Intelligent, Explainable and Trustworthy AI for Advanced Nuclear and Sustainable Energy Systems

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	-	-	2020	20.8 Days	CHF 1600
Algorithms algorithms	2.3	3.7	2008	15 Days	CHF 1600
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600
Journal of Nuclear Engineering jne	-	-	2020	23.5 Days	CHF 1000

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Published Papers (1 paper)

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All AI Algorithms Applied Sciences Energies JNE

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21 pages, 11559 KiB  

Open AccessArticle

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 - 09 Apr 2024

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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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