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Optimal Design and Analysis of Advanced Nuclear Reactors

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B4: Nuclear Energy".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 3291

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Special Issue Editors

Dr. Jing Zhang

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Guest Editor

Department of Nuclear Science and Technology, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: nuclear reactor design; safety and simulation of nuclear power system; nuclear reactor thermal hydraulic; artificial intelligence

Dr. Yuan Yuan

E-Mail Website
Guest Editor

College of Physical Science and Technology, Sichuan University, Chengdu 610065, China
Interests: reactor thermal hydraulic; reactor numerical calculation; artificial intelligence

Special Issue Information

Dear Colleagues,

Nuclear energy is an efficient and clean type of energy. The nuclear reactor-based energy supply is characterized by high energy density, low carbon emissions, long sustainable operation time and wide use. Reactor technology continues to evolve, with a large number of passive generation III+ and generation IV reactor designs emerging. All designs focus on the reactor's inherent safety improvement, while allowing for a more efficient and flexible energy supply. With the development of experimental measurement and computer simulation technology, more accurate analytical methods provide support for the design and optimization of advanced reactors; thus, the economy and safety of reactors would be enhanced.

This Special Issue aims to present and disseminate the most recent advances related to the theory, design, modeling and optimization of all types of advanced nuclear reactors. Topics of interest for publication include, but are not limited to:

Thermal-hydraulic characteristics of advanced reactors;
Multi-physics coupling in the reactor core;
Nuclear reactor systems design;
Safety analysis of advanced reactors;
Explicable machine learning in nuclear energy;
Advanced optimization algorithms.

Dr. Jing Zhang
Dr. Yuan Yuan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

thermal hydraulics
safety analysis
multi-physics coupling
explicable machine learning in nuclear energy
severe accident

Published Papers (3 papers)

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Research

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14 pages, 2948 KiB

Open AccessArticle

Coupling Design and Validation Analysis of an Integrated Framework of Uncertainty Quantification

by Bo Pang, Yuhang Su, Jie Wang, Chengcheng Deng, Qingyu Huang, Shuang Zhang, Bin Wu and Yuanfeng Lin

Energies 2023, 16(11), 4435; https://doi.org/10.3390/en16114435 - 31 May 2023

Viewed by 851

Abstract

The uncertainty quantification is an indispensable part for the validation of the nuclear safety best-estimate codes. However, the uncertainty quantification usually requires the combination of statistical analysis software and nuclear reactor professional codes, and it consumes huge computing resources. In this paper, a design method of coupling interface between DAKOTA Version 6.16 statistical software and nuclear reactor professional simulation codes is proposed, and the integrated computing workflow including interface pre-processing, code batching operations, and interface post-processing can be realized. On this basis, an integrated framework of uncertainty quantification is developed, which is characterized by visualization, convenience, and efficient computing. Meanwhile, a typical example of small-break LOCA analysis of the LOBI test facility was used to validate the reliability of the developed integrated framework of uncertainty quantification. This research work can provide valuable guidance for developing an autonomous uncertainty analysis platform in China. Full article

(This article belongs to the Special Issue Optimal Design and Analysis of Advanced Nuclear Reactors)

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18 pages, 10356 KiB

Open AccessArticle

Three-Dimensional Surrogate Model Based on Back-Propagation Neural Network for Key Neutronics Parameters Prediction in Molten Salt Reactor

by Xinyan Bei, Yuqing Dai, Kaicheng Yu and Maosong Cheng

Energies 2023, 16(10), 4044; https://doi.org/10.3390/en16104044 - 12 May 2023

Cited by 1 | Viewed by 1045

Abstract

The simulation and analysis of neutronics parameters in Molten Salt Reactors (MSRs) is fundamental for the design of the reactor core. However, high-fidelity neutron transport calculations of the MSR are time-consuming and require significant computational resources. Artificial neural networks (ANNs) have been used in various industries, and in recent years are increasingly introduced in the nuclear industry. Back-Propagation neural network (BPNN) is one type of ANN. A surrogate model based on BP neural network is developed to quickly predict two key neutronics parameters in reactor core: the effective multiplication factor (

k_{eff}

) and the three-dimensional channel-by-channel neutron flux distribution. The dataset samples are generated by modeling and simulating different operation states of the Molten Salt Reactor Experiment (MSRE) using the Monte Carlo code. Hyper-parameters optimization is performed to obtain the optimal surrogate model. The numerical results on the test dataset show good agreement between the surrogate model and the Monte Carlo code. Additionally, the surrogate model significantly reduces computation time compared to the Monte Carlo code and greatly enhances efficiency. The feasibility and advantages of the proposed surrogate model is demonstrated, which has important significance for real-time prediction and design optimization of the reactor core. Full article

(This article belongs to the Special Issue Optimal Design and Analysis of Advanced Nuclear Reactors)

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Review

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23 pages, 2961 KiB

Open AccessReview

Research Advances in the Application of the Supercritical CO₂ Brayton Cycle to Reactor Systems: A Review

by Yuhui Xiao, Yuan Zhou, Yuan Yuan, Yanping Huang and Gengyuan Tian

Energies 2023, 16(21), 7367; https://doi.org/10.3390/en16217367 - 31 Oct 2023

Viewed by 845

Abstract

Amid the global emphasis on efficient power conversion systems under the “dual carbon” policy framework, the supercritical CO₂ (SCO₂) Brayton cycle is a noteworthy subject, owing to its pronounced efficiency, compact design, economic viability, and remarkable potential to increase the thermal cycle efficiency of nuclear reactors. However, its application across various nuclear reactor loops presents divergent challenges, complicating system design and analytical processes. This paper offers a thorough insight into the latest research on the SCO₂ Brayton cycle, particularly emphasising its integration within directly and indirectly cooled nuclear reactors. The evolution of the Brayton cycle in nuclear reactor systems has been meticulously explored, focusing on its structural dynamics, key components, and inherent pros and cons associated with distinct reactor loops. Based on the theoretical frameworks and empirical findings related to turbomachinery and heat exchangers within the cycle, we chart a course for future enquiries into its critical components, underscoring the indispensable role of experimental investigations. This paper conclusively assesses the feasibility of deploying the SCO₂ Brayton cycle in direct and indirect cooling contexts, offering a forward-looking perspective on its developmental trajectory. The SCO₂ Brayton cycle may become a focal point for research, potentially creating avenues for nuclear energy endeavours. Full article

(This article belongs to the Special Issue Optimal Design and Analysis of Advanced Nuclear Reactors)

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