Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.2
Journals
Energies
Special Issues
Operation Safety and Simulation of Nuclear Energy Power Plant
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Operation Safety and Simulation of Nuclear Energy Power Plant

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

Deadline for manuscript submissions: 5 March 2026 | Viewed by 1944

Special Issue Editors

Dr. Jun Yang

E-Mail Website
Guest Editor
School of Electric Power, South China University of Technology, Guangzhou 510641, China
Interests: dynamic reliability and probabilistic safety evaluation of complex industrial safety-critical systems; nuclear power plant operation safety and simulation
Dr. Rong Liu

E-Mail Website
Guest Editor
School of Electric Power, South China University of Technology, Guangzhou 510640, China
Interests: nuclear fuel design and fuel behaviour analysis; optimisation of nuclear fuel performance under extreme operating conditions; uncertainty quantification and sensitivity analysis of the nuclear fuel behaviour

Special Issue Information

Dear Colleagues,

Safety is always paramount with nuclear energy power plant operations. Simulation is widely considered one of the pillars of nuclear reactor safety, as it acts as a virtual testing ground to simulate complex multi-physics and multi-scale phenomena in high-fidelity but without risking real-world harm to plant personnel or the environment. Benefiting from the rapid development of emerging technologies such as Industrial Internet of Things (IIoT), big data, digital twin, Artificial Intelligence (AI), and cloud computing, the operational safety and simulation of nuclear energy has also undergone a renaissance in recent years. The intersection of AI and advanced simulation technologies has become a new trend in delivering realistic and real-time insights into nuclear safety.

This Special Issue aims to present and disseminate the most recent advances in the design and operational safety and simulation of nuclear energy power plants. Topics of interest for submission include, but are not limited to, the following:

(1) AI-driven simulation technologies;

(2) Big data and IoT applications in intelligent operation and maintenance;

(3) Prognostics and health management of nuclear safety-critical equipment;

(4) System reliability modelling and analysis;

(5) Human reliability analysis;

(6) Alarm analysis and fault diagnosis;

(7) Risk assessment and safety management;

(8) Nuclear physics design and reload safety evaluation;

(9) Nuclear emergency preparedness and response;

(10) Uncertainty quantification and sensitivity analysis;

(11) Digitalization and standardization in nuclear applications;

(12) Nuclear fuel design and fuel behaviour analysis;

(13) Optimization of nuclear fuel performance under extreme operating conditions.

Dr. Jun Yang
Dr. Rong Liu
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

  • nuclear safety and simulation
  • operator support system
  • prognostic and health management

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 452 KB  
Article
A Pilot Application Study on Risk-Informed In-Service Inspection Methods for Pipelines in HPR1000 Nuclear Power Plants: A Case Study of the RCV System
by Ming Wang, Bing Zhang, Jiaoshen Xu and Sijuan Chen
Energies 2025, 18(17), 4753; https://doi.org/10.3390/en18174753 - 6 Sep 2025
Viewed by 593
Abstract
Traditional in-service inspection (ISI) methods for pipelines have certain limitations in identifying pipeline leakages. When these methods are directly applied to the ISI of Hua-long pressurized reactor (HPR1000) nuclear power plants, where the system complexity has significantly increased, they may lead to insufficient [...] Read more.
Traditional in-service inspection (ISI) methods for pipelines have certain limitations in identifying pipeline leakages. When these methods are directly applied to the ISI of Hua-long pressurized reactor (HPR1000) nuclear power plants, where the system complexity has significantly increased, they may lead to insufficient inspection efficiency and an extremely heavy workload. In this study, based on the framework of typical risk-informed analysis methods for nuclear power plants in the industry and integrating domestic engineering practical experience, an optimized ISI model for pipelines in HPR1000 nuclear power plants was constructed, and a pilot application was carried out on the chemical and volume control system (RCV) of the primary circuit. The inspection strategy was optimized through a series of steps, including determining the analysis scope, conducting pipe segment failure analysis, constructing a risk matrix, selecting inspection elements, and assessing risk impacts. Case studies showed that the risk-informed in-service inspection (RI-ISI) method successfully classified over 3000 welds in the RCV system based on risk levels (high, medium, low). After optimization, 16 low-risk welds (risk level 7) and one of the two medium-risk welds (risk level 4) that originally required volumetric inspection were exempted from inspection. Quantitative risk analysis confirmed that the increments in core damage frequency (CDF) and large early-release frequency (LERF) caused by this optimization were far below the regulatory limits. This method significantly reduces the inspection burden of medium- and low-risk pipelines while ensuring that high-risk areas receive priority attention, providing important technical support for the safe and efficient operation and maintenance of HPR1000 and subsequent third-generation nuclear power units. Full article
(This article belongs to the Special Issue Operation Safety and Simulation of Nuclear Energy Power Plant)
►▼ Show Figures

Figure 1

20 pages, 2430 KB  
Article
A Bayesian Network Approach to Predicting Severity Status in Nuclear Reactor Accidents with Resilience to Missing Data
by Kaiyu Li, Ling Chen, Xinxin Cai, Cai Xu, Yuncheng Lu, Shengfeng Luo, Wenlin Wang, Lizhi Jiang and Guohua Wu
Energies 2025, 18(11), 2684; https://doi.org/10.3390/en18112684 - 22 May 2025
Viewed by 793
Abstract
Nuclear energy is a cornerstone of the global energy mix, delivering reliable, low-carbon power essential for sustainable energy systems. However, the safety of nuclear reactors is critical to maintaining operational reliability and public trust, particularly during accidents like a Loss of Coolant Accident [...] Read more.
Nuclear energy is a cornerstone of the global energy mix, delivering reliable, low-carbon power essential for sustainable energy systems. However, the safety of nuclear reactors is critical to maintaining operational reliability and public trust, particularly during accidents like a Loss of Coolant Accident (LOCA) or a Steam Line Break Inside Containment (SLBIC). This study introduces a Bayesian Network (BN) framework used to enhance nuclear energy safety by predicting accident severity and identifying key factors that ensure energy production stability. With the integration of simulation data and physical knowledge, the BN enables dynamic inference and remains robust under missing-data conditions—common in real-time energy monitoring. Its hierarchical structure organizes variables across layers, capturing initial conditions, intermediate dynamics, and system responses vital to energy safety management. Conditional Probability Tables (CPTs), trained via Maximum Likelihood Estimation, ensure accurate modeling of relationships. The model’s resilience to missing data, achieved through marginalization, sustains predictive reliability when critical energy system variables are unavailable. Achieving R2 values of 0.98 and 0.96 for the LOCA and SLBIC, respectively, the BN demonstrates high accuracy, directly supporting safer nuclear energy production. Sensitivity analysis using mutual information pinpointed critical variables—such as high-pressure injection flow (WHPI) and pressurizer level (LVPZ)—that influence accident outcomes and energy system resilience. These findings offer actionable insights for the optimization of monitoring and intervention in nuclear power plants. This study positions Bayesian Networks as a robust tool for real-time energy safety assessment, advancing the reliability and sustainability of nuclear energy production. Full article
(This article belongs to the Special Issue Operation Safety and Simulation of Nuclear Energy Power Plant)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop