A methodology for digital mockup update based on the 3D scanned spatial information for the automated dismantling of nuclear facilities

Kim, Ikjune; Hyun, Dae Sung; Joo, Seung–Ki; Lee, Jong‐Hwan

doi:10.1016/j.anucene.2019.107238

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…They identified cylindrical objects within the scene and estimated their parameters using Random Sample Consensus (RANSAC). Alternatively, some studies have shown success in 3D scanning data including creating a VR model [13], building a 3D CAD model [14] [15] and using laser rangefinders and conventional cameras [16].…”

Section: Visual Waste Classificationmentioning

confidence: 99%

Object Detection in a Framework for Automated Nuclear Waste Classification

Hume¹,

Dobie²,

West³

2021

12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021)

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In this paper, we present a new framework for automatically triaging nuclear waste classification inside a nuclear cell for decommissioning. The process of decommissioning includes a large amount of human involvement for decision making, physical inspections and even lifting and relocating radioactive waste items. The current process accounts for risks like close human contact with radioactive material for extended periods of time, and errors based on operator knowledge rather than automated detection systems. The aims of this new framework are to reduce cost and speed up the sort and segregation process by providing a list of expected waste items, their location within the cell, and expected waste classification autonomously. We aim to reduce the reliance on the subjectivity of human decisions by capturing, formalizing and codifying their knowledge and experience and reducing the potential for errors arising from reliance on individuals. This paper focuses on the design and description of the framework and demonstration of the first step of the framework through a case study drawn from a mockup of a nuclear cell. We perform planar segmentation and cylinder detection on a point cloud dataset using RANSAC based methods to, firstly, distinguish indoor walls from objects for processing, and to detect objects and their estimated parameters.

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Section: Visual Waste Classificationmentioning

confidence: 99%

Object Detection in a Framework for Automated Nuclear Waste Classification

Hume¹,

Dobie²,

West³

2021

12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021)

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“…Очевидно преимущество применения цифровых технологий в области технического обслуживания и ремонта, демонтажа оборудования при выводе из эксплуатации. Например, в исследовании (Kim I. et al, 2020) предложена методология обновления цифрового макета для автоматизированного демонтажа ядерной установки и предложен алгоритм реализации прототипа программного обеспечения для тестирования методологии. Рос-сийскими исследователями изучение факторов, влияющих на дозы облучения персонала, моделирование ситуаций их снижения за счет использования современных инструментов ремонта, проводились на протяжении всей истории развития ядерных технологий.…”

Section: перспективные направления исследований цифровизации ядерной энергетикиunclassified

Industry 4.0 in nuclear energy: economic efficiency of the digitalization of the industry

Allenykh¹

2020

Druk. Vest.

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The article examines the global trends in the development of digital technologies, formulates the factors and limitations of digitalization of the electric power industry in Russia. The directions of implementation of digital technologies in nuclear energy are considered: digital energy, digital nuclear power plants, digital products using Smart Grid technologies, Cyber security technology and cross-technologies (IoT, IIoT, Machine learning, 3D / 4D modeling, VR / AR / MR, BIM , Digital drone, Data mining, Digital twins and Digital footprint, Smart Big Data and others). The issues of assessing the economic efficiency of the implementation and use of digital technologies are considered.

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“…Due to immature developments in computer technology at the time, these reactors lack detailed electronic drawings, and even comprehensive paper drawings are challenging to preserve. Furthermore, over the decades of their operation, activities such as equipment replacements, plant expansions, and process circuits modifications have inevitably led to discrepancies between the paper drawings of nuclear facilities and the actual conditions [3].…”

Section: Introductionmentioning

confidence: 99%

The Application of Laser-Scanning 3D Model Reconstruction Technology for Visualizing a Decommissioning Model of the Heavy Water Research Reactor

Li,

Zhang,

et al. 2024

Applied Sciences

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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.

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A methodology for digital mockup update based on the 3D scanned spatial information for the automated dismantling of nuclear facilities

Cited by 5 publications

References 10 publications

Object Detection in a Framework for Automated Nuclear Waste Classification

Object Detection in a Framework for Automated Nuclear Waste Classification

Industry 4.0 in nuclear energy: economic efficiency of the digitalization of the industry

The Application of Laser-Scanning 3D Model Reconstruction Technology for Visualizing a Decommissioning Model of the Heavy Water Research Reactor

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