Generative Design of a Gas-Cooled Micro Reactor Based on Multi-Physics Analysis

Abstract Additive manufacturing (AM) is a “game-changing” digital fabrication technology that can fundamentally alter the way products are designed and manufactured. In nuclear engineering, the application of additive manufacturing can enable advanced reactor designs with better performance, lower cost, and quicker deployment. To exploit the maximum potential of additive manufacturing, design for additive manufacturing (DfAM) is needed, DfAM can leverage the complexity freedom offered by additive manufacturing technology to break traditional design constraints and achieve enhanced performance. Generative design, one of the key technologies in DfAM, is an iterative exploration method using AI-driven algorithms to generate a series of design solutions that meet specified constraints. In this work, a generative design platform for micro-reactors is developed based on DASSULT SIMULIA Isight. To simulate and analyze the multi-physics performance of the reactor design, various multi-physics simulation codes are integrated on this generative design platform, including RMC for neutron physics analysis, ANSYS Fluent for computational fluid dynamics analysis, and ANSYS Mechanical for structural mechanics analysis. A multi-objective optimization method is used to explore geometric parameters and fuel configurations to achieve better neutronics, thermal-fluidics, and structural mechanics performance. The generative design solution is a 4 MW helium-cooled nuclear reactor which includes 3D-printed silicon carbide (SiC) matrix embedded with tri-structural isotropic (TRISO) particle fuel. Helium at 3 MPa is chosen as the coolant, and the cross-shaped coolant channels are distributed within the matrix to remove the heat generated by fission. According to the multiphysics analysis, the reactor has a initial keff of 1.0571, power peaking factor FQ of 1.50, and operating lifetime of 1200 EFPD (Effective Full Power Days), with a maximum temperature of 1331 K and a peak thermal stress of 141.3 MPa. The generative design method and platform provide a systematic and efficient approach for the design of additively manufactured micro-reactors, and the generative design solution demonstrates the feasibility and effectiveness of the proposed method.

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Publication Details

Published
2024-08-04
DOI
https://doi.org/10.1115/icone31-135237
Citations
2
Primary Topic
Heat Transfer and Optimization
Type
article
Field-Weighted Citation Impact
0.43
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article

Generative Design of a Gas-Cooled Micro Reactor Based on Multi-Physics Analysis

Wenbin Han, Shanfang Huang, Qi Lu, Jian Deng
2 citations
Heat Transfer and Optimization
0.43
article

Generative Design of a Gas-Cooled Micro Reactor Based on Multi-Physics Analysis

Wenbin Han, Shanfang Huang, Qi Lu, Jian Deng
article en
2 citations

Abstract

Abstract Additive manufacturing (AM) is a “game-changing” digital fabrication technology that can fundamentally alter the way products are designed and manufactured. In nuclear engineering, the application of additive manufacturing can enable advanced reactor designs with better performance, lower cost, and quicker deployment. To exploit the maximum potential of additive manufacturing, design for additive manufacturing (DfAM) is needed, DfAM can leverage the complexity freedom offered by additive manufacturing technology to break traditional design constraints and achieve enhanced performance. Generative design, one of the key technologies in DfAM, is an iterative exploration method using AI-driven algorithms to generate a series of design solutions that meet specified constraints. In this work, a generative design platform for micro-reactors is developed based on DASSULT SIMULIA Isight. To simulate and analyze the multi-physics performance of the reactor design, various multi-physics simulation codes are integrated on this generative design platform, including RMC for neutron physics analysis, ANSYS Fluent for computational fluid dynamics analysis, and ANSYS Mechanical for structural mechanics analysis. A multi-objective optimization method is used to explore geometric parameters and fuel configurations to achieve better neutronics, thermal-fluidics, and structural mechanics performance. The generative design solution is a 4 MW helium-cooled nuclear reactor which includes 3D-printed silicon carbide (SiC) matrix embedded with tri-structural isotropic (TRISO) particle fuel. Helium at 3 MPa is chosen as the coolant, and the cross-shaped coolant channels are distributed within the matrix to remove the heat generated by fission. According to the multiphysics analysis, the reactor has a initial keff of 1.0571, power peaking factor FQ of 1.50, and operating lifetime of 1200 EFPD (Effective Full Power Days), with a maximum temperature of 1331 K and a peak thermal stress of 141.3 MPa. The generative design method and platform provide a systematic and efficient approach for the design of additively manufactured micro-reactors, and the generative design solution demonstrates the feasibility and effectiveness of the proposed method.

China General Nuclear Power Corporation (China) (CN), Tsinghua University (CN)
Openalex Percentile: Top 41%
Heat Transfer and Optimization
0.43
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