Numerical simulation and ANN prediction of crack problems within corrosion defects
Buried pipelines are widely used, so it is necessary to analyze and study their fracture characteristics. The locations of corrosion defects on the pipe are more susceptible to fracture under the influence of internal pressure generated during material transportation. In the open literature, a large...
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sg-ntu-dr.10356-1805922024-10-19T16:48:48Z Numerical simulation and ANN prediction of crack problems within corrosion defects Ren, Meng Zhang, Yanmei Fan, Mu Xiao, Zhongmin School of Mechanical and Aerospace Engineering Engineering Buried pipeline Corrosion defect Buried pipelines are widely used, so it is necessary to analyze and study their fracture characteristics. The locations of corrosion defects on the pipe are more susceptible to fracture under the influence of internal pressure generated during material transportation. In the open literature, a large number of studies have been conducted on the failure pressure or residual strength of corroded pipelines. On this basis, this study conducts a fracture analysis on buried pipelines with corrosion areas under seismic loads. The extended finite element method was used to model and analyze the buried pipeline under seismic load, and it was found that the stress value at the crack tip was maximum when the circumferential angle of the crack was near 5° in the corrosion area. The changes in the stress field at the crack tip in the corrosion zone of the pipeline under different loads were compared. Based on the BP algorithm, a neural network model that can predict the stress field at the pipe crack tip is established. The neural network is trained using numerical model data, and a prediction model with a prediction error of less than 10% is constructed. The crack tip characteristics were further studied using the BP neural network model, and it was determined that the tip stress fluctuation range is between 450 MPa and 500 MPa. The neural network model is optimized based on the GA algorithm, which solves the problem of convergence difficulties and improves the prediction accuracy. According to the prediction results, it is found that when the internal pressure increases, the corrosion depth will significantly affect the crack tip stress field. The maximum error of the optimized neural network is 5.32%. The calculation data of the optimized neural network model were compared with the calculation data of other models, and it was determined that GA-BPNN has better adaptability in this research problem. Published version This work was supported by the Key Laboratory of New Technology for Construction of Cities in Mountain Aera, Ministry of Education, Chongqing University (LNTCCMA-20210111). 2024-10-14T05:47:46Z 2024-10-14T05:47:46Z 2024 Journal Article Ren, M., Zhang, Y., Fan, M. & Xiao, Z. (2024). Numerical simulation and ANN prediction of crack problems within corrosion defects. Materials, 17(13), 3237-. https://dx.doi.org/10.3390/ma17133237 1996-1944 https://hdl.handle.net/10356/180592 10.3390/ma17133237 2-s2.0-85198326965 13 17 3237 en Materials © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
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Engineering Buried pipeline Corrosion defect Ren, Meng Zhang, Yanmei Fan, Mu Xiao, Zhongmin Numerical simulation and ANN prediction of crack problems within corrosion defects |
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Buried pipelines are widely used, so it is necessary to analyze and study their fracture characteristics. The locations of corrosion defects on the pipe are more susceptible to fracture under the influence of internal pressure generated during material transportation. In the open literature, a large number of studies have been conducted on the failure pressure or residual strength of corroded pipelines. On this basis, this study conducts a fracture analysis on buried pipelines with corrosion areas under seismic loads. The extended finite element method was used to model and analyze the buried pipeline under seismic load, and it was found that the stress value at the crack tip was maximum when the circumferential angle of the crack was near 5° in the corrosion area. The changes in the stress field at the crack tip in the corrosion zone of the pipeline under different loads were compared. Based on the BP algorithm, a neural network model that can predict the stress field at the pipe crack tip is established. The neural network is trained using numerical model data, and a prediction model with a prediction error of less than 10% is constructed. The crack tip characteristics were further studied using the BP neural network model, and it was determined that the tip stress fluctuation range is between 450 MPa and 500 MPa. The neural network model is optimized based on the GA algorithm, which solves the problem of convergence difficulties and improves the prediction accuracy. According to the prediction results, it is found that when the internal pressure increases, the corrosion depth will significantly affect the crack tip stress field. The maximum error of the optimized neural network is 5.32%. The calculation data of the optimized neural network model were compared with the calculation data of other models, and it was determined that GA-BPNN has better adaptability in this research problem. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Ren, Meng Zhang, Yanmei Fan, Mu Xiao, Zhongmin |
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Article |
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Ren, Meng Zhang, Yanmei Fan, Mu Xiao, Zhongmin |
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Ren, Meng |
title |
Numerical simulation and ANN prediction of crack problems within corrosion defects |
title_short |
Numerical simulation and ANN prediction of crack problems within corrosion defects |
title_full |
Numerical simulation and ANN prediction of crack problems within corrosion defects |
title_fullStr |
Numerical simulation and ANN prediction of crack problems within corrosion defects |
title_full_unstemmed |
Numerical simulation and ANN prediction of crack problems within corrosion defects |
title_sort |
numerical simulation and ann prediction of crack problems within corrosion defects |
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2024 |
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https://hdl.handle.net/10356/180592 |
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