A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis. (November 2015)

• Record Type: Journal Article
• Authors: Kumar, Akansha; Tsvetkov, Pavel V.
• Is Part Of: Annals of nuclear energy. Volume 85(2015:Nov.)
• Page Start: 27
• Page End: 35
• Publication Date: 2015-11
• Subjects: Genetic -- Algorithm -- Reactor -- Design -- Modular -- Optimization; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2015.04.028
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: This paper presents a new method useful for the optimization of complex dynamic systems. The method uses the strengths of; genetic algorithms (GA), and regression splines. The method is applied to the design of a gas cooled fast breeder reactor design. Tools like Java, R, and codes like MCNP, Matlab are used in this research. Abstract: A module based optimization method using genetic algorithms (GA), and multivariate regression analysis has been developed to optimize a set of parameters in the design of a nuclear reactor. GA simulates natural evolution to perform optimization, and is widely used in recent times by the scientific community. The GA fits a population of random solutions to the optimized solution of a specific problem. In this work, we have developed a genetic algorithm to determine the values for a set of nuclear reactor parameters to design a gas cooled fast breeder reactor core including a basis thermal–hydraulics analysis, and energy transfer. Multivariate regression is implemented using regression splines (RS). Reactor designs are usually complex and a simulation needs a significantly large amount of time to execute, hence the implementation of GA or any other global optimization techniques is not feasible, therefore we present a new method of using RS in conjunction with GA. Due to using RS, we do not necessarily need to run the neutronics simulation for all the inputs generated from the GA module rather, run the simulations for a predefined set of inputs, build a multivariate regression fit to the input and the output parameters, and then use this fit to predict the output parameters for the inputs generated by GA. The reactor parameters are given by the, radius of a fuel pin cell, isotopic enrichment of the fissile material in the fuel, mass flow rate of the coolant, and temperature of the coolant at the core inlet. And, the optimization objectives for the reactor core are, high breeding of U-233 and Pu-239 in desired power peaking limits, desired effective and infinite neutron multiplication factors, high fast fission factor, high thermal efficiency in the conversion from thermal energy to electrical energy using the Brayton cycle, and high fuel burn-up. It is to be noted that we have kept the total mass of the fuel as constant. In this work, we present a module based (modular) approach to perform the optimization wherein, we have defined the following modules: single fuel pin cell, whole core, thermal–hydraulics, and energy conversion. In each of the modules we have defined a specific set of parameters and optimization objectives. The GA system (GAS), and RS together, play the role of optimizing each of the individual modules, and integrating the modules to determine the final nuclear reactor core. However, implementation of GA could lead to a local minimum or a non-unique set of parameters, those meet the specific optimization objectives. The GA code is built using Java, neutronic analysis using MCNP6, thermal–hydraulics calculations using Java, and regression analysis using R.