Parallel Adaptive Evolutionary Algorithms for Pressurized Water Reactor Reload Pattern Optimizations

Axmann, Joachim K.

doi:10.13182/nt97-a35403

Cited by 18 publications

(10 citation statements)

References 12 publications

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“…The optimization parameters in Table 3 are set based on the experiences in the previous studies. [1][2][3][4][5][6][7][8] Total number of evaluate candidates (6,000) is not enough to obtain a "true optimum" solution, but it was sufficient to provide valuable solutions in actual in-core fuel management tasks.…”

Section: Sensitivity Study Of Dga Parametersmentioning

confidence: 99%

“…Recently, some stochastic optimization methods such as the simulated annealing, genetic algorithms (GA) and evolutionary algorithms are successfully applied to the practical in-core optimization problems [1][2][3][4][5][6][7][8] and are used as practical design tools. [9][10][11] However, since the stochastic optimization methods typically require several thousands evaluations of loading patterns to obtain a converged solution, computation time tends to be longer.…”

Section: Introductionmentioning

confidence: 99%

“…[9][10][11] However, since the stochastic optimization methods typically require several thousands evaluations of loading patterns to obtain a converged solution, computation time tends to be longer. There are several approaches to reduce computation time: development of quick core calculation method, 1,9) development of more efficient optimization algorithms, 12,13) application of parallel computing 8,[14][15][16] and so on.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of the Distributed Genetic Algorithm for In-Core Fuel Optimization Problems under Parallel Computational Environment.

Yamamoto¹,

Hashimoto²

2002

J. Nucl. Sci. Technol.

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The distributed genetic algorithm (DGA) is applied for loading pattern optimization problems of the pressurized water reactors. A basic concept of DGA follows that of the conventional genetic algorithm (GA). However, DGA equally distributes candidates of solutions (i.e. loading patterns) to several independent "islands" and evolves them in each island. Communications between islands, i.e. migrations of some candidates between islands are performed with a certain period. Since candidates of solutions independently evolve in each island while accepting different genes of migrants, premature convergence in the conventional GA can be prevented.Because many candidate loading patterns should be evaluated in GA or DGA, the parallelization is efficient to reduce turn around time. Parallel efficiency of DGA was measured using our optimization code and good efficiency was attained even in a heterogeneous cluster environment due to dynamic distribution of the calculation load. The optimization code is based on the client/server architecture with the TCP/IP native socket and a client (optimization) module and calculation server modules communicate the objects of loading patterns each other.Throughout the sensitivity study on optimization parameters of DGA, a suitable set of the parameters for a test problem was identified. Finally, optimization capability of DGA and the conventional GA was compared in the test problem and DGA provided better optimization results than the conventional GA.

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Section: Sensitivity Study Of Dga Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of the Distributed Genetic Algorithm for In-Core Fuel Optimization Problems under Parallel Computational Environment.

Yamamoto¹,

Hashimoto²

2002

J. Nucl. Sci. Technol.

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“…For this purpose, an interface program was developed for linking a standard industry black oil simulator to the Multipurpose Environment for Parallel Optimization (MEPO). This optimization environment has previously been applied to various scientific and industrial engineering problems [12][13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

Optimization Methods for History Matching of Complex Reservoirs

Schulze-Riegert¹,

Axmann²,

Haase³

et al. 2001

All Days

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractConventional direct optimization methods and Evolutionary Algorithms are applied to the problem of history matching in reservoir engineering. For the optimization of complex reservoir models the potential of parallel computing is investigated. Methods to improve the convergence of Evolutionary Algorithms by introducing expert knowledge is discussed. An interface program has been developed which links an industry standard reservoir simulator to an optimization software package designed as a multipurpose environment for parallel optimization. Permeabilities, fault transmissibilities as well as relative permeabilites and barrier locations have been included in the optimization. Results are presented for synthetic and real reservoirs with up to 30 wells and 40 design parameters. The potential and the area of applicability of the optimization method to the problem of reservoir modeling in various modeling phases are discussed. The improvement of performance based on parallelism in a network environment is evaluated. In conclusion, results suggest that Evolution Strategies can be successfully applied for generating possible solutions in the early modeling phase. The introduction of expert knowledge to the optimization methods is essential for reducing the multidimensional search space and improving convergence.

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“…Researchers are continuing to explore the potential of GAs 51,[58][59][60][61][62][63] and Evolution Strategies 64 for solving the PWR in-core nuclear fuel management problem. (Evolution Strategies 65 are similar to GAs but emphasize mutation rather than crossover as the primary means of development.)…”

Section: In-core Nuclear Fuel Managementmentioning

confidence: 99%

Advances in Nuclear Fuel Management for Light Water Reactors

Turinsky

Parks

Advances in Nuclear Science and Technology

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Parallel Adaptive Evolutionary Algorithms for Pressurized Water Reactor Reload Pattern Optimizations

Cited by 18 publications

References 12 publications

Application of the Distributed Genetic Algorithm for In-Core Fuel Optimization Problems under Parallel Computational Environment.

Application of the Distributed Genetic Algorithm for In-Core Fuel Optimization Problems under Parallel Computational Environment.

Optimization Methods for History Matching of Complex Reservoirs

Advances in Nuclear Fuel Management for Light Water Reactors

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