Developing an approach for maximizing neutron activation reaction rate by optimizing moderator dimensions and target position using the Monte Carlo code in combination with the GA and ANN algorithms

Abstract

In this study, in order to maximize the reaction rate of neutron activation (NA), an approach using combination of the MCNP code, the feed-forward neural network with the Bayesian regularization (FFNN-BR) learning algorithm, and the genetic algorithm (GA) is proposed. The MCNP code calculates the reaction rates based on the different moderator dimensions/ target positions. The calculated reaction rates with appropriate features (i.e. RT, R2S, and Z2S) are applied for training of the FFNN-BR. The trained neural network is utilized for estimating the reaction rates of the generated individuals by the GA. The results show that the trained neural network estimates the reaction rates with acceptable accuracy. For selection of the appropriate individual for maximizing the reaction rate, the distance of the source from the target as well as the value of reaction rate and the moderator dimensions are considered. The proposed approach in this study gives a simple and practical algorithm for maximizing the NA reaction rate without complexities related to the nature of the problem.