Pipeline wall thinning rate prediction model based on machine learning
| Autor: | Dong-Jin Kim, Seong-In Moon, Gyeong-Geun Lee, Kyung Mo Kim, Yongkyun Yu |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Piping Wall thinning business.industry Computer science Pipeline (computing) TK9001-9401 ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) Convolutional neural network Construct (python library) Machine learning computer.software_genre Nuclear Energy and Engineering Nuclear engineering. Atomic power Artificial intelligence Flow-accelerated corrosion business computer Predictive modelling |
| Zdroj: | Nuclear Engineering and Technology, Vol 53, Iss 12, Pp 4060-4066 (2021) |
| ISSN: | 1738-5733 |
| DOI: | 10.1016/j.net.2021.06.040 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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