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An in-core fuel reload design tool using the improved pivot particle swarm method was developed for the loading pattern optimization problems in a typical PWR, such as Daya Bay Nuclear Power Plant. The discrete, multi-objective improved pivot particle swarm optimization, was integrated with the in-core physics calculation code ‘donjon’ based on fin...
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Generalized perturbation theory (GPT) is a mean to evaluate eigenvalue and reaction rates variations due to small changes in the lattice properties. It requires the evaluation of the changes in the neutron production and loss operators resulting from macroscopic cross sections variations. In fact these changes can be decomposed in two terms: a dire...
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... An improvement to the method presented in [139] proposed an improved Pivot Particle Swarm Optimization approach. This did indeed show some improvements. ...
Periodically, nuclear Pressurized Water Reactors need to have a proportion of their fuel removed, new fuel added, and the remaining pattern reloaded in such a way as to yield the desired balance of operational considerations. This loading pattern then remains in the reactor until the next reloading event. The subtleties in the calculations of physical properties and the high degree of sensitivity to changes make this a highly complex combinatorial optimization problem. The methods that have historically been used to make the decisions about nuclear reactor loading pattern optimization are increasingly supplemented by computational methods. This work assessed one such method’s ability to optimize multiple objectives simultaneously – multi-objective Tabu Search. It was statistically analysed in comparison to other common leading methods – notably the Genetic Algorithm. It was tested on real reactor models using realistic data provided by a utility. The Tabu Search was first tuned via sensitivity studies to ensure a fair comparison in that both algorithms are near optimally configured. The focus of the work is light water reactors, both standard and small modular size, and it will not look at other reactor types. The objectives chosen reflect a range of the possible calculations. The principal aim is to establish whether the single- and then multi-objective Tabu Search can produce comparable, better, or worse optimization sets than its main competitor, the Genetic Algorithm, when applied to this loading pattern optimization problem. It was found that, although the Tabu Search outperformed the current industry standard algorithms for single-objective runs, the multi-objective results, although comparable, were more mixed. This work discovered that the Tabu Search for the in-core loading pattern optimization is still effective when single objective searches are not restricted, for example, by generalized perturbation theory. The set up, for both single objectives and multi-objective problems, is robust in terms of the choice of configuration. However, on multi-objective search spaces the inherent discontinuities in the search space mean that the confusion in which direction along the search space to traverse means that the population based methods still out perform the method.
... Early on, deterministic approaches such as linear [2], quadratic [3], dynamic [4], and approximation [5] programming were used. These have largely been supplanted by heuristic methods such as simulated annealing [6], genetic algorithms [7][8][9], and others [10]. In many instances there are particular details unique to the specific problem at hand that indicate different approaches, methods, and algorithms to achieve the desired outcome. ...
... DAKOTA [11] is perhaps the most generalized tool currently used for nuclear fuel optimization [8]; it offers several gradient and nongradientbased methods for optimization. Despite the many capabilities of DAKOTA, many opt for the flexibility of developing their own methods and codes for their specific applications [9,10]. Using in-house code focused on the target problem enables more tuning and control over fine-grain details, which can ultimately improve code capabilities and functionality. ...
The Versatile Test Reactor (VTR) is expected to operate in a persistent non-equilibrium state due to inter-cycle variations in experimental loading. The goal of planning and optimizing the fuel loading for this mode of operation can differ from equilibrium cycle optimization. In this work, a general algorithm for optimizing a core reload of a fast reactor with respect to some objective function is developed. The objective function used in this work is a preliminary model that is defined to capture most of the core parameters expected to be of interest, but elements could be added or subtracted as needed for different types of problems. The optimization method is a discrete evolutionary algorithm. Instead of using diffusion or transport to evaluate each potential core configuration that is considered during the execution of the optimization method, the necessary inputs to the objective function (k-effective and assembly power distribution) are evaluated approximately by treating the reloaded configuration as a small change to the previous configuration, for which a diffusion or transport solution has already been calculated. This approximate calculation facilitates evaluation of the objective function for several hundred potential configurations without a neutron transport solution, which would be a significant bottleneck in the optimization method. In the results, the evolutionary algorithm demonstrates good responsiveness to the tuning of the parameters of the objective function.
