Determination of Reactivity and Neutron Flux Using Modified Neural Network for HTGR

M. Subekti (Center for Nuclear Reactor Technology and Safety, National Nuclear Energy Agency, Puspiptek Area Serpong, Tangerang Selatan 15310, Indonesia)
K. Kudo (Kyushu University, 2-33-19 Mizutani Higashi-ku,Fukuoka 813-0041, Japan)
K. Nabeshima (Research Group for Advanced Reactor System, Japan Atomic Energy Agency,Tokai-mura, Ibaraki-ken, Japan)
K. Takamatsu (Department of HTTR Project, Japan Atomic Energy Agency, Oarai-machi, Ibaraki-ken 311-1394, Japan)

Atom Indonesia Journal
Vol 43, No 2 (2017): August 2017

Nuclear kinetic calculations 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 type high-temperature gas-cooled reactor (HTGR) named High Temperature Engineering Test Reactor (HTTR), the transient calculation of kinetic parameter, 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 Time Delayed Neural Network (TDNN) and Jordan Recurrent Neural Network (Jordan RNN) named 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.