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Dr. Dinesh Kumar
Department of Mechanical Engineering, University of Bristol, Bristol, UK
1. MARTIANS Lab (Machine Learning and ARTificial Intelligence for Advancing Nuclear Systems), Missouri University of Science and Technology, Rolla, MO 65409, USA
2. Nuclear Plasma and Radiological Engineering, University of Illinois Urbana, Champaign, IL 61801, USA
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Intelligent, Explainable and Trustworthy AI for Advanced Nuclear and Sustainable Energy Systems

Abstract submission deadline
closed (13 October 2023)
Manuscript submission deadline
closed (31 January 2024)
Viewed by
2625

Topic Information

Dear Colleagues,

Machine learning (ML) and artificial Intelligence (AI) are increasingly used in nuclear and sustainable energy systems. The United States Nuclear Regulatory Research (NRC) and the Department of Energy (DOE) initiated a significant research effort to determine the feasibility of ML/AI-driven techniques to advance energy systems research. These tools facilitate risk-informed decision-making and streamline high-performing simulations by analyzing vast amounts of data. However, these models must be fair, unbiased, explainable, and overall intelligent in nature to gain confidence in AI's trustworthiness. In order to assure trustworthiness for decision-making, ML/AI techniques need to be audited, accounted for, and easy to understand for the energy systems. Furthermore, the concepts of explainable AI (XAI) and interpretable machine learning (IML) need to be incorporated to understand the reasoning behind the prediction of complex energy systems. This understanding can lead to better maintenance and repair planning and improved system performance for sustainable energy systems. This Special Issue aims to explore potential improvements and current research in ML and AI that are explainable and trustworthiness and incorporate AI risk management for energy systems. Potential authors are encouraged to submit novel ideas, concepts, and results by following the submission guidelines.

Dr. Dinesh Kumar
Dr. Syed Bahauddin Alam
Topic Editors

Keywords

  • uncertainty quantification
  • surrogate modeling
  • uncertainty aware data-driven algorithms
  • explainable artificial intelligence
  • machine learning risk assessment
  • robust optimization

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai 		3.1 7.2 2020 18.9 Days CHF 1600
Algorithms
algorithms 		1.8 4.1 2008 18.9 Days CHF 1600
Applied Sciences
applsci 		2.5 5.3 2011 18.4 Days CHF 2400
Energies
energies 		3.0 6.2 2008 16.8 Days CHF 2600
Journal of Nuclear Engineering
jne 		- - 2020 38.3 Days CHF 1000

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Published Papers (1 paper)

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21 pages, 11559 KiB  
Article
The Application of Laser-Scanning 3D Model Reconstruction Technology for Visualizing a Decommissioning Model of the Heavy Water Research Reactor
by Wensi Li, Yu Zhang, Ruizhi Li, Lijun Zhang, Xingwang Zhang, Hongyin Li, Peng Nie and Shengdong Zhang
Appl. Sci. 2024, 14(8), 3135; https://doi.org/10.3390/app14083135 - 9 Apr 2024
Viewed by 1631
Abstract
Currently, over 100 nuclear power units globally have been in operation for more than 40 years. Hindered by the limitations of computer technology at the time, these nuclear facilities lack detailed electronic drawings. Activities such as equipment replacement and process circuit system modifications [...] Read more.
Currently, over 100 nuclear power units globally have been in operation for more than 40 years. Hindered by the limitations of computer technology at the time, these nuclear facilities lack detailed electronic drawings. Activities such as equipment replacement and process circuit system modifications during operation result in discrepancies between paper drawings and actual conditions. Given the complexity and irreversibility of nuclear facility decommissioning activities, virtual simulation technology is often employed before the decommissioning process begins to assist in designing and validating decommissioning plans. Consequently, the creation of high-precision 3D models is crucial for subsequent decommissioning designs. Through innovatively utilizing laser-scanning 3D model reconstruction technology in the reconstruction of the model of China’s first heavy water research reactor undergoing decommissioning, this paper provides an overview of the process of laser-scanning 3D model reconstruction and its application in reconstructing the heavy water research reactor model. Using a 3D laser scanner, four decommissioning areas of the heavy water research reactor, including the reactor building, secondary water pump room, ventilation center, and low-level radioactive wastewater storage tank area, were subjected to 3D laser scanning. The acquired point cloud data from 572 scanning stations were processed using point cloud processing software for denoising, stitching, and triangulation. The triangulated model was then imported into modeling software for 3D reconstruction, ultimately establishing a digitalized model of the heavy water research reactor suitable for subsequent decommissioning simulation and design. Full article
(This article belongs to the Topic Intelligent, Explainable and Trustworthy AI for Advanced Nuclear and Sustainable Energy Systems)
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