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문성인,김종민,권준엽,이봉상,최권재,김민철,한상배 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.3
Steam generator (SG) tubes in a nuclear power plant can undergo rapid changes in pressure and temperatureduring an accident; thus, an accurate model to predict short-term creep damage is essential. The theta (q) projection method has been widely used for modeling creep-strain behavior under constantstress. However, many creep test data are obtained under constant load, so creep rupture behavior undera constant load cannot be accurately simulated due to the different stress conditions. This paper proposesa novel methodology to obtain the creep curve under constant stress using a modified q projectionmethod 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 arealso discussed. The paper also proposes a creep-strain model for alloy 690 as an SG material and a novelcreep hardening rule we call the damage-fraction hardening rule. The creep hardening rule is applied toevaluate the creep rupture behavior of SG tubes. The results of this study show its great potential toevaluate the rupture behavior of an SG tube governed by creep deformation.
문성인,강토,한순우,전준영,박규해 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.11
In plate-like structures, wall-thinning defects resulting from corrosion may not be accompanied by any indication of damage on the surface. Thus, inspections are required to ensure that wall-thinning defects are within allowable limits. However, conventional ultrasonic techniques require physical contact to the structure. Alternatively, acoustic wavenumber spectroscopy (AWS) may be used for detecting, locating, and characterizing defects. This paper describes the performance of AWS in the estimation of a wall-thinning defect size in thinplate structures using finite element analysis (FEA). Through a series of FEAs, the structure’s steady-state response to a single-tone ultrasonic excitation is simulated, and the wall-thinning defect-size effect on the wavenumber-estimation accuracy is investigated. In general, the A0 guided wave mode is widely used to visualize defects because of the nature of the wave speed variation in relation to the plate thickness. However, it is not appropriate for the detection of relatively shallow wall-thinning defects, because the rate of change in wave speed with the thickness decreases with increasing plate thickness. To overcome this limitation, we propose a method to optimize excitation frequency and effective guided wave mode instead of utilizing the A0 mode. The results can be used to determine the size of shallow wall-thinning defects in plate-like structures.
Plate bending wave propagation behavior under metal sphere impact loading
문성인,강토,서정석,이정한,한순우,박진호 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.3
A loose part can cause component damages and material wear on nuclear power plants; thus, a mass estimation of the loose part is crucial to safety management. A bending wave propagation of a structure under the loose part impact loading is precisely simulated to accurately estimate the mass of a loose part. Lamb’s general solution for an arbitrary impact force function and Hertz impact theory have been used to identify the characteristics of the bending wave that is impacted by a metallic loose part in reactor pressure boundary components. However, these approaches cannot provide accurate information on the acceleration response that is required to identify the impact source. In this study, the bending wave propagation behavior of plate structures under a simulated loose part (Metal sphere) impact loading was modeled using a Finite element analysis (FEA) technique. The characteristics (e.g., Maximum acceleration amplitude, primary frequency, and bending wave velocity) of the impact response signal from a metal sphere were analyzed with the FEA results and were verified with experimental results. In addition, the correlation between plate thickness and characteristic length was presented. Results from this study can be utilized to estimate the location and mass of a loose part for condition monitoring and diagnostics in nuclear power plants.
Pipeline wall thinning rate prediction model based on machine learning
문성인,김경모,이경근,유용균,김동진 한국원자력학회 2021 Nuclear Engineering and Technology Vol.53 No.12
Flow-accelerated corrosion (FAC) of carbon steel piping is a significant problem in nuclear power plants. The basic process of FAC is currently understood relatively well; however, the accuracy of predictionmodels of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose amethodology to construct pipe wall-thinning rate prediction models using artificial neural networks anda convolutional neural network, which is confined to a straight pipe without geometric changes. Furthermore, a methodology to generate training data is proposed to efficiently train the neural networkfor the development of a machine learning-based FAC prediction model. Consequently, it is concludedthat machine learning can be used to construct pipe wall thinning rate prediction models and optimizethe number of training datasets for training the machine learning algorithm. The proposed methodologycan be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rateprediction model for a real situation