Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm
Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a h...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/17/7379 |
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| author | Shuomang Shi Baiyu Jiang Simone Ludwig Luyang Xu Hao Wang Ying Huang Fei Yan |
| author_facet | Shuomang Shi Baiyu Jiang Simone Ludwig Luyang Xu Hao Wang Ying Huang Fei Yan |
| author_sort | Shuomang Shi |
| collection | DOAJ |
| description | Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement. |
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| format | Article |
| id | doaj.art-dc7ff8e90cd345f59f95c74fb83b8dd5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:13:52Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-dc7ff8e90cd345f59f95c74fb83b8dd52023-11-19T08:49:06ZengMDPI AGSensors1424-82202023-08-012317737910.3390/s23177379Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic AlgorithmShuomang Shi0Baiyu Jiang1Simone Ludwig2Luyang Xu3Hao Wang4Ying Huang5Fei Yan6Department of Civil, Construction, and Environmental Engineering, North Dakota State University, Fargo, ND 58105, USADepartment of Civil and Environmental Engineering, School of Engineering, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USADepartment of Computer Science, North Dakota State University, Fargo, ND 58105, USADepartment of Civil, Construction, and Environmental Engineering, North Dakota State University, Fargo, ND 58105, USADepartment of Civil and Environmental Engineering, School of Engineering, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USADepartment of Civil, Construction, and Environmental Engineering, North Dakota State University, Fargo, ND 58105, USADepartment of Civil, Construction, and Environmental Engineering, North Dakota State University, Fargo, ND 58105, USAInternal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement.https://www.mdpi.com/1424-8220/23/17/7379pipelinesStructural Health Monitoring (SHM)Computational Fluid Dynamics (CFD)Genetic Algorithm (GA)corrosion |
| spellingShingle | Shuomang Shi Baiyu Jiang Simone Ludwig Luyang Xu Hao Wang Ying Huang Fei Yan Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm Sensors pipelines Structural Health Monitoring (SHM) Computational Fluid Dynamics (CFD) Genetic Algorithm (GA) corrosion |
| title | Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm |
| title_full | Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm |
| title_fullStr | Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm |
| title_full_unstemmed | Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm |
| title_short | Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm |
| title_sort | optimization for pipeline corrosion sensor placement in oil water two phase flow using cfd simulations and genetic algorithm |
| topic | pipelines Structural Health Monitoring (SHM) Computational Fluid Dynamics (CFD) Genetic Algorithm (GA) corrosion |
| url | https://www.mdpi.com/1424-8220/23/17/7379 |
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