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Energies
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Technological Advancements Enabling Sustainment and Expansion of the Nuclear Industry
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Technological Advancements Enabling Sustainment and Expansion of the Nuclear Industry

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 6083

Special Issue Editors

Dr. Vivek Agarwal

E-Mail Website
Guest Editor
Department of Instrumentation, Controls, & Data Science, Idaho National Laboratory, Idaho Falls, ID 83415, USA
Interests: artificial intelligence; machine learning; autonomous controls; remote operation and monitoring; digital twin; signal and image processing; reactor technologies; wireless communication; digital technologies; reliability; and risk assessment
Dr. Nancy Lybeck

E-Mail Website
Guest Editor
Department of Instrumentation, Controls, & Data Science, Idaho National Laboratory, Idaho Falls, ID 83415, USA
Interests: diagnostics; prognostics; numerical solution of differential equations; parameter identification; optimization; machine learning
Dr. Vaibhav Yadav

E-Mail Website
Guest Editor
Department of Instrumentation, Controls, & Data Science, Idaho National Laboratory, Idaho Falls, ID 83415, USA
Interests: risk assessment; risk-informed methods; risk-informed performance-based regulations; nuclear safety; nuclear security; safeguards; integration of safety, security and safeguards; digital twins; regulatory aspects of digital twins; advanced reactor security and safeguards; advanced reactor deployment roadmap

Special Issue Information

Dear Colleagues,

As the nuclear industry focuses on both the sustainment of the existing fleet of light water reactors and the development and deployment of advanced reactors, the current capabilities of technological advancements must be leveraged. This Special Issue focuses on the application of innovative science and technological solutions that enable the sustainment and expansion of the nuclear industry.

This Special Issue welcomes contributions that attend to topics including, but not limited to, the following:

  • The lifecycle management of artificial intelligence and machine learning models;
  • Applications of artificial intelligence and machine learning in nuclear operation and maintenance;
  • The use of automation to enhance efficiency in the maintenance and operation of nuclear power plants;
  • Digital twins in the design and development of advanced nuclear reactors;
  • Control architectures, including autonomous and semi-autonomous controls for advanced nuclear reactors;
  • Technologies for non-traditional nuclear markets;
  • The need for communication to maintain situational awareness;
  • Cyber-security considerations;
  • Sensor technologies;
  • Risk-informed methodologies and strategies;
  • Additive manufacturing for light-weight materials for structural and shielding applications.

Dr. Vivek Agarwal
Dr. Nancy Lybeck
Dr. Vaibhav Yadav
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.

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Published Papers (3 papers)

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Research

28 pages, 16297 KiB  
Article
Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems
by Niharika Karnik, Congjian Wang, Palash K. Bhowmik, Joshua J. Cogliati, Silvino A. Balderrama Prieto, Changhu Xing, Andrei A. Klishin, Richard Skifton, Musa Moussaoui, Charles P. Folsom, Joe J. Palmer, Piyush Sabharwall, Krithika Manohar and Mohammad G. Abdo
Energies 2024, 17(13), 3355; https://doi.org/10.3390/en17133355 - 8 Jul 2024
Cited by 3 | Viewed by 1735
Abstract
Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through [...] Read more.
Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP. Full article
(This article belongs to the Special Issue Technological Advancements Enabling Sustainment and Expansion of the Nuclear Industry)
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24 pages, 5734 KiB  
Article
Technical Language Processing of Nuclear Power Plants Equipment Reliability Data
by Congjian Wang, Diego Mandelli and Joshua Cogliati
Energies 2024, 17(7), 1785; https://doi.org/10.3390/en17071785 - 8 Apr 2024
Viewed by 2164
Abstract
Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance [...] Read more.
Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well. Full article
(This article belongs to the Special Issue Technological Advancements Enabling Sustainment and Expansion of the Nuclear Industry)
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11 pages, 6075 KiB  
Article
Lattice Design and Advanced Modeling to Guide the Design of High-Performance Lightweight Structural Materials
by Rongjie Song, Michael Moorehead, Dewen Yushu and Jia-Hong Ke
Energies 2024, 17(6), 1468; https://doi.org/10.3390/en17061468 - 19 Mar 2024
Cited by 2 | Viewed by 1404
Abstract
Lightweight structural materials are required to increase the mobility of fission batteries. The materials must feature a robust combination of mechanical properties to demonstrate structural resilience. The primary objective of this project is to produce lightweight structural materials whose strength-to-weight ratios exceed those [...] Read more.
Lightweight structural materials are required to increase the mobility of fission batteries. The materials must feature a robust combination of mechanical properties to demonstrate structural resilience. The primary objective of this project is to produce lightweight structural materials whose strength-to-weight ratios exceed those of the current widely used structural materials such as 316L stainless steels (316L SS). To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structures models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The preliminary results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison with the solid material and that the unit cell size of the lattice had a minimal effect. The novelty here is to apply up-front modeling to determine the best structure for the application before actually producing the sample. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for material development. Full article
(This article belongs to the Special Issue Technological Advancements Enabling Sustainment and Expansion of the Nuclear Industry)
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