Comparison of heuristic optimization techniques for the enrichment and gadolinia distribution in BWR fuel lattices and decision analysis

Castillo, Alejandro; Martín‐del‐Campo, Cecilia; Montes-Tadeo, José-Luis; François, Juan‐Luis; Ortiz-Servin, Juan José; Perusquía-del-Cueto, Raúl

doi:10.1016/j.anucene.2013.08.039

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…These results are consistent with the results of the work of others and their optimization analyses. The GA is well known for a consistent global performance compared to other optimization algorithms [22], but the GA is also known for not reaching the most optimal solution within the solution space [28]. The results also reveal the power of the SA algorithm-that is, its best result outperformed that from the GA by nearly 20 EFPD.…”

Section: Optimization Methods and Resultsmentioning

confidence: 97%

“…Similarly, while developing their specialized objective function, Park et al updated their parallel SA algorithm to improve on issues encountered in their optimization and objective function [20,21]. Some studies have also compared many different optimization algorithms to test their pin lattice and core loading pattern optimization performance [22,23].…”

Section: Introductionmentioning

confidence: 99%

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MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems

Andersen¹,

Delipei²,

Kropaczek³

et al. 2022

Preprint

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A variety of optimization algorithms have been developed to solve engineering design problems in which the solution space is too large to manually determine the optimal solution. The Modular Optimization Framework (MOF) was developed to facilitate the development and application of these optimization algorithms. MOF is written in Python 3, and it uses object-oriented programming to create a modular design that allows users to easily incorporate new optimization algorithms, methods, or engineering design problems into the framework.Additionally, a common input file allows users to easily specify design problems, update the optimization parameters, and perform comparisons between various optimization methods and algorithms. In the current MOF version, genetic algorithm (GA) and simulated annealing (SA) approaches are implemented. Applications to different nuclear engineering optimization problems are included as examples. The effectiveness of the GA and SA optimization

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Section: Optimization Methods and Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems

Andersen¹,

Delipei²,

Kropaczek³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The use of the above mentioned heuristic techniques in the BWR fuel lattice design has been reported by Castillo et al (2014). In this work a performance comparison of all these techniques was made.…”

Section: Heuristic Techniquesmentioning

confidence: 98%

“…Recently, the same problem was solved by Chaung and tung-Hsien (2012) applying a particle swarm optimization algorithm and local search; in this case the local power peaking factor was minimized searching for the best radial U-235 enrichment and gadolinia distribution. Also, a recent comparison among different heuristic techniques to solve this problem has been published by Castillo et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Searching for enrichment and gadolinia distributions in BWR fuel lattices through a Heuristic-Knowledge Method

Montes-Tadeo

Perusquía-del-Cueto

Martín‐del‐Campo

et al. 2015

Progress in Nuclear Energy

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“…In this research, the chosen GA method and path relinking were coupled to scatter search, which shows the best results in terms of global cost [3]. In this regard, a modular optimization framework (MOF) was proposed to facilitate the application of other optimization methods, as well as the GA using object-oriented programming [4].…”

Section:  Can Handle Both Constrained and Unconstrained Problemsmentioning

confidence: 99%

Development and Demonstration of a Risk-Informed Approach to the Regulatory Required Fuel Reload Safety Analysis

Choi

Abdo

Palamone

et al. 2022

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The United States (U.S.) nuclear industry is facing a strong challenge to maintain regulatory required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor (LWR) nuclear power plants (NPPs) and can be achieved more economically by using a risk-informed ecosystem, such as the one being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program is promoting a wide range of research and development (R&D) activities with the goal to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering second license renewal.The RISA Pathway has two main goals: (1) the deployment of methodologies and technologies that enable better representation of safety margins and the factors that contribute to cost and safety; and (2) the development of advanced applications that enable cost-effective plant operation.The plant reload optimization framework development project aims to build a reactor core designing tool that integrates reactor safety and fuel performance analyses and uses artificial intelligence to support optimization of core design solutions.This report summarizes the following activities that were successfully performed in fiscal year (FY)-2022:  Enhancement of a single objective genetic algorithm (GA)-based optimization framework by applying evolution, mutation, survivor, and constraints control methods. Investigation of new experiment data and water droplet models to improve uncertainty analysis in a reflood phenomenon during a loss of coolant accident (LOCA).

show abstract

Comparison of heuristic optimization techniques for the enrichment and gadolinia distribution in BWR fuel lattices and decision analysis

Cited by 12 publications

References 13 publications

MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems

MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems

Searching for enrichment and gadolinia distributions in BWR fuel lattices through a Heuristic-Knowledge Method

Development and Demonstration of a Risk-Informed Approach to the Regulatory Required Fuel Reload Safety Analysis

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