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Energies
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Development and Application of Innovative Nuclear Energy Systems
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Development and Application of Innovative Nuclear Energy Systems

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

Deadline for manuscript submissions: closed (24 October 2024) | Viewed by 805

Special Issue Editors

Dr. Donny Hartanto

E-Mail Website
Guest Editor
Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Interests: nuclear energy; reactor physics
Dr. Friederike Bostelmann

E-Mail Website
Guest Editor
Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Interests: reactor physics; nuclear data
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

Special Issue Information

Dear Colleagues,

Nuclear energy stands as a cornerstone of sustainable and reliable energy sources, effectively meeting global energy demands while actively contributing to mitigating climate change challenges. As we transition towards cleaner energy systems, nuclear power emerges as a critical component, offering advantages that are essential for a sustainable future.

The ongoing evolution of nuclear technology brings forth continuous innovations in reactor designs, fuel cycle, safety systems, and waste management. These advancements significantly enhance efficiency, safety, and introduce new applications such as nuclear–renewable hybrid systems, industrial heat applications, medical isotope production, and space exploration.

This Special Issue aims to showcase the latest developments and applications in innovative nuclear energy systems, providing a comprehensive platform to discuss nuclear energy’s pivotal role in shaping a sustainable future. Specifically, we invite contributions in the following key areas:

  1. Advanced Reactor Concepts: Exploration of advanced reactor designs such as molten salt reactors, fast reactors, gas-cooled reactors, thorium-based reactors, and small modular reactors (SMRs), focusing on their technological advancements, reactor physics and thermal hydraulics analyses, safety features, and potential applications.
  2. Next-Generation Nuclear Fuels: Research on advanced nuclear fuel materials, cladding technologies, fuel cycle innovations, and fuel performance assessments in advanced/innovative reactor designs.
  3. Computational Modelling and Simulation: High-fidelity simulations, multi-physics modelling, machine learning and artificial intelligence applications, uncertainty quantification, and validation studies for reactor design, and optimization of nuclear systems.

Dr. Donny Hartanto
Dr. Friederike Bostelmann
Prof. Dr. Enrico Zio
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 energy
  • innovative nuclear energy systems
  • nuclear reactor design
  • nuclear reactor physics
  • nuclear thermal hydraulics
  • nuclear fuel cycle
  • computational modelling and simulation
  • innovative/advanced safety solutions for nuclear systems
  • AI in innovative nuclear systems
  • autonomous solutions for innovative nuclear systems

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Published Papers (1 paper)

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Research

25 pages, 2528 KiB  
Article
Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification
by Rita Appiah, Alexander Heifetz, Derek Kultgen, Lefteri H. Tsoukalas and Richard B. Vilim
Energies 2024, 17(24), 6257; https://doi.org/10.3390/en17246257 - 11 Dec 2024
Viewed by 465
Abstract
This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based [...] Read more.
This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems. Full article
(This article belongs to the Special Issue Development and Application of Innovative Nuclear Energy Systems)
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