Radiological characterization of the reactor pressure vessel of Trino NPP for dismantling purposes

Energy Science & Engineering

et al. 2022

Kori 1, the first commercial reactor in Korea, was permanently shut down in 2017 and is preparing for decommissioning in the mid-2020s. Although Kori 1 is shut down, some radiation sources remain in the system; as such, the dismantling workers are exposed to radiation until decommissioning is completed. Unexpected radiation events have been reported in some decommissioning nuclear power plants (NPPs), which resulted in additional radiation exposure to workers and significant financial losses due to delays in the decommissioning schedule. Hence, an appropriate radiation protection program must be established to minimize radiation exposure as well as the economic burden. In this study, radiation doses from precedent decommissioning NPPs worldwide are compared with those estimated from Kori 1 to determine dose reduction measures that can achieve radiation exposure as low as reasonably achievable to decommissioning workers.

Section: Methodsmentioning

confidence: 99%

Comparative analysis of occupational radiation doses when decommissioning nuclear power plants

Lee

Kong

Energy Science & Engineering

et al. 2022

“…Radiation protection through shielding is finally determined by calculating the thickness based on the initial radiation resistance, the sources of radiation and the shielding material. According to the radiological characterization of the reactor pressure vessel in a nuclear power plant, the main source emitting gamma rays is Co-60 [ 20 , 21 ]. Half value layers vary according to the energy of gamma rays and shield materials.…”

Section: Radiation Protectionmentioning

confidence: 99%

“…The shielding thickness is calculated by following the basic equation [ 21 ] that assumes a narrow beam of radiation penetrating a thin shield.

where X is the exposure rate with the shield in place, X 0 is the exposure rate without the shield, u/ρ is the mass attenuation coefficient, ρ is the density of the shielding material, and x is the thickness of the shield.…”

Section: Radiation Protectionmentioning

confidence: 99%

3D Point Cloud Acquisition and Correction in Radioactive and Underwater Environments Using Industrial 3D Scanners

Hyun

Joo

et al. 2022

Sensors

This study proposes a method to acquire an accurate 3D point cloud in radioactive and underwater environments using industrial 3D scanners. Applications of robotic systems at nuclear facility dismantling require 3D imaging equipment for localization of target structures in radioactive and underwater environments. The use of industrial 3D scanners may be a better option than developing prototypes for researchers with basic knowledge. However, such industrial 3D scanners are designed to operate in normal environments and cannot be used in radioactive and underwater environments. Modifications to environmental obstacles also suffer from hidden technical details of industrial 3D scanners. This study shows how 3D imaging equipment based on the industrial 3D scanner satisfies the requirements of the remote dismantling system, using a robotic system despite insufficient environmental resistance and hidden technical details of industrial 3D scanners. A housing unit is designed for waterproofing and radiation protection using windows, mirrors and shielding. Shielding protects the industrial 3D scanner from radiation damage. Mirrors reflect the light required for 3D scanning because shielding blocks the light. Windows in the waterproof housing also transmit the light required for 3D scanning with the industrial 3D scanner. The basic shielding thickness calculation method through the experimental method is described, including the analysis of the experimental results. The method for refraction correction through refraction modeling, measurement experiments and parameter studies are described. The developed 3D imaging equipment successfully satisfies the requirements of the remote dismantling system: waterproof, radiation resistance of 1 kGy and positional accuracy within 1 mm. The proposed method is expected to provide researchers with an easy approach to 3D scanning in radioactive and underwater environments.

“…Pressure vessel cracks identification can be performed by collecting data (i.e., ultrasonic signals, eddy-current signals, thermal images, and acoustic emission signals), which has been an important aspect of studies conducted over the last couple of decades [1,8,9,10]. These fault identification studies prove that diagnosis of the pressure vessel can reduce maintenance expenses by enhancing the reliability of equipment.…”

Section: Introductionmentioning

confidence: 99%

“…These fault identification studies prove that diagnosis of the pressure vessel can reduce maintenance expenses by enhancing the reliability of equipment. In the field of pressure vessel crack identification, ultrasonic signals and eddy currents have been widely exploited [4,10]. Alternatively, acoustic emission (AE) monitoring has gained significant attention recently in the field of pressure vessel monitoring since AE signals can capture intrinsic information from low-energy signals, even when the crack size is very small or structural deformation or cracks are not visible on the pressure vessel surface [11].…”

Section: Introductionmentioning

confidence: 99%

Crack Classification of a Pressure Vessel Using Feature Selection and Deep Learning Methods

Islam

Sohaib

2018

Sensors

Pressure vessels (PV) are designed to hold liquids, gases, or vapors at high pressures in various industries, but a ruptured pressure vessel can be incredibly dangerous if cracks are not detected in the early stage. This paper proposes a robust crack identification technique for pressure vessels using genetic algorithm (GA)-based feature selection and a deep neural network (DNN) in an acoustic emission (AE) examination. First, hybrid features are extracted from multiple AE sensors that represent diverse symptoms of pressure vessel faults. These features stem from various signal processing domains, such as the time domain, frequency domain, and time-frequency domain. Heterogenous features from various channels ensure a robust feature extraction process but are high-dimensional, so may contain irrelevant and redundant features. This can cause a degraded classification performance. Therefore, we use GA with a new objective function to select the most discriminant features that are highly effective for the DNN classifier when identifying crack types. The potency of the proposed method (GA + DNN) is demonstrated using AE data obtained from a self-designed pressure vessel. The experimental results indicate that the proposed method is highly effective at selecting discriminant features. These features are used as the input of the DNN classifier, achieving a 94.67% classification accuracy.