As nuclear decommissioning ventures become increasingly complex, the role of digitalization in facilitating and enhancing these operations is becoming indispensable. This transition to a more digitized approach presents a myriad of advantages, including: augmented avenues for data acquisition, analysis, and visualization to bolster dismantling strategies; simulations in virtual environments for operator training; precise forecasting of future waste emergence, culminating in refined cost estimations; and more immersive decommissioning visualizations for both operators and external stakeholders. Salient benefits conferred by the integration of digital technologies in decommissioning encompass improved collaboration, enriched knowledge transfer, clarity regarding present technological constraints, insights into key influencing factors, clearer criteria for technology selection, and a profound understanding of the potential challenges and merits of a broader incorporation of digital tools in decommissioning endeavors. Of paramount importance is the opportunity presented for superior workforce training and safety measures, exemplified by ALARAbased planning. Amidst the myriad facets of digital adoption, 3D modeling of nuclear facilities derived from laser-scanned point clouds stands out as a pivotal domain in the digitalization. The transformation of intricate point cloud data into a comprehensible 3D mesh remains the crux of this paper. The process of mesh generation, despite being simpler than its counterpart of converting to a 3D solid model, is crucial for multiple reasons. The resultant 3D mesh offers an enhanced visual representation compared to a sparse point cloud, paving the way for improved spatial perception. Furthermore, it serves as a rudimentary tool for approximating component volumes and the ensuing waste, thereby playing an instrumental role in waste manipulation strategies, notably in collision detection. This paper delves deep into the nuances of mesh generation, conducting an parametric study of mesh conversion algorithms, including down-sampling rates. Through this rigorous examination, we endeavor to shed light on optimal methodologies, hoping to catalyze advancements in the digitalization of nuclear decommissioning processes.

Nuclear facilities present the important task related to the migration and retention of radioactive contaminants such as cesium (Cs), strontium (Sr), and cobalt (Co) for unexpected events in various environmental conditions. The distribution coefficient (Kd) is important factor for understanding these contaminants mobility, influenced by environmental variables. This study focusses the prediction of Kd values for radionuclides within solid phase groups through the application of machine-learning models trained on experimental data and open source data from Japan atomic energy agency. Three machine-learning models, such as the convolutional neural network, artificial neural network, and random forest, were trained for prediction model of the distribution coefficient (Kd). Fourteen input variables drawn from the database and experimental data, including parameters such as initial concentration, solid-phase characteristics, and solution conditions, served as the basis for model training. To enhance model performance, these variables underwent preprocessing steps involving normalization and log transformation. The performances of the models were evaluated using the coefficient of determination. These results showed that the environmental media, initial radionuclide concentration, solid phase properties, and solution conditions were significant variables for Kd prediction. These models accurately predict Kd values for different environmental conditions and can assess the environmental risk by analyzing the behavior of radionuclides in solid phase groups. The results of this study can improve safety analyses and longterm risk assessments related to waste disposal and prevent potential hazards and sources of contamination in the surrounding environment.

The mobility of radionuclides is largely determined by their radiological properties, geochemical conditions, and adsorption reactions, such as surface adsorption, chemical precipitation, and ion exchange. To evaluate the safety assessments of radionuclides in nuclear sites, it is essential to understand the behavior and mechanism of radionuclides onto soils. Since nuclear power plants are located in coastal areas, the chemical composition of groundwater can vary depending on the intrusion of seawater, altering the adsorption distribution coefficient (Kd) values of radionuclides. This study examines the impact of seawater on the Kd values of clay minerals for cesium (Cs) and strontium (Sr). The results of Cs+ adsorption experiments showed a broad range of Kd values from 36 to 1,820 mL/g at an initial concentration of 1 mg/L and a high sorption coefficient of 15-613 mL/g at an initial concentration of 10 mg/L. Montmorillonite, hydrobiotite, illite, and kaolinite were ranked in terms of their CEC values for Cs+ adsorption, with hydrobiotite having the highest adsorption at 1 mg/L. The results of Sr adsorption experiments showed a wide range of Kd values from 82 to 1,209 mL/g at an initial concentration of 1 mg/L and a lower adsorption coefficient of 6.68-344 mL/g at an initial concentration of 10 mg/L. Both Cs+ and Sr2+ demonstrated lower Kd values at higher initial concentrations. CEC of clays found to have a significant impact on Sr2+ Kd values. Ca2+ ions showed a significant impact on Sr2+ adsorption distribution coefficients, demonstrating the greater impact of seawater on Sr2+ compared to Cs+. These findings can inform future safety assessments of radionuclides in nuclear sites.

This study presents an example of creating and optimizing a task sequence required in an automated remote dismantling system using a digital manufacturing system. An automated remote dismantling system using a robotic arm has recently been widely studied to improve the efficiency and safety of the dismantling operations. The task sequence must be verified in advance through discrete eventbased process simulation in a digital manufacturing system to avoid problems in actual remote cutting operations as the main input of the automated remote dismantling system. The laser cutting method can precisely cut complicated target structures such as reactor internals with versatility, but a robot and a pre-prepared program are required to deploy sophisticated motion of the laser cutting head on the target structure. For safe and efficient dismantling operations, the robot’s program must be verified in advance in a virtual environment that can represent the actual dismantling site. This study presents creating and optimizing the task sequence of a robotic underwater laser cutting as part of the project of developing an automated remote dismantling system. A task sequence is created to implement the desired cutting path for the target structure using the automated remote dismantling system in the virtual environment. The task sequence is optimized for the posture of the laser cutting head and the robot to avoid collisions during the operation through discrete event-based process simulation since the target structure is complicated and the volume occupied by the laser cutting head and the robot arm is considerably large. The task sequence verified in the digital manufacturing system is demonstrated by experiments cutting the target structure along the desired cutting path without any problems. The various simulation cases presented in this study are expected to contribute not only to the development of the automated remote dismantling system, but also to the establishment of a safe and efficient dismantling process in the nuclear facility decommissioning.

