By simulating and augmenting human intelligence, artificial intelligence can address challenges such as predicting k_eff and neutron flux of a reactor.

This study aims to apply the optimized extreme learning machine model to the prediction of reactor neutron flux and k_eff.

First of all, a three-dimensional IAEA reactor was selected as the research object, with the steady-state neutron flux and k_eff as the predictive variables. and the core physics analysis program ADPRES was employed to generate data samples. Then, the basic neural network models for neutron flux and k_eff were constructed using Extreme Learning Machine (ELM), and the importance of feature values was evaluated using Random Forest (RF) to establish the optimal input feature subset for each model. Subsequently, the optimal number of neurons in the hidden layer was determined using a traversal method. Finally, the Whale Optimization Algorithm (WOA) was used to optimize the initial weights and thresholds for further improvement of the model accuracy.

The evaluation results show that after dimensionality reduction and optimization processing, the predictive accuracy of k_eff has improved by two orders of magnitude, and the prediction error of neutron flux has decreased by 87.24%, and the model training time is also reduced.

The model method constructed of this study has certain reference significance for solving reactor keff and neutron flux.