... Artificial Bee Colony In-Core fuel management -Artificial bee colony with random keys is superior to genetic algorithm and particle swarm optimisation Montes et al. (2011) Ant colony system Fuel lattice design -Ant colony technique is a potent algorithm to find for the best fuel arrangement pattern Lin and Lin (2012) Particle swarm optimisation Fuel lattice design -Fuel lattice composition is designed automatically Pazirandeh and Tayefi (2012) Artificial neural network Fuel management -Radial, axial, and total power peaking factor are improved Liu and Cai (2012) Pivot particle swarm Fuel loading pattern -Optimised arrangement proved to have advantage in economic efficiency and safety Lin and Hung (2013) Particle swarm optimisation ...
... The object core used is a generic PWR with a power of 1000 MWe (AP1000). The core is composed of 192 fuel assembly, for this reason, several fuel management schemes are needed which include fuel arrangement, core calculation and operational safety and economic aspects (5,6). Burnup calculations in core fuel management on the PWR performed in two modes. ...
The in-core fuel management of a nuclear power plant is a problem of optimization of core parameters such as operation cycle, average fuel burnup and shut down margin for determining a fuel loading pattern to meet the safety and economic aspects. The study is aimed to obtain an optimal fuel loading pattern. Two models of fuel burn up calculations were taken namely equilibrium and transition cores burn up models. The calculations will be carried out by means of computer codes SRAC2006 for cell calculation and PWR-FUEL for the fuel management. The results of keff values at BOC and EOC for each transition core are approximately 1.05 as the input data and the core cycle length is found to be 330 days. The keff values at both BOC and EOC are very near to critical at equilibrium core and the core cycle length is found 360 days. The results of the calculation of neutron flux distribution and power density using the NODAL and FDM methods of the PWR-FUEL the code has the same results. From the results of the neutronic parameter, it is shown that the optimal loading pattern of PWR core can be determined by the PWR-FUEL code either with equilibrium core search or with transition core burnup models. Key words: fuel loading pattern, PWR-FUEL code, operation safety.
... Metaheuris- tics have, in particular, emerged as the most prominent solution tech- niques applicable to the problem. Examples thereof include simulated annealing [4], genetic algorithms [7], particle swarm optimisation [8], ant colony optimisation [1] and tabu search [9]. Apart from solution techniques, research efforts have also been aimed toward reducing the computational cost associated with function evaluations in the ICFMO problem. ...
This paper is concerned with the problem of constrained multiobjective in-core fuel management optimisation (MICFMO) using, for the first time, a hyperheuristic technique as solution approach. A multiobjective hyperheuristic called the AMALGAM method (an evolutionary-based technique incorporating multiple sub-algorithms simultaneously) is compared to three previously-studied metaheuristics, namely the nondominated sorting genetic algorithm II, the Pareto ant colony optimisation algorithm and the multiobjective optimisation using cross-entropy method, in an attempt to improve upon the level of generality at which MICFMO may be conducted. This solution approach was motivated by a lack of consistent performance by the aforementioned metaheuristics when applied in isolation. Comparisons are conducted in the context of a test suite of several problem instances based on the SAFARI-1 nuclear research reactor. Nonparametric statistical analyses in respect of the optimisation results reveal that the AMALGAM method significantly outperforms the three metaheuristics in the majority of problem instances within the test suite. Additional comparisons are also performed between the proposed AMALGAM method and a randomised (or no-learning) version thereof, as well as a selection choice function-based multiobjective hyperheuristic available in the literature. It is found that the proposed method is superior to the choice function-based algorithm within the context of the MICFMO test suite, and yields results of similar quality when compared to its randomised version. The practical relevance of the hyperheuristic results is further demonstrated by comparing the solutions thus obtained to a reload configuration designed according to the current fuel assembly reload design approach followed at the SAFARI-1 reactor.
