Modeling nonlinear fractional-order subdiffusive dynamics in nuclear reactor with artificial neural networks

• Published in: International journal of dynamics and control Vol. 11; no. 4; pp. 1995 - 2020
• Main Authors: Bhusari, Balu P., Patil, Mukesh D., Jadhav, Sharad P., Vyawahare, Vishwesh A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-08-2023; Springer Nature B.V
• Subjects: Artificial neural networks; Complexity; Control; Control and Systems Theory; Differential equations; Dynamical Systems; Engineering; Fractional calculus; Integral equations; Iterative methods; Kinetics; Neural networks; Nonlinear dynamics; Nuclear reactors; Vibration; Nonlinear dynamic models; ANN modeling; Fractional-order modeling; Subdiffusive neutron transport; Nuclear reactor modeling
• ISSN: 2195-268X; 2195-2698
• DOI: 10.1007/s40435-022-01100-6

This paper presents the development and analysis of artificial neural network (ANN) models for the nonlinear fractional-order (FO) point reactor kinetics model, FO Nordheim–Fuchs model, inverse FO point reactor kinetics model and FO constant delayed neutron production rate approximation model. These models represent the dynamics of a nuclear reactor with neutron transport modeled as subdiffusion. These FO models are nonlinear in nature, are comprised of a system of coupled fractional differential equations and integral equations, and are considered to be difficult for solving both analytically and numerically. The ANN models were developed using the data generated from these models. The work involves the iterative process of ANN learning with different combinations of layers and neurons. It is shown through extensive simulation studies that the developed ANN models faithfully capture the transient and steady-state dynamics of these FO models, thereby providing a satisfactory representation for the nonlinear subdiffusive process of neutron transport in a nuclear reactor.