Pipeline wall thinning rate prediction model based on machine learning
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 prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propos...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-12-01
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| Series: | Nuclear Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573321003879 |
| _version_ | 1818721504363282432 |
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| author | Seongin Moon Kyungmo Kim Gyeong-Geun Lee Yongkyun Yu Dong-Jin Kim |
| author_facet | Seongin Moon Kyungmo Kim Gyeong-Geun Lee Yongkyun Yu Dong-Jin Kim |
| author_sort | Seongin Moon |
| collection | DOAJ |
| description | 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 prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a 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 network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation. |
| first_indexed | 2024-12-17T20:39:47Z |
| format | Article |
| id | doaj.art-fa49902b8ee7438c9554ab7ccacc6846 |
| institution | Directory Open Access Journal |
| issn | 1738-5733 |
| language | English |
| last_indexed | 2024-12-17T20:39:47Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj.art-fa49902b8ee7438c9554ab7ccacc68462022-12-21T21:33:22ZengElsevierNuclear Engineering and Technology1738-57332021-12-01531240604066Pipeline wall thinning rate prediction model based on machine learningSeongin Moon0Kyungmo Kim1Gyeong-Geun Lee2Yongkyun Yu3Dong-Jin Kim4Corresponding author.; Korea Atomic Energy Research Institute, Daejeon, 34057, South KoreaKorea Atomic Energy Research Institute, Daejeon, 34057, South KoreaKorea Atomic Energy Research Institute, Daejeon, 34057, South KoreaKorea Atomic Energy Research Institute, Daejeon, 34057, South KoreaKorea Atomic Energy Research Institute, Daejeon, 34057, South KoreaFlow-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 prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a 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 network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation.http://www.sciencedirect.com/science/article/pii/S1738573321003879Flow-accelerated corrosionWall thinningMachine learningArtificial neural networkConvolutional neural network |
| spellingShingle | Seongin Moon Kyungmo Kim Gyeong-Geun Lee Yongkyun Yu Dong-Jin Kim Pipeline wall thinning rate prediction model based on machine learning Nuclear Engineering and Technology Flow-accelerated corrosion Wall thinning Machine learning Artificial neural network Convolutional neural network |
| title | Pipeline wall thinning rate prediction model based on machine learning |
| title_full | Pipeline wall thinning rate prediction model based on machine learning |
| title_fullStr | Pipeline wall thinning rate prediction model based on machine learning |
| title_full_unstemmed | Pipeline wall thinning rate prediction model based on machine learning |
| title_short | Pipeline wall thinning rate prediction model based on machine learning |
| title_sort | pipeline wall thinning rate prediction model based on machine learning |
| topic | Flow-accelerated corrosion Wall thinning Machine learning Artificial neural network Convolutional neural network |
| url | http://www.sciencedirect.com/science/article/pii/S1738573321003879 |
| work_keys_str_mv | AT seonginmoon pipelinewallthinningratepredictionmodelbasedonmachinelearning AT kyungmokim pipelinewallthinningratepredictionmodelbasedonmachinelearning AT gyeonggeunlee pipelinewallthinningratepredictionmodelbasedonmachinelearning AT yongkyunyu pipelinewallthinningratepredictionmodelbasedonmachinelearning AT dongjinkim pipelinewallthinningratepredictionmodelbasedonmachinelearning |