• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1224-1230
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• 2007

In this paper, we study the classification of defects at steam generator tube in nuclear power plant using eddy current testing (ECT). We consider 4 defect patterns of SG tube: I-In type, I-Out type, V-In type, and V-Out type. Through numerical analysis program based on finite element modeling, 400 ECT signals are generated by varying width and depth of each defect type. In order to improve the classification performance, we propose new feature extraction technique. After extracting new features from the generated ECT signals, multi-layer perceptron is used to classify the defect patterns. Through the computer simulation study, it is shown that the proposed method achieves 100% classification success rate while the previous method yields 91% success rate.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.475-478
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• 2005

Calculation of wear depth with regard to the wear topology is peformed numerically Four typical wear topology, that is round, crescent, flat, and diamond types are adopted to represent the configuration of wear volume. Diamond and flat types are the most severe topology for wear depth history, whereas round and crescent types have small increasing rate of wear depth to the wear volume. Based on this study we can guess that the most severe wear phenomena happens to be upper side of U-tubes in the KSNP SG, because flat or diamond wear will be generated by the wearing motion between tubes and diagonal, vertical, horizontal strips. The misalignment of tube at the stage of manufacturing or distortion of upper structure due to the thermal expansion or vibration of upper structure such as diagonal, vertical, and horizontal strips will be one of the main causes of flat or diamond wear.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1374-1381
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• 2022

Under the influence of nuclear radiation, the reliability of steam generators (SGs) is an important factor in the efficiency and safety of nuclear power plant (NPP) reactors. Motion planning that remotely manipulates an SG mobile tube-inspection robot to inspect SG heat transfer tubes is the mainstream trend of NPP robot development. To achieve motion planning, conditional traversal is usually used for base position optimization, and then the A^* algorithm is used for path planning. However, the proposed approach requires considerable processing time and has a single expansion during path planning and plan paths with many turns, which decreases the working speed of the robot. Therefore, to reduce the calculation time and improve the efficiency of motion planning, modifications such as the matrix method, improved parent node, turning cost, and improved expanded node were proposed in this study. We also present a comprehensive evaluation index to evaluate the performance of the improved algorithm. We validated the efficiency of the proposed method by planning on a tube sheet with square-type tube arrays and experimenting with Model SG.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.4 no.2
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• pp.34-39
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• 2008

Steam Generator(SG) tubes are classified as one of the key components in nuclear power plants, and they should be periodically examined by the intensified management program for the assurance and diagnosis of their structural integrity. In this study, we use the optimum analysis technique to draw the detection and categorization of bowing(BOW) signals; abnormal tube-to-tube proximity in the SG upper bundle free span area. The locations in which BOW signals are detected likely have latent degradation of ODSCC(Outer Diameter Stress Corrosion Cracking). For the sake of timely and correct detection of BOW signals and diagnosis of ODSCC, we carried out the experimental demonstrations using a reduced mock-up. And we validated the MRPC(Motorized Rotating Pancake Coil) analysis technique is better than the bobbin. Hence, it comes to conclusion that the optimum analysis technique can be a good alternative for the reliable SG tube examination.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.917-925
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• 2022

Steam generator (SG) tubes in a nuclear power plant can undergo rapid changes in pressure and temperature during an accident; thus, an accurate model to predict short-term creep damage is essential. The theta (𝜃) projection method has been widely used for modeling creep-strain behavior under constant stress. However, many creep test data are obtained under constant load, so creep rupture behavior under a constant load cannot be accurately simulated due to the different stress conditions. This paper proposes a novel methodology to obtain the creep curve under constant stress using a modified 𝜃 projection method that considers the increase in true stress during creep deformation in a constant-load creep test. The methodology is validated using finite element analysis, and the limitations of the methodology are also discussed. The paper also proposes a creep-strain model for alloy 690 as an SG material and a novel creep hardening rule we call the damage-fraction hardening rule. The creep hardening rule is applied to evaluate the creep rupture behavior of SG tubes. The results of this study show its great potential to evaluate the rupture behavior of an SG tube governed by creep deformation.

