applied to point kinetic model as the necessity of real-time calculation that could work in parallel with the digital reactivity meter. The combination of TDNN and Jordan RNN, such as TD-Jordan RNN, was the result of the modeling approach. The application of TD-Jordan RNN with adequate learning, tested offline, determined results accurately even when signal inputs were noisy. Furthermore, the preprocessing for neural network input utilized noise reduction as one of the equations to transform two of twelve time-delayed inputs into power corrected inputs.
Ono, Tomio*; Subekti, M.*; Kudo, Kazuhiko*; Takamatsu, Kuniyoshi; Nakagawa, Shigeaki; Nabeshima, Kunihiko
Nihon Genshiryoku Gakkai Wabun Rombunshi, 4(2), p.115 - 126, 2005/06
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 artificial neural networks to predict the changes of reactor power and reactivity. The network model applied in this study is based on recurrent neural networks. The inputs of the network are the changes of the central control rods position and other significant core parameters, and the outputs are the changes of reactor power and reactivity. Furthermore, Time Synchronizing Signal(TSS) is added to input to improve the modeling of time series data. The actual tests data, which were previously carried out in the HTTR, were used for learning the model of the plant dynamics. After the learning, the network can predict the changes of reactor power and reactivity in the following tests.