... It is impossible to evaluate power profiles for all loading patterns (LPs) exhaustively. Therefore, recent computational intelligence techniques have been applied to obtain optimum core-loading pattern, as discussed below [1][2][3][4][5][6][7][8]. ...
... On the other hand, Liu et al. [6] proposed improved pivot particle swarm optimization method and obtained 9.6% increase in multiplication factor, while decreasing power peaking factor by 0.6% for first core loading of Daya Bay nuclear power plant. Karahroudi et al. [7] performed multi-objective optimization of first core of nuclear reactor by using genetic algorithm. ...
Fuel reload pattern optimization is essential for attaining maximum fuel burnup for minimization of generation cost while minimizing power peaking factor (PPF). The aim of this work is to carry out detailed assessment of particle swarm optimization (PSO) in the context of fuel reload pattern search. With astronomically large number of possible loading patterns, the main constraints are limiting local power peaking factor, fixed number of assemblies, fixed fuel enrichment, and burnable poison rods. In this work, initial loading pattern of fixed batches of fuel assemblies is optimized by using particle swarm optimization technique employing novel feature of varying inertial weights with the objective function to obtain both flat power profile and cycle keff > 1. For neutronics calculation, PSU-LEOPARD-generated assembly depletion-dependent group-constant-based ADD files are used. The assembly data description file generated by PSU-LEOPARD is used as input cross-section library to MCRAC code, which computes normalized power profile of all fuel assemblies of PWR nuclear reactor core. The standard PSO with varying inertial weights is then employed to avoid trapping in local minima. A series of experiments have been conducted to obtain near-optimal converged fuel-loading pattern of 300 MWe PWR Chashma reactor. The optimized loading pattern is found in good agreement with results found in literature. Hybrid scheme of PSO with simulated annealing has also been implemented and resulted in faster convergence.
... Among primary approaches, simulated annealing (SA), (Smuc et al., 1994), and genetic algorithm (GA), (Fatih et al., 2008) developed to treat the loading pattern optimization problem. But in the recent years, new optimization methods also have been applied to the LPO of nuclear reactors such as: improved pivot particle swarm method, (Liu and Cai, 2012), ant colony algorithm, (Lin and Lin, 2012), self-adaptive global best harmony search algorithm, (Poursalehi et al., 2013a), robust optimization approach, (Pelykh et al., 2013), differential harmony search algorithm, (Poursalehi et al., 2013b), self-adaptive quantum population-based incremental learning algorithm, (Silva and Schirru, 2014), novel crossover genetic algorithms (GA) and hybrid GA(SA) schemes, (Zameer et al., 2014), bi-parametric method, (Perusquía et al., 2014), bat algorithm, (Kashi et al., 2014), cross entropy scheme, (Meneses and Schirru, 2015), evolutionary harmony search algorithm, (Poursalehi, 2015), tabu search, (Hill and Parks, 2015), electromagnetism mechanism, (Poursalehi et al., 2016), and etc. By means of the above methods, issues like cycle burn up, reactivity, power peaking factor and costs of reactors fuel can be elected as objective functions. ...
The fuel loading pattern optimization is an important process in the refueling design of a nuclear reactor core. Also the analysis of reactor core performance during the operation cycle can be a significant step in the core loading pattern optimization (LPO). In this work, for the first time, a new method i.e. cuckoo search algorithm (CS) has been applied to the fuel loading pattern design of Bushehr WWER-1000 core. In this regard, two objectives have been chosen for finding the best configuration including the improvement of operation cycle length associated with flattening the radial power distribution of fuel assemblies. The core pattern optimization has been performed by coupling the CS algorithm to thermal-neutronic codes including PARCS v2.7, COBRA-EN and WIMSD-5B for earning desired parameters along the operation cycle. The calculations have been done for the beginning of cycle (BOC) to the end of cycle (EOC) states. According to numerical results, the longer operation cycle for the semi-optimized loading pattern has been achieved along with less power peaking factor (PPF) in comparison to the original core pattern of Bushehr WWER-1000. Gained results confirm the efficient and suitable performance of the developed program and also the introduced CS method in the LPO of a nuclear WWER type.