The RPV internal structure is a high radio activated part and has very complex geometry. Therefore, it needs to be cut remotely with an automated cutting system to minimize the worker exposures. To do so, we made up the remote laser cutting system with a laser cutter, robot manipulator and control software system and the laser cutter is moved by the robot manipulator based on the command from the control software system. A laser cutter is required to keep the desired standoff position between the nozzle of the laser cutter and surface of the cut target model to cut properly. Moreover, in the remote cutting process, an exact time and sequence control of the air supply and the laser emission is required for the cutting quality and the process safety. In this study, we proposed the PERT chart-based process execution and control methodology. The PERT chart is a graph which is represented by nodes and edges. The node of the PERT chart has the information about the activity details such as activity type, execution time and related device. Using the edge we make the sequence of the desired activity execution. A PERT chart of the cutting scenario is compiled in the control software system to creates data and thread structure to operate the physical device. We built software architecture to interpret and execute the PERT chart efficiently in the digital simulation platform which enables us to use existing pre-built simulation scenario for the laser cutting process. In addition, we have tested various laser cutting test cases in our test bed to verify the performance of our system. The test bed environment has the shape of the RPV internal structure and is placed under water.

For highly contaminated elements such as reactor pressure vessels or reactor internals, it is a viable option to cool-down and dismantle these elements in submerged (e.g. underwater) state. Several tools and processes such as saw cutting, water jet cutting or plasma cutting are currently used for underwater cutting, with each of them having their own advantages and disadvantages. The main disadvantage of these existing methods, especially saw and water jet cutting, is the generation of secondary waste that then needs to be filtered out of the water. In addition, in the case of water jet cutting, a considerable amount of abrasive material is added, which must also be stored. To overcome this drawback, the feasibility of using laser cutting under water to minimize secondary waste production has been actively studied recently. One of the challenges with the underwater laser cutting is to visually monitor the cutting process. Flowing air bubbles generated by the cutting gas and the flashing light emitted from the laser and melting material prohibit visual monitoring of the cutting process. This study introduces a method to enhance the video from a monitoring camera. Air bubbles can be detected by computing optical flows and the video quality can be enhanced by selective removal of the detected bubbles. In addition, suppressing the frame image update from flashing light area can also effectively enhance the video quality. This paper describes the simple yet effective video quality enhancement method and reports preliminary results.

3D imaging equipment is essential for automated robotic operations that cut radiologically contaminated structure and transfer segmented pieces in nuclear facility dismantling site. Automated dismantling operations using programmed robotic arms can make conventional nuclear facility dismantling operations much more efficient and safer, so dismantling technologies using robotic arms are being actively researched. Resolving the position uncertainty of the target structure is very important in automated robot work, and in general industries, the problem of position uncertainty is solved through the method of teaching the robot in the field, but at the nuclear facility dismantling site, the teaching method by workers is impossible due to activated target structures. Therefore, 3D imaging equipment is a key technology for a remote dismantling system using automated robotic arms at nuclear facility dismantling site where teaching methods are impossible. 3D imaging equipment available in radioactive and underwater environments is required to be developed for a remote dismantling system using robotic arms because most commercial 3D scanners are available in air and certain 3D scanners available in radioactive and underwater environments cannot satisfy requirements of the remote dismantling system such as measurement range and radiation resistance performance. The 3D imaging equipment in this study is developed based on an industrial 3D scanner available in air for efficient development. To protect the industrial 3D scanner against water and radiation, a housing is designed by using mirrors, windows and shieldings. To correct measurement errors caused by refraction, refraction model for the developed 3D imaging equipment is defined and parameter studies for uncertain variables are performed. The 3D imaging equipment based on the industrial 3D scanner has been successfully developed to satisfy the requirements of the remote dismantling system. The 3D imaging equipment can survive up to a cumulative dose of 1 kGy and can measure a 3D point cloud in the air and in water with an error of less than 1 mm. To achieve the requirements, a proper industrial 3D scanner is selected, a housing and shielding for water and radiation protection is designed, refraction correction are performed. The developed 3D imaging equipment is expected to contribute to the wider application of automated robotic operations in radioactive or underwater environments.

The remote dismantling system proposed in this paper is a system that performs the actual dismantling process using the process and program predefined in the digital manufacturing system. The key to the successful applying this remote dismantling system is how to overcome the problem of the difference between the digital mockup and the actual dismantling site. In the case of nuclear facility decommissioning, compensation between the virtual world and the real world is difficult due to harsh environments such as unsophisticated dismantling sites, radiation, and underwater, while offline programming can be proposed as a solution for other industries due to its sophisticated and controllable environment. In this paper, the problem caused by the difference in the digital mockup is overcome through three steps of acquisition of 3D point cloud in radiation and underwater environment, refraction correction, and 3D registration. The 3D point cloud is acquired with a 3D scanner originally developed in our laboratory to achieve 1 kGy of radiation resistance and water resistance. Refraction correction processes the 3D point cloud acquired underwater so that the processed 3D point cloud represents the actual position of the scanned object. 3D registration creates a transformation matrix that can transform a digital mockup of the virtual world into the actual location of a scanned object at the dismantling site. The proposed remote dismantling system is verified through various cutting experiments. In the experiments, the cutting test object has a shape similar to the reactor upper internals and is made of the same material as the reactor upper internals. The 105 successful experiments demonstrate that the proposed remote dismantling system successfully solved the key problem presented in this paper.