Optimization of Pressurized Water Reactor Shuffling by Simulated Annealing with Heuristics

Stevens, John G.; Smith, Kord; Rempe, K.R.; Downar, Thomas J.

doi:10.13182/nse121-67

Cited by 88 publications

(15 citation statements)

References 4 publications

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“…Examples of systems in which simulated annealing is employed are the FORMOSA suite of codes [12], the XIMAGE/SIMAN graphical fuel management and loading pattern optimisation suite [13], and the ROSA software package [9]. Similarly, examples of systems in which a genetic algorithm is employed as the metaheuristic are the CIGARO system [14] and the more extensive GARCO package [7].…”

Section: Introductionmentioning

confidence: 99%

An Optimisation-Based Decision Support System Framework for Multi-Objective in- Core Fuel Management of Nuclear Reactor Cores

Schlünz

Bokov

Vuuren

2016

SAJIE

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Section: Introductionmentioning

confidence: 99%

An Optimisation-Based Decision Support System Framework for Multi-Objective in- Core Fuel Management of Nuclear Reactor Cores

Schlünz

Bokov

Vuuren

2016

SAJIE

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“…algorithm 1 has been used widely [2][3][4] because of the powerful feature of SA that prevents the solver from being trapped in local minima by allowing moves to worse states, let alone to better states. In the single-objective SA~SOSA!…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Loading Pattern Optimization by Simulated Annealing Employing Discontinuous Penalty Function and Screening Technique

Park

Joo

Kim

2009

Nuclear Science and Engineering

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-The problem of multiobjective fuel loading pattern (LP) optimization employing high-fidelity three-dimensional (3-D) models is resolved by introducing the concepts of discontinuous penalty function, dominance, and two-dimensional (2-D)-based screening into the simulated annealing (SA) algorithm. Each constraint and objective imposed on a reload LP design is transformed into a discontinuous penalty function that involves a jump to a quadratic variation

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“…[1][2][3] During the past decade, stochastic optimization methods such as the simulated annealing, the genetic algorithm, the evolutionary algorithm and the tabu search were successfully applied to actual incore fuel management optimization problems. [4][5][6][7][8][9][10][11][12][13][14][15][16][17] Origin of these optimization methods is not very new, but they were remaining in the academic area for a long time since these methods require considerable computational resources. Thanks to improvement of computer hardware, the stochastic optimization methods were winning their admiration not only in academia but also industry.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Loading Patterns Optimization for Two Successive Cycles of Pressurized Water Reactors

Yamamoto

Sugimura

Kitamura

et al. 2004

Journal of Nuclear Science and Technology

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In this paper, simultaneous optimization is carried out for successive two cycles of pressurized water reactors. At first, a simplified problem of the simultaneous optimization was studied by assuming the batch-wise power sharing as independent variable, i.e., batch-wise power sharing was optimized without considering corresponding loading patterns. The optimization of the batch-wise power sharing was carried out for the conventional single cycle, the equilibrium cycle and the two successive (tandem) cycles. The analysis indicated that the tandem cycle optimization well reproduce that of the equilibrium cycle optimization, which is considered as a typical case of the true multicycle optimization. Next, simultaneous optimization of loading patterns for tandem cycles is carried out using the simulated annealing method. Since the design space of the tandem cycles optimization is much larger than that of the conventional single cycle optimization, the optimization condition (i.e., number of calculated patterns) are established through sensitivity study. The optimization results are compared with those obtained by the successive single cycle optimizations and it is clarified that the successive single cycle optimization well reproduces the optimization results obtained by the simultaneous optimization if objective functions are appropriately chosen. The above result will be encouraging for the current in-core optimization method since single cycle optimization is utilized due to limitation of computation time.

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Optimization of Pressurized Water Reactor Shuffling by Simulated Annealing with Heuristics

Cited by 88 publications

References 4 publications

An Optimisation-Based Decision Support System Framework for Multi-Objective in- Core Fuel Management of Nuclear Reactor Cores

An Optimisation-Based Decision Support System Framework for Multi-Objective in- Core Fuel Management of Nuclear Reactor Cores

Multiobjective Loading Pattern Optimization by Simulated Annealing Employing Discontinuous Penalty Function and Screening Technique

Simultaneous Loading Patterns Optimization for Two Successive Cycles of Pressurized Water Reactors

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