... In most research papers claiming to perform MICFMO, scalarising approaches involving linear weighted sum aggregations of the objectives are adopted, which only yield a single solution [19][20][21]. Apart from the serious shortcomings associated with these approaches (e.g. the inability to uncover a Pareto optimal solution if the problem is nonconvex and the misleading role of weights [22]), the manner in which they were applied does not solve the MICFMO problem in terms of finding a Pareto optimal set of solutions. For this reason, we restrict this overview to MICFMO approaches in the literature involving "true" multiobjective optimisation. ...
In this paper, the topic of constrained multiobjective in-core fuel management optimisation (MICFMO) using metaheuristics is considered. Several modern and state-of-the-art metaheuristics from different classes, including evolutionary algorithms, local search algorithms, swarm intelligence algorithms, a probabilistic model-based algorithm and a harmony search algorithm, are compared in order to determine which approach is most suitable in the context of constrained MICFMO. A test suite of sixteen optimisation problem instances, based on the SAFARI-1 nuclear research reactor, has been established for the comparative study. The suite is partitioned into three classes, each consisting of problem instances having a different number of objectives, but subject to the same stringent constraint set. The effectiveness of a multiplicative penalty function constraint handling technique is also compared with the constrained-domination technique from the literature. The different optimisation approaches are compared in a nonparametric statistical analysis. The analysis reveals that multiplicative penalty function constraint handling is a competitive alternative to constrained-domination, and seems to be particularly effective in the context of bi-objective optimisation problems. In terms of the metaheuristic solution comparison, it is found that the nondominated sorting genetic algorithm II (NSGA-II), the Pareto ant colony optimisation (P-ACO) algorithm and the multiobjective optimisation using cross-entropy method (MOOCEM) are generally the best-performing metaheuristics across all three problem classes, along with the multiobjective variable neighbourhood search (MOVNS) in the bi-objective problem class. Furthermore, the practical relevance of the metaheuristic results is demonstrated by comparing the solutions thus obtained to the current SAFARI-1 reload configuration design approach.
... If initial run, generate r R randomly between 0 and 1 but not 0, 0.25,0. (Liu & Cai, 2012). ...
The code and economics of refueling a live working reactor is discussed in this computer science thesis for a master's degree. Multiple systems were reviewed, as were methods. One was selected as the best, and this method is also able to be improved upon for further research.
... A multi-objective optimization with linear fitness function was performed and better results were achieved as compared to reference LP. Likewise, Liu et al. [5] reported that a better LP for Daya Bay Nuclear Power Plant was achieved as compared to the reference loading pattern by using multi-objective improved pivot particle swarm optimization. ...
Nuclear reactor core is the heart of a power plant producing power from fissile fuel fission. Refueling is needed periodically when it becomes impossible to maintain the reactor operating at nominal power as a result of fuel burn up. In PWR core reloading, attention is drawn to the configuration that meets safety requirements and minimizes energy cost. This paper focuses on finding the best core configuration for a typical two-loop, 300 MWe PWR satisfying the objectives of power peaking factor minimization to enhance safety of the reactor and maximization of multiplication factor to increase fuel burn up. Multi-objective optimization of the first core has been accomplished by implementing the batch composition preserving genetic algorithms (GA). Neutronic calculations and burn up analysis of the optimized loading patterns have been carried out using available reactor physics codes. It is found from this study that burn up of the optimized core has been extended by 48 effective full power days (EFPD's) while satisfying safety criterion by keeping power peaking factor below the reference value.
