Two stochastic optimization algorithms applied to nuclear reactor core design

Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck’s Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak factor in a three-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. Results show that the PCA and the GDA perform very well compared to the canonical Genetic Algorithm and its variants, and also to Simulated Annealing, hence demonstrating their potential for other optimization applications

Doctor of Philosophy

dissertationNuclear research reactors are found throughout the world and have been crucial in the advancement of scientific and engineering discoveries but the majority are approaching operational ages that require a renewed focus on safely maintaining and optimizing their use. A novel multilevel safety-factors-centered framework for the optimization and utilization of aging research reactors has been developed that can be implemented at any research reactor facility. The framework consists of an optimization tool for neutron activation analysis (NAA) and irradiation experiments, an optimization system, DACOS, for optimizing reactor operation parameters, and the overall Engineering Safety Culture ideology. The selection of NAA experimental parameters for irradiation in research reactors is essential in lowering the radiation dose to personnel while also minimizing the generation of excessive radioactive products. This comes in competition with assuring that enough activity of an examined sample is produced in order to be able to measure targeted trace nuclei. This is accomplished by coupling a NAA precalculator tool, PyNIC, with the optimization tool, DAKOTA, creating the PyNIC-DAKOTA tool system. This PyNIC-DAKOTA tool system is used to determine the optimal parameters for NAA. The PyNIC-DAKOTA tool system is benchmarked with several examples using the University of Utah TRIGA Reactor (UUTR). The PyNIC-DAKOTA tool system shows expected agreement with the actual NAA experiments. DACOS is a newly developed computational optimization system that merges well-known neutron transport code AGENT and well-known optimization tool DAKOTA. The DACOS can be applied to any reactor configuration for the purpose of optimizing its operation parameters such as but not limited to determining the optimal fuel composition and spatial distribution, amount and position of reflectors and neutron absorbing materials to achieve a specified neutron flux at a given location in the reactor or reactor power level. DACOS demonstrations of application are given for modeling of the UUTR. All of the research reactor optimizations and improvements are housed under the umbrella of a newly formed concept of Engineering Safety Culture and the workflow process that it encompasses. This new ideology is presented with illustrative examples of its implementation and resulting benefits

Nuclear Power

The world of the twenty first century is an energy consuming society. Due to increasing population and living standards, each year the world requires more energy and new efficient systems for delivering it. Furthermore, the new systems must be inherently safe and environmentally benign. These realities of today's world are among the reasons that lead to serious interest in deploying nuclear power as a sustainable energy source. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. The goal of the book is to show the current state-of-the-art in the covered technical areas as well as to demonstrate how general engineering principles and methods can be applied to nuclear power systems

Multiobjective in-core fuel management optimisation for nuclear research reactors

Thesis (PhD)--Stellenbosch University, 2016.ENGLISH SUMMARY : The efficiency and effectiveness of fuel usage in a typical nuclear reactor is influenced by the specific arrangement of available fuel assemblies in the reactor core positions. This arrangement of assemblies is referred to as a fuel reload configuration and usually has to be determined anew for each operational cycle of a reactor. Very often, multiple objectives are pursued simultaneously when designing a reload configuration, especially in the context of nuclear research reactors. In the multiobjective in-core fuel management optimization (MICFMO) problem, the aim is to identify a Pareto optimal set of compromise or trade-off reload configurations. Such a set may then be presented to a decision maker (i.e. a nuclear reactor operator) for consideration so as to select a preferred configuration. In the first part of this dissertation, a secularization-based methodology for MICFMO is pro- posed in order to address several shortcomings associated with the popular weighting method often employed in the literature for solving the MICFMO problem. The proposed methodology has been implemented in a reactor simulation code, called the OSCAR-4 system. In order to demonstrate its practical applicability, the methodology is applied to solve several MICFMO problem instances in the context of two research reactors. In the second part of the dissertation, an extensive investigation is conducted into the suitability of several multiobjective optimization algorithms for solving the constrained MICFMO problem. The computation time required to perform the investigation is reduced through the usage of several artificial neural networks constructed in the dissertation for objective and constraint function evaluations. Eight multiobjective metaheuristics are compared in the context of a test suite of several MICFMO problem instances, based on the SAFARI-1 research reactor in South Africa. The investigation reveals that the NSGA-II, the P-ACO algorithm and the MOOCEM are generally the best-performing metaheuristics across the problem instances in the test suite, while the MOVNS algorithm also performs well in the context of bi-objective problem instances. As part of this investigation, a multiplicative penalty function (MPF) constraint handling technique is also proposed and compared to an existing constraint handling technique, called constrained-domination. The comparison reveals that the MPF technique is a competitive alternative to constrained-domination. In an attempt to raise the level of generality at which MICFMO may be performed and potentially improve the quality of optimization results, a multiobjective hyperheuristic, called the AMALGAM method, is also considered in this dissertation. This hyperheuristic incorporates multiple metaheuristic sub-algorithms simultaneously for optimization. Testing reveals that the AMALGAM method yields superior results in the majority of problem instances in the test suite, thus achieving the dual goal of raising the level of generality and of yielding improved optimization results. The method has also been implemented in the OSCAR-4 system and is applied to solve several MICFMO case study problem instances, based on two research reactors, in order to demonstrate its practical applicability. Finally, in the third part of this dissertation, a conceptual framework is proposed for an optimization-based personal decision support system, dedicated to MICFM. This framework may serve as the basis for developing a computerized tool to aid nuclear reactor operators in designing suitable reload configurations.AFRIKAANSE OPSOMMING : Die doeltreffendheid en doelmatigheid van brandstofverbruik in 'n tipiese kernreaktor word deur die spesieke rangskikking van beskikbare brandstofelemente in die laaiposisies van die reaktor beinvloed. Hierdie rangskikking staan bekend as 'n brandstof herlaaikongurasie en word gewoonlik opnuut bepaal vir elke operasionele siklus van 'n reaktor. Die gelyktydige optimering van veelvuldige doele word dikwels tydens die ontwerp van 'n herlaaikongurasie nagestreef, veral binne die konteks van navorsingsreaktore. Die doelwit van meerdoelige binne-kern brandstofbeheeroptimering (MBKBBO) is om 'n Pareto optimale versameling van herlaaikongurasieafruilings te identiseer. So 'n versameling mag dan vir oorweging (deur byvoorbeeld 'n kernreaktoroperateur) voorgele word sodat 'n voorkeurkongurasie gekies kan word. In die eerste gedeelte van hierdie proefskrif word 'n skalariseringsgebaseerde metodologie vir MBKBBO voorgestel om verskeie tekortkominge in die gewilde gewigverswaringsmetode aan te spreek. Laasgenoemde metode word gereeld in die literatuur gebruik om die MBKBBO probleem op te los. Die voorgestelde metodologie is in 'n reaktorsimulasiestelsel, bekend as die OSCAR-4 stelsel, geimplementeer. Om die praktiese toepasbaarheid daarvan te demonstreer, word die metodologie gebruik om 'n aantal MBKBBO probleemgevalle binne die konteks van twee navorsingsreaktore op te los. In die tweede gedeelte van die proefskrif word 'n uitgebreide ondersoek ingestel om die geskiktheid van verskeie meerdoelige optimeringsalgoritmes vir die oplos van die beperkte MBKBBO probleem te bepaal. Die berekeningstyd wat vir die ondersoek benodig word, word verminder deur die gebruik van kunsmatige neurale netwerke, wat in die proefskrif gekonstrueer word, om doelfunksies en beperkings te evalueer. Agt meerdoelige metaheuristieke word binne die konteks van verskeie MBKBBO toetsprobleemgevalle vergelyk wat op die SAFARI-1 navorsingsreaktor in Suid-Afrika gebaseer is. Toetse dui daarop dat die NSGA-II, die P-ACO algoritme en die MOOCEM oor die algemeen die beste oor al die toetsprobleemgevalle presteer. Die MOVNS algoritme presteer ook goed in die konteks van tweedoelige probleemgevalle. 'n Vermenigvuldigende boetefunksie (VBF) beperkinghanteringstegniek word ook voorgestel en vergelyk met 'n bestaande tegniek bekend as beperkte dominasie. Daar word bevind dat the VBF tegniek 'n mededingende alternatief tot beperkte dominasie is. 'n Poging word aangewend om die vlak van algemeenheid waarmee MBKBBO uitgevoer word, te verhoog, asook om potensieel die kwaliteit van die optimeringsresultate te verbeter. 'n Meerdoelige hiperheuristiek, bekend as die AMALGAM metode, word in die nastreef van hierdie twee doelwitte oorweeg. Die metode funksioneer deur middel van die gelyktydige insluiting van 'n aantal metaheuristieke deel-algoritmes. Toetse dui daarop dat the AMALGAM metode beter resultate vir die meerderheid van toetsprobleme lewer, en dus word die bogenoemde twee doelwitte bereik. Die metode is ook in the OSCAR-4 stelsel ge mplementeer en word gebruik om 'n aantal MBKBBO gevallestudie probleemgevalle (binne die konteks van twee navorsingsreaktore) op te los. Sodoende word die praktiese toepasbaarheid van die metode gedemonstreer. In die derde deel van die proefskrif word 'n konseptuele raamwerk laastens vir 'n optimeringsgebaseerde persoonlike besluitsteunstelsel gemik op MBKBB, voorgestel. Hierdie raamwerk mag as grondslag dien vir die ontwikkeling van 'n gerekenariseerde hulpmiddel vir kernreaktoroperateurs om aanvaarbare herlaaikongurasies te ontwerp.Doctora

Nuclear Power

The world of the twenty first century is an energy consuming society. Due to increasing population and living standards, each year the world requires more energy and new efficient systems for delivering it. Furthermore, the new systems must be inherently safe and environmentally benign. These realities of today's world are among the reasons that lead to serious interest in deploying nuclear power as a sustainable energy source. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. The goal of the book is to show the current state-of-the-art in the covered technical areas as well as to demonstrate how general engineering principles and methods can be applied to nuclear power systems