Two new multi-objective differential evolution (DE) algorithms are used to optimize heterogeneous low-enriched uranium + mixed oxide fuel assemblies for use in a pressurized water reactor. The objectives were to maximize plutonium content and minimize the power peaking factor. A performance comparison to a genetic algorithm is used to evaluate the applicability of DE algorithms to nuclear fuel assembly design optimization problems. Results show that DE performs highly competitively against a more established algorithm and can arguably better represent the trade-off between both objectives through greater variety in the number of different pin arrangements explored and a higher reliability in finding the ‘true’ Pareto-front.

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.

No abstract

The ANSWERS® WIMS reactor physics code is being developed for whole core multiphysics modelling. The established neutronics capability for lattice calculations has recently been extended to be suitable for whole core modelling of Small Modular Reactors (SMRs). A whole core transport, SP3 or diffusion flux solution is combined with fuel assembly resonance shielding and pin-by-pin differential depletion. An integrated thermal hydraulic solver permits differential temperature and density variations to feedback to the neutronics calculation. This paper presents new methodology developed in WIMS to couple the core neutronics to the integrated core thermal hydraulics solver. Two coupling routes are presented and compared using a challenging PWR SMR benchmark. The first route, called GEOM, dynamically calculates the resonance shielding and homogenisation with the whole core flux solution. The second coupling route, called CAMELOT, separates the resonance shielding and pincell homogenisation from the whole core solution via generating tabulated cross sections. Both routes can use the MERLIN homogenised pin-by-pin whole core flux solver and couple to the same integrated thermal hydraulic solver, called ARTHUR. Heterogeneous differences between the neutronics and thermal hydraulics are mapped via thermal identifiers for neutronics materials and thermal regions. The ability for the integrated thermal hydraulic solver to call an external code via a Fortran-C-Python (FCP) interface is also summarised. This flexible external coupling permits one way coupling to an external fuel performance code or two way coupling to an external thermal hydraulic code.

No abstract