Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Special Issues
Digitalization of Nuclear Power Plant Asset Management Using Artificial Intelligence...
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

Digitalization of Nuclear Power Plant Asset Management Using Artificial Intelligence and Machine Learning Methods

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

Deadline for manuscript submissions: 20 July 2025 | Viewed by 3205

Special Issue Editors

Prof. Dr. Enrico Zio

E-Mail Website
Guest Editor
1. Centre de Recherce sur les Risques et les Crises (CRC), MINES Paris-PSL Université, 06904 Paris, France
2. Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano, Italy
Interests: risk assessment; reliability analysis; simulation; artificial intelligence; nuclear systems
Special Issues, Collections and Topics in MDPI journals
Dr. Ibrahim Ahmed

E-Mail Website
Guest Editor
Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano, Italy
Interests: nuclear systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The quest for improving the safety and reliability of Nuclear Power Plants (NPP) is motivating the nuclear industry to explore the recent advancements in smart sensing, data analysis and computational efficiency for the realization of reliable control, efficient production, real-time early failure detection and mitigation, the minimization of unplanned shutdown, the improvement of safety and operation, and a reduction in maintenance costs. Paired with the increasing adoption of digitalization in nuclear systems and the associated availability of large volumes of data, Artificial Intelligence (AI) and Machine Learning (ML) methods offer the enhanced capacity to determine the operating conditions directly from a wide range of heterogenous datasets, ranging from single modality to multi-modality sets of data. 

The aim of this Special Issue is to promote, collect and share recent research in AI and ML advancements, including both methodological and practical research for the digitalization of nuclear systems, ranging from modeling and simulation, control, prediction, detection, diagnostics and prognostics, to reliability decision-making and risk assessment. 

The topics of interest include, but are not limited to, the following: 

  • Digital twins and digital-twin-enabling technologies for the modelling and simulation of NPP;
  • AI/ML methods for prognostics and health management of NPP components and systems;
  • AI/ML methods for structural health management of NPP assets;
  • AI/ML methods for resilience assessment and enhancement of NPP;
  • Reinforcement learning for maintenance decision-making in NPP;
  • Explainability/interpretability of AI/ML methods for NPP applications;
  • Risk-informed system health and asset management;
  • Risk-informed condition-based predictive maintenance;
  • AI/ML methods for domain adaptation;
  • Other relevant AI and ML approaches for the digitalization of NPP.

Prof. Dr. Enrico Zio
Dr. Ibrahim Ahmed
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 power plants
  • digital twin
  • asset management
  • control
  • prognostics and health management
  • artificial intelligence
  • machine learning

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

18 pages, 2795 KiB  
Article
Transformers and Long Short-Term Memory Transfer Learning for GenIV Reactor Temperature Time Series Forecasting
by Stella Pantopoulou, Anthonie Cilliers, Lefteri H. Tsoukalas and Alexander Heifetz
Energies 2025, 18(9), 2286; https://doi.org/10.3390/en18092286 - 30 Apr 2025
Viewed by 365
Abstract
Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends [...] Read more.
Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends on the availability of large amount of training data, which is difficult to obtain for GenIV, as this technology is still under development. We propose the use of transfer learning (TL), which involves utilizing knowledge across different domains, to compensate for this lack of training data. TL can be used to create pre-trained ML models with data from small-scale research facilities, which can then be fine-tuned to monitor GenIV reactors. In this work, we develop pre-trained Transformer and long short-term memory (LSTM) networks by training them on temperature measurements from thermal hydraulic flow loops operating with water and Galinstan fluids at room temperature at Argonne National Laboratory. The pre-trained models are then fine-tuned and re-trained with minimal additional data to perform predictions of the time series of high temperature measurements obtained from the Engineering Test Unit (ETU) at Kairos Power. The performance of the LSTM and Transformer networks is investigated by varying the size of the lookback window and forecast horizon. The results of this study show that LSTM networks have lower prediction errors than Transformers, but LSTM errors increase more rapidly with increasing lookback window size and forecast horizon compared to the Transformer errors. Full article
(This article belongs to the Special Issue Digitalization of Nuclear Power Plant Asset Management Using Artificial Intelligence and Machine Learning Methods)
►▼ Show Figures

Figure 1

23 pages, 1566 KiB  
Article
A Multistage Physics-Informed Neural Network for Fault Detection in Regulating Valves of Nuclear Power Plants
by Chenyang Lai, Ibrahim Ahmed, Enrico Zio, Wei Li, Yiwang Zhang, Wenqing Yao and Juan Chen
Energies 2024, 17(11), 2647; https://doi.org/10.3390/en17112647 - 30 May 2024
Cited by 2 | Viewed by 1877
Abstract
In Nuclear Power Plants (NPPs), online condition monitoring and the fault detection of structures, systems and components (SSCs) can aid in guaranteeing safe operation. The use of data-driven methods for these tasks is limited by the requirement of physically consistent outcomes, particularly in [...] Read more.
In Nuclear Power Plants (NPPs), online condition monitoring and the fault detection of structures, systems and components (SSCs) can aid in guaranteeing safe operation. The use of data-driven methods for these tasks is limited by the requirement of physically consistent outcomes, particularly in safety-critical systems. Considering the importance of regulating valves (e.g., safety relief valves and main steam isolation valves), this work proposes a multistage Physics-Informed Neural Network (PINN) for fault detection in such components. Two stages of the PINN are built by developing the process model of the regulating valve, which integrates the basic valve sizing equation into the loss function to jointly train the two stages of the PINN. In the 1st stage, a shallow Neural Network (NN) with only one hidden layer is developed to estimate the equivalent flow coefficient (a key performance indicator of regulating valves) using the displacement of the valve as input. In the 2nd stage, a Deep Neural Network (DNN) is developed to estimate the flow rate expected in normal conditions using inputs such as the estimated flow coefficient from the 1st stage, the differential pressure, and the fluid temperature. Then, the residual, i.e., the difference between the estimated and measured flow rates, is fed into a Deep Support Vector Data Description (DeepSVDD) to detect the occurrence of faults. Moreover, the deviation between the estimated flow coefficients of normal and faulty conditions is used to interpret the consistency of the detection result with physics. The proposed method is, first, applied to a simulation case implemented to emulate the operating characteristics of regulating the valves of NPPs and then validated on a real-world case study based on the DAMADICS benchmark. Compared to state-of-the-art fault detection methods, the obtained results from the proposed method show effective fault detection performance and reasonable flow coefficient estimation, thus guaranteeing the physical consistency of the detection results. Full article
(This article belongs to the Special Issue Digitalization of Nuclear Power Plant Asset Management Using Artificial Intelligence and Machine Learning Methods)
►▼ 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