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Advances in Simulation and Numerical Model of Nuclear Fuel Safety

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

Deadline for manuscript submissions: 8 January 2025 | Viewed by 468

Special Issue Editor

Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
Interests: nuclear reactor physics; core design of advanced nuclear reactor; nuclear fuel cycle
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled “Advances in Simulation and Numerical Model of Nuclear Fuel Safety”, aims to present and disseminate the latest advances in the simulation and experiment of nuclear fuel cycle safety.

The nuclear industry has experienced Gen-I, Gen-II, and Gen-III nuclear reactor systems since the 1950s, with the economy and safety being greatly enhanced as a result. To further improve the performance of reactor systems, Gen-IV advanced reactors, which encompass the gas-cooled fast reactor (GFR), lead-cooled fast reactor (LFR), molten salt reactor (MSR), sodium-cooled fast reactor (SFR), very-high-temperature reactor (VHTR), and super-critical water-cooled reactor (SCWR), were proposed in 2002 by the GIF (Generation IV International Forum) with the aim of achieving commercialization after 2030. Up until now, experimental reactors or demonstration reactors have been successfully built for the LFR, MSR, SFR, and VHTR.  Meanwhile, with a growing demand on the exploration of ocean/space and off-grid electricity supply for remote areas, micro-nuclear reactors (power capacity up to 20 MWe) and small modular nuclear reactors (power capacity of up to 300 MWe) have attracted growing attention worldwide.

The nuclear fuel cycle is a complex process, covering the steps of fuel life from uranium/thorium mining, uranium enrichment, fuel element manufacture, fuel burning in the core, and spent fuel processing to the final disposal of spent fuel. Potential safety risks exist in each step of the nuclear fuel cycle, and related issues are a great challenge even for advanced reactor systems due to the introduction of unique technical features. Accident analysis and experiments of nuclear fuel cycle reactors have become paramount for reactor systems’ design and license application. As computing technologies constantly improve, reactor safety analysis approaches real accident conditions with little approximation and can provide more reliable accident assessments. Presenting these latest advances in the nuclear industry would provide a valuable reference for scholars involved in research in the nuclear industry.

In this Special Issue, potential topics of interest include, but are not limited to, the following:

  • Accident modeling and analysis of the nuclear fuel cycle;
  • Nuclear safety computing codes development, validation, and application;
  • Accident probability analysis for micro-nuclear reactors and small modular nuclear reactors;
  • Safety analysis in advanced U-Pu and Th-U nuclear fuel cycle;
  • Application of machine learning methods in safety analysis of the nuclear fuel cycle.

Dr. Jianhui Wu
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 energy
  • nuclear fuel cycle
  • nuclear physics
  • thermal hydraulics
  • nuclear waste management
  • nuclear safety calculation code
  • micro-nuclear reactor
  • modular nuclear reactor
  • GEN-IV reactor
  • machine learning
  • nuclear safety
  • radiation shielding
  • nuclear control
  • nuclear materials

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Published Papers (1 paper)

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Research

11 pages, 2980 KiB  
Article
Research on Data-Driven Methods for Solving High-Dimensional Neutron Transport Equations
by Zhiqiang Peng, Jichong Lei, Zining Ni, Tao Yu, Jinsen Xie, Jun Hong and Hong Hu
Energies 2024, 17(16), 4153; https://doi.org/10.3390/en17164153 - 21 Aug 2024
Viewed by 357
Abstract
With the continuous development of computer technology, artificial intelligence has been widely applied across various industries. To address the issues of high computational cost and inefficiency in traditional numerical methods, this paper proposes a data-driven artificial intelligence approach for solving high-dimensional neutron transport [...] Read more.
With the continuous development of computer technology, artificial intelligence has been widely applied across various industries. To address the issues of high computational cost and inefficiency in traditional numerical methods, this paper proposes a data-driven artificial intelligence approach for solving high-dimensional neutron transport equations. Based on the AFA-3G assembly model, a neutron transport equation solving model is established using deep neural networks, considering factors that influence the neutron transport process in real engineering scenarios, such as varying temperature, power, and boron concentration. Comparing the model’s predicted values with reference values, the average error in the infinite multiplication factor kinf of the assembly is found to be 145.71 pcm (10−5), with a maximum error of 267.10 pcm. The maximum relative error is less than 3.5%, all within the engineering error standards of 500 pcm and 5%. This preliminary validation demonstrates the feasibility of using data-driven artificial intelligence methods to solve high-dimensional neutron transport equations, offering a new option for engineering design and practical engineering computations. Full article
(This article belongs to the Special Issue Advances in Simulation and Numerical Model of Nuclear Fuel Safety)
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