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Reliability Analysis and Risk Assessment of Nuclear Systems
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Reliability Analysis and Risk Assessment of Nuclear Systems

A special issue of Journal of Nuclear Engineering (ISSN 2673-4362).

Deadline for manuscript submissions: 15 January 2025 | Viewed by 2850

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 reliability analysis and risk assessment of nuclear systems need to be thorough and trustable, in order to support robust design and decision making. In this context, the present Special Issue aims to present the recent advancements in the methods and techniques employed to conduct the reliability analysis and risk assessment of nuclear systems.

Applications of interest include (but are not limited to) internal events risk assessment, external hazards risk assessment, natural hazards risk assessment, climate change risk assessment, uncertainty and sensitivity analysis, active and passive system reliability, structural reliability, structural health management, disaster management, and risk-based decision making.

This Special Issue is motivated by the presentation of fascinating works on these topics at the ICSRS Conference, which is to be held in Bologna, Italy, on 22–24 November 2023. Extended versions of selected works presented at the Conference will be solicited. Relevant papers from other authors are also welcome.

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. Journal of Nuclear Engineering is an international peer-reviewed open access quarterly 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 1000 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

  • reliability
  • risk
  • safety
  • nuclear systems

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Published Papers (2 papers)

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Research

26 pages, 380 KiB  
Article
First-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations: Mathematical Framework and Illustrative Application to the Nordheim–Fuchs Reactor Safety Model
by Dan Gabriel Cacuci
J. Nucl. Eng. 2024, 5(3), 347-372; https://doi.org/10.3390/jne5030023 - 13 Sep 2024
Cited by 1 | Viewed by 628
Abstract
This work introduces the mathematical framework of the novel “First-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations” (1st-CASAM-NODE) which yields exact expressions for the first-order sensitivities of NODE decoder responses to the NODE parameters, including encoder initial conditions, while enabling [...] Read more.
This work introduces the mathematical framework of the novel “First-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations” (1st-CASAM-NODE) which yields exact expressions for the first-order sensitivities of NODE decoder responses to the NODE parameters, including encoder initial conditions, while enabling the most efficient computation of these sensitivities. The application of the 1st-CASAM-NODE is illustrated by using the Nordheim–Fuchs reactor dynamics/safety phenomenological model, which is representative of physical systems that would be modeled by NODE while admitting exact analytical solutions for all quantities of interest (hidden states, decoder outputs, sensitivities with respect to all parameters and initial conditions, etc.). This work also lays the foundation for the ongoing work on conceiving the “Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations” (2nd-CASAM-NODE) which aims at yielding exact expressions for the second-order sensitivities of NODE decoder responses to the NODE parameters and initial conditions while enabling the most efficient computation of these sensitivities. Full article
(This article belongs to the Special Issue Reliability Analysis and Risk Assessment of Nuclear Systems)
20 pages, 1293 KiB  
Article
Phenomenological Nondimensional Parameter Decomposition to Enhance the Use of Simulation Modeling in Fire Probabilistic Risk Assessment of Nuclear Power Plants
by Sari Alkhatib, Tatsuya Sakurahara, Seyed Reihani, Ernest Kee, Brian Ratte, Kristin Kaspar, Sean Hunt and Zahra Mohaghegh
J. Nucl. Eng. 2024, 5(3), 226-245; https://doi.org/10.3390/jne5030016 - 2 Jul 2024
Viewed by 1129
Abstract
Simulation modeling is crucial in support of probabilistic risk assessment (PRA) for nuclear power plants (NPPs). There is a challenge, however, associated with simulation modeling that relates to the time and resources required for collecting data to determine the values of the input [...] Read more.
Simulation modeling is crucial in support of probabilistic risk assessment (PRA) for nuclear power plants (NPPs). There is a challenge, however, associated with simulation modeling that relates to the time and resources required for collecting data to determine the values of the input parameters. To alleviate this challenge, this article develops a formalized methodology to generate surrogate values of input parameters grounded on the decomposition of phenomenological nondimensional parameters (PNPs) while avoiding detailed data collection. While the fundamental principles of the proposed methodology can be applicable to various hazards, the developments in this article focus on fire PRA as an example application area for which resource intensiveness is recognized as a practical challenge. This article also develops a computational platform to automate the PNP decomposition and seamlessly integrates it with state-of-practice fire scenario analysis. The applicability of the computational platform is demonstrated through a multi-compartment fire case study at an NPP. The computational platform, with its embedded PNP decomposition methodology, can substantially reduce the effort required for input data collection and extraction, thereby facilitating the efficient use of simulation modeling in PRA and enhancing the fire scenario screening analysis. Full article
(This article belongs to the Special Issue Reliability Analysis and Risk Assessment of Nuclear Systems)
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