• Nuclear Engineering and Technology
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• v.41 no.1
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• pp.125-134
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• 2009

This paper presents details on a tube inspection robotic system and a positioning method of the robot for a steam generator (SG) in nuclear power plants (NPPs). The robotic system is separated into three parts for easy handling, which reduces the radiation exposure during installation. The system has a supporting leg to increase the rigidity of the robot base. Since there are several thousands of tubes to be inspected inside a SG, it is very important to position the tool of the robot at the right tubes even if the robot base is positioned inaccurately during the installation. In order to obtain absolute accuracy of a position, the robot kinematics was mathematically modeled with the modified DH(Denavit-Hartenberg) model and calibrated on site using tube holes as calibration points. To tune the PID gains of a commercial motor driver systematically, the time delay control (TDC) based gain tuning method was adopted. To verify the performance of the robotic system, experiments on a Framatomes 51B Model type SG mockup were undertaken.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.137-145
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• 1998

In this paper, we propose a tube-hole center detection vision algorithm verifying the position of a tele-controlled robot and providing visual information for increasing reliability and efficiency in the diagnosis of steam generator (SG) tubes in nuclear power plant. A tele-controlled robot plays a role in carrying the probe used in inspecting the integrity of SG tubes. Thus accurately locating a tele-controlled robot on the desired tube-hole center is important issue for reliability of inspection. To do this work, we have to find the tube-hole center locations from the input image. At first, we apply the three-class segmentation method modified for this application. WE extract minimum bounding rectangles (MBRs) in the theresholded binary image. Second, for discriminating between MBR by tube and MBR by noise, we introduce the MBR rejection rules as knowledge-based rule set. MBRs are divided into the very dark region MBRs and the very bright region MBRs. In order to describe the region of complete tube-hole, the MBRs need a process of pairing each other. We then can find the tube-hole center from the paired MBR. For more accurately finding the tube-hole center in several sequential images, the centers of some frames need to be averaged. We tested the performance of our method using hundreds of real images.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.4
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• pp.374-382
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• 2009

Steam generator(SG) tube, as a barrier isolating primary to the secondary coolant system of nuclear power plants(NPP), must maintain the structural integrity far the public safety and its efficient power generation capacity. And SG tubes bearing defects must be timely detected and taken repair measures if needed. For the accomplishment of these objectives, SG tubes have been periodically examined by eddy current testing(ECT) on the basis of administrative notices and intensified SG management program(SGMP). Stress corrosion cracking(SCC) on the SG tubes is not easily detected and even missed since it has lower signal amplitude and other disturbing factors against its detection. However once SCC is developed, that can cause detrimental affects to the SG tubes due to its rapid propagation rate. Accordingly SCC is categorized as prime damage mechanism challenging the soundness of the SG tubes. In this study, reproduced EDM notch specimens are examined for the detectability and quantitative characterization of the axial ODSCC by +PT MRPC probe, containing pancake, +PT and shielded pancake coils apart in a single plane around the circumference. The results of this study are assumed to be applicable fur providing key information of engineering evaluation of SCC and improvement of confidence level of ECT on SG tubes.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.1
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• pp.13-19
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• 2010

In this paper, we study a new estimation system for the prediction of steam generator tube defects. In the previous research works, defect size estimators were independently designed for each defect types in order to estimate the defect size. As a result, the structure of estimation system is rather complex and the estimation performance gets worse if the classification performance is degraded for some reason. This paper studies a new estimation system that does not require the classification of defect types. Although the previous works are expected to achieve much better estimation performance than the proposed system since it uses the estimator specialized in each defect, the performance difference is not so large. Therefore, it is expected that the proposed estimator can be effectively used for the case where the defect type classification is imperfect.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.302-310
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• 2010

In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.