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Advances in Nuclear Power Plants and Nuclear Safety
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Advances in Nuclear Power Plants and Nuclear Safety

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

Deadline for manuscript submissions: 25 July 2025 | Viewed by 1652

Special Issue Editor

Dr. Yuan Zhou

E-Mail Website
Guest Editor
Department of Nuclear Engineering and Technology, Sichuan University, Chengdu, China
Interests: advanced nuclear energy systems; reactor thermal hydraulics; severe accidents in nuclear power plants; reactor safety analysis

Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to a Special Issue of Energies on the subject area of “Advances in Nuclear Power Plants and Nuclear Safety”. Advanced nuclear power plant system research is the main trend in the development of nuclear energy. There are now many fourth-generation nuclear power systems being researched in recent years. Moreover, artificial intelligence is also an interesting topic for Nuclear Power Plants Control and Nuclear Safety. With the development of artificial intelligence technology and the urgent demand for smart nuclear power supported by high-precision models of numerical reactors and digital twin operation and maintenance, it is necessary to explore the application of cutting-edge methods of artificial intelligence in reactors.

This Special Issue will deal with Advanced nuclear system research and artificial intelligence applications for “Advances in Nuclear Power Plants and Nuclear Safety”, Topics of interest for publication include, but are not limited to:

  • Fourth-generation nuclear power systems;
  • Supercritical carbon dioxide nuclear energy system;
  • Helium xenon-cooled nuclear reactor;
  • Nuclear reactor thermal hydraulics;
  • Analysis of Nuclear Reactor Safety;
  • Intelligent accident diagnosis algorithm;
  • Non-invasive order reduction method for nuclear;
  • Flow field reconstruction for nuclear.

Dr. Yuan Zhou
Guest Editor

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

  • nuclear reactor
  • thermal hydraulics
  • safety analysis
  • control methods
  • artificial intelligence

