| Autor: |
Erina HAMASE, Kazuki KUWAGAKI, Norihiro DODA, Kenji YOKOYAMA, Masaaki TANAKA |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Mechanical Engineering Journal, Vol 11, Iss 2, Pp 23-00440-23-00440 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2187-9745 |
| DOI: |
10.1299/mej.23-00440 |
| Popis: |
A core design optimization process is developed as part of the design optimization support tool named ARKADIA (Advanced Reactor Knowledge- and AI-aided Design Integration Approach through the whole plant lifecycle) for an efficient and innovative core design process. The process comprises analyses integrated by neutronics, thermal-hydraulics in a fuel assembly, fuel integrity, and plant dynamics for safety assessment. The optimal design parameters are explored using the Bayesian optimization (BO) algorithm to reduce the number of iterative calculations and solve the optimization problem. This study defines a representative problem by identifying the objective functions, constraints, and design parameters for an actual core design based on previous core design experiences to efficiently develop the core design optimization process. Next, a constrained single-objective optimization problem, the simplified representative problem, is solved by the integrated analyses only with neutronics and plant dynamics using the BO algorithm to confirm the applicability of the optimization process for the representative problem. Consequently, the design parameters that optimized the objective function within constraints can be obtained. The optimal solution correlates well with the reference solution. Furthermore, the effectiveness of the optimization process is discussed by comparing an ordinary and a defined core design process. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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