Application of Differential Evolution algorithms to multi-objective optimization problems in mixed-oxide fuel assembly design

Abstract

Multi-objective optimization of nuclear engineering fuel assembly design problems is particularly difficult due to the highly non-linear interactions of a large number of possible variables. In addition, effective optimization algorithms are often highly problem-dependent and require extensive tuning, which reduces their applicability to the real world. To address this issue, Differential Evolution (DE) algorithms have been proposed as a new and effective method for heterogeneous fuel assembly optimization design problems. This paper presents the first complete study to investigate their applicability and performance. Firstly, two multiobjective DE algorithms have their performance compared against an Evolutionary Algorithm (EA) from the literature in optimizing a CORAIL mixed-oxide (MOX) fuel assembly for maximum plutonium content and minimum power peaking factor. Statistical analysis of the results shows the DE algorithms exhibit superior performance to the EA. The DE algorithms are then used to optimize a MOX fuel assembly with gadolinia poison, with results showing DE produces assembly designs comparable in performance to those in the literature. Finally, a sensitivity study is conducted on the control parameters of the better performing of the DE algorithms. Results indicate DE performance remains consistent for a wide range of values of both control parameters, suggesting the algorithm is able to perform effectively without requiring user expertise or effort to find the 'optimal' control parameter settings.

Intelligent Decision System of Higher Educational Resource Data under Artificial Intelligence Technology

“…The typical DEA is similar to genetic algorithms (GA), and also involves three kinds of operations: selection, crossover, and mutation [10]. If the expression of an individual is xi, G, where G is an algebra.…”

“…Then the values of ok and tk after the deviation correction can be calculated by equation (10) and equation ( 11):…”

Multi-objective, multi-physics optimization of 3D mixed-oxide LWR fuel assembly designs using the MOJADE algorithm

“…MOJADE was developed to investigate the performance of DE algorithms on optimization problems in nuclear engineering, and was first presented in (Charles and Parks, 2017), where it was shown to outperform a conventional genetic algorithm. It has since been shown to be effective on more complex problems and its performance is relatively insensitive to changes in its control parameters (Charles and Parks, 2018), making it a suitable choice for the multi-physics design problems under consideration. MOJADE was implemented in C++ and has two major modifications to allow optimization within a multi-objective environment.…”

“…Furthermore, the problems are based on real-world multi-physics fuel assembly design challenges concerning two of the latest reactor designs on the market: the European Pressurized Reactor (EPR) by Orano and the Advanced Boiling Water Reactor (ABWR) by Hitachi GE. Optimization was performed using the multi-objective DE algorithm MOJADE, which has already demonstrated both its effectiveness and its insensitivity (discussed in Section 2 below) to different nuclear engineering fuel assembly optimization problems (Charles and Parks, 2018). MOJADE was combined with a development version of the analysis package WIMS (Lindley et al, 2015) to evaluate the reactor physics and thermal hydraulics performance of the algorithm-generated solutions.…”

“…The rest of the paper is laid out as follows. Section 2 explains the concept of differential evolution and the multi-objective form of the JADE algorithm, developed previously in (Charles and Parks, 2018). Sections 3 and 4 detail the two complex and realistic problems in the nuclear engineering design of LWR fuel assemblies on which the algorithm was tested.…”