Modeling nuclear reactor core dynamics with recurrent neural networks

A recurrent multilayer perceptron (RMLP) model is designed and developed for simulation of core neutronic phenomena in a nuclear power plant, which constitute a non-linear, complex dynamic system characterized by a large number of state variables. Training and testing data are generated by REMARK, a first principles neutronic core model [16]. A modified backpropagation learning algorithm with an adaptive steepness factor is employed to speed up the training of the RMLP. The test results presented exhibit the capability of the recurrent neural network model to capture the complex dynamics of the system, yielding accurate predictions of the system response. The performance of the network is also demonstrated for interpolation, extrapolation, fault tolerance due to incomplete data, and for operation in the presence of noise.