Validation and Application of CFD Methodology for Core Inlet Flow Distribution in APR1000 Reactor

The core inlet flow distribution in the APR1000 reactor is critical for ensuring the reactors safety and efficient operation by maintaining uniform coolant flow across fuel assemblies. Previous studies, though insightful, faced challenges in fully replicating reactor-scale flow conditions due to tec...

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Main Authors: Sung Man Son, Won Man Park, Dae Kyung Choi, Choengryul Choi
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Energies
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Online Access:https://www.mdpi.com/1996-1073/18/3/512
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author Sung Man Son
Won Man Park
Dae Kyung Choi
Choengryul Choi
author_facet Sung Man Son
Won Man Park
Dae Kyung Choi
Choengryul Choi
author_sort Sung Man Son
collection DOAJ
description The core inlet flow distribution in the APR1000 reactor is critical for ensuring the reactors safety and efficient operation by maintaining uniform coolant flow across fuel assemblies. Previous studies, though insightful, faced challenges in fully replicating reactor-scale flow conditions due to technical and economic constraints associated with scaled-down experimental models and the limited numerical validation methodologies. This study addresses these limitations by developing and validating a robust computational fluid dynamics (CFD) methodology to accurately analyze the core inlet flow distribution. A 1/5 scaled-down experimental model adhering to similarity laws was employed for validation. CFD analyses using ANSYS Fluent and CFX, combined with turbulence model evaluations and grid sensitivity studies, demonstrated that the SST and RNG k-ε turbulence models provided the most accurate predictions, with a high correlation to previous experimental data. Full-scale simulations revealed uniform coolant distribution at the core inlet, with peripheral assemblies exhibiting higher flow rates, consistent with previous experimental observations. Quantitative metrics such as the coefficient of variation (COV), relative error (RD), and root mean square error (RMSE) confirmed the superior performance of the SST model in CFX, achieving a COV of 7.993% (experimental COV: 5.694%) and an RD of 0.047. This methodology not only validates the CFD approach but also highlights its applicability to reactor design optimization and safety assessment. The findings of this study provide critical guidelines for analyzing complex thermal-fluid systems in nuclear reactor designs.
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spelling doaj-art-3f246fdaf0f34f6b8d73e72752f10e212025-08-20T02:12:25ZengMDPI AGEnergies1996-10732025-01-0118351210.3390/en18030512Validation and Application of CFD Methodology for Core Inlet Flow Distribution in APR1000 ReactorSung Man Son0Won Man Park1Dae Kyung Choi2Choengryul Choi3ELSOLTEC, 1401-2, 184 Jungbu-daero, Giheung-gu, Yougin-si 17095, Gyeonggi-do, Republic of KoreaELSOLTEC, 1401-2, 184 Jungbu-daero, Giheung-gu, Yougin-si 17095, Gyeonggi-do, Republic of KoreaELSOLTEC, 1401-2, 184 Jungbu-daero, Giheung-gu, Yougin-si 17095, Gyeonggi-do, Republic of KoreaELSOLTEC, 1401-2, 184 Jungbu-daero, Giheung-gu, Yougin-si 17095, Gyeonggi-do, Republic of KoreaThe core inlet flow distribution in the APR1000 reactor is critical for ensuring the reactors safety and efficient operation by maintaining uniform coolant flow across fuel assemblies. Previous studies, though insightful, faced challenges in fully replicating reactor-scale flow conditions due to technical and economic constraints associated with scaled-down experimental models and the limited numerical validation methodologies. This study addresses these limitations by developing and validating a robust computational fluid dynamics (CFD) methodology to accurately analyze the core inlet flow distribution. A 1/5 scaled-down experimental model adhering to similarity laws was employed for validation. CFD analyses using ANSYS Fluent and CFX, combined with turbulence model evaluations and grid sensitivity studies, demonstrated that the SST and RNG k-ε turbulence models provided the most accurate predictions, with a high correlation to previous experimental data. Full-scale simulations revealed uniform coolant distribution at the core inlet, with peripheral assemblies exhibiting higher flow rates, consistent with previous experimental observations. Quantitative metrics such as the coefficient of variation (COV), relative error (RD), and root mean square error (RMSE) confirmed the superior performance of the SST model in CFX, achieving a COV of 7.993% (experimental COV: 5.694%) and an RD of 0.047. This methodology not only validates the CFD approach but also highlights its applicability to reactor design optimization and safety assessment. The findings of this study provide critical guidelines for analyzing complex thermal-fluid systems in nuclear reactor designs.https://www.mdpi.com/1996-1073/18/3/512APR1000 reactor1/5 scale-down modelcore inlet flow distributioncomputational fluid dynamics (CFD)grid sensitivityturbulence model sensitivity
spellingShingle Sung Man Son
Won Man Park
Dae Kyung Choi
Choengryul Choi
Validation and Application of CFD Methodology for Core Inlet Flow Distribution in APR1000 Reactor
Energies
APR1000 reactor
1/5 scale-down model
core inlet flow distribution
computational fluid dynamics (CFD)
grid sensitivity
turbulence model sensitivity
title Validation and Application of CFD Methodology for Core Inlet Flow Distribution in APR1000 Reactor
title_full Validation and Application of CFD Methodology for Core Inlet Flow Distribution in APR1000 Reactor
title_fullStr Validation and Application of CFD Methodology for Core Inlet Flow Distribution in APR1000 Reactor
title_full_unstemmed Validation and Application of CFD Methodology for Core Inlet Flow Distribution in APR1000 Reactor
title_short Validation and Application of CFD Methodology for Core Inlet Flow Distribution in APR1000 Reactor
title_sort validation and application of cfd methodology for core inlet flow distribution in apr1000 reactor
topic APR1000 reactor
1/5 scale-down model
core inlet flow distribution
computational fluid dynamics (CFD)
grid sensitivity
turbulence model sensitivity
url https://www.mdpi.com/1996-1073/18/3/512
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