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

25 pages, 17509 KiB  
Article
Development and Application of a Sensitivity and Uncertainty Analysis Framework for Safety Analysis of Molten Salt Reactors
by Haijun Liu, Rui Li, Xiandi Zuo, Maosong Cheng, Shichao Chen and Zhimin Dai
Energies 2025, 18(9), 2179; https://doi.org/10.3390/en18092179 - 24 Apr 2025
Viewed by 184
Abstract
To provide reliable safety margins in reactor design and safety analysis, the best estimate plus uncertainty (BEPU) analysis, which is recommended by the International Atomic Energy Agency (IAEA), has drawn increasing attention worldwide. In order to systematically evaluate the sensitivity and uncertainty in [...] Read more.
To provide reliable safety margins in reactor design and safety analysis, the best estimate plus uncertainty (BEPU) analysis, which is recommended by the International Atomic Energy Agency (IAEA), has drawn increasing attention worldwide. In order to systematically evaluate the sensitivity and uncertainty in the design and safety analysis of molten salt reactors (MSRs), a sensitivity and uncertainty analysis framework has been developed by integrating the reactor system safety analysis code RELAP5-TMSR with the data analysis code RAVEN. The framework is tested using the transient scenarios of the molten salt reactor experiment (MSRE): reactivity insertion accident (RIA) and station blackout (SBO). The testing results demonstrate that the proposed framework effectively conducts sensitivity and uncertainty analysis. Sensitivity analyses identify key input parameters, including the primary exchanger parameters, air radiator parameters, initial temperatures, delayed neutron parameters and volumetric heat capacity of the INOR-8 alloy. Uncertainty quantification provides 95% confidence intervals for the figures of merit (FOMs) and the steady-state and RIA scenarios remained within safety limits. The developed framework enables automated, efficient, and high-capacity sensitivity and uncertainty analysis across multiple parameters and transient scenarios. The systematic analysis provides sensitivity indicators and uncertainty distributions, offering quantitative insights into the safety margins and supporting the design and safety analysis of MSRs. Full article
(This article belongs to the Special Issue Advances in Nuclear Power Plants and Nuclear Safety)
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17 pages, 3519 KiB  
Article
An Interpretable Dynamic Feature Search Methodology for Accelerating Computational Process of Control Rod Descent in Nuclear Reactors
by Qingyu Huang, Cong Xiao, Wei Zeng, Le Xu, Jia Liu, Zhixin Pang, Yuanfeng Lin, Mengwei Zhao and Xiaobo Liu
Energies 2025, 18(7), 1827; https://doi.org/10.3390/en18071827 - 4 Apr 2025
Viewed by 181
Abstract
Within the operational dynamics of a nuclear reactor, the customary approach involves modulating the reactor’s power output by means of control rod manipulation, which effectively alters the neutron density across the core. The descent behavior of the control rod drive lines pertains to [...] Read more.
Within the operational dynamics of a nuclear reactor, the customary approach involves modulating the reactor’s power output by means of control rod manipulation, which effectively alters the neutron density across the core. The descent behavior of the control rod drive lines pertains to the intricate motion exhibited by the control rod components within the reactor during its operational lifespan, characterized by conditions of heightened irradiation, temperature, pressure, and complex fluid dynamics. The precise calculation of the control rod descent process is an integral facet of reactor structural design to ensure the safe and reliable operation of the reactor. However, the current computational fluid dynamics-based simulation methods employed for this purpose necessitate extensive grid computations, imposing significant computational burdens in terms of resources and time. In light of this challenge, we present a novel and interpretative algorithm rooted in dynamic similarity feature search. Through comprehensive validation, this algorithm demonstrates remarkable precision, with the computational results exhibiting an error margin within 10% while simultaneously achieving a substantial enhancement of computational efficiency of nearly three orders of magnitude when compared to conventional computational fluid dynamics techniques and sequence-to-sequence machine learning algorithms. Notably, this algorithm showcases exceptional versatility, holding immense promise for broad applicability across various operational scenarios encountered during the intricate process of nuclear reactor design. Full article
(This article belongs to the Special Issue Advances in Nuclear Power Plants and Nuclear Safety)
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26 pages, 16265 KiB  
Article
A Multi-Variable Coupled Control Strategy Based on a Deep Deterministic Policy Gradient Reinforcement Learning Algorithm for a Small Pressurized Water Reactor
by Jie Chen, Kai Xiao, Ke Huang, Zhen Yang, Qing Chu and Guanfu Jiang
Energies 2025, 18(6), 1517; https://doi.org/10.3390/en18061517 - 19 Mar 2025
Viewed by 217
Abstract
The reactor system has multivariate, nonlinear, and strongly coupled dynamic characteristics, which puts high demands on the robustness, real-time demand, and accuracy of the control strategy. Conventional control approaches depend on the mathematical model of the system being controlled, making it challenging to [...] Read more.
The reactor system has multivariate, nonlinear, and strongly coupled dynamic characteristics, which puts high demands on the robustness, real-time demand, and accuracy of the control strategy. Conventional control approaches depend on the mathematical model of the system being controlled, making it challenging to handle the reactor system’s dynamic complexity and uncertainties. This paper proposes a multi-variable coupled control strategy for a nuclear reactor steam supply system based on a Deep Deterministic Policy Gradient reinforcement learning algorithm, designs and trains a multi-variable coupled intelligent controller to simultaneously realize the coordinated control of multiple parameters, such as the reactor power, average coolant temperature, steam pressure, etc., and performs a simulation validation of the control strategy under the typical transient variable load working conditions. Simulation results show that the reinforcement learning control effect is better than the PID control effect under a ±10% FP step variable load condition, a linear variable load condition, and a load dumping condition, and that the reactor power overshooting amount and regulation time, the maximum deviation of the coolant average temperature, the steam pressure, the pressure of pressurizer and relative liquid level, and the regulation time are improved by at least 15.5% compared with the traditional control method. Therefore, this study offers a theoretical framework for utilizing reinforcement learning in the field of nuclear reactor control. Full article
(This article belongs to the Special Issue Advances in Nuclear Power Plants and Nuclear Safety)
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23 pages, 20560 KiB  
Article
Refinement of Finite Element Method Analysis Model of Pressurized Water Reactor Nuclear Fuel Spacer Grid Based on Experimental Data
by Minhee Kim and Ihn Namgung
Energies 2025, 18(3), 528; https://doi.org/10.3390/en18030528 - 23 Jan 2025
Viewed by 611
Abstract
A Finite Element Method (FEM) analysis of the nuclear fuel spacer grid was conducted to assess the strength of components for the safety of nuclear power plants. The fuel assembly consists of fuel rods, upper end-fitting, lower end-fitting, guide tubes, and spacer grids. [...] Read more.
A Finite Element Method (FEM) analysis of the nuclear fuel spacer grid was conducted to assess the strength of components for the safety of nuclear power plants. The fuel assembly consists of fuel rods, upper end-fitting, lower end-fitting, guide tubes, and spacer grids. Spacer grids play a critical role in maintaining the proper spacing between fuel rods within a fuel assembly and ensuring smooth coolant flow. This role becomes particularly crucial during unforeseen emergencies, such as seismic loads, where minimizing deformation caused by external forces is essential. Therefore, this study proposes FEM models of spacer grids, mesh refinement of models, and analysis of the stiffness of the spacer grids concerning the presence or absence of pellet and clad. The results revealed significantly lower shear stiffness compared to normal stiffness, indicating potential vulnerability of the fuel assembly to large loads such as those experienced during seismic events. Full article
(This article belongs to the Special Issue Advances in Nuclear Power Plants and Nuclear Safety)
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