Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 17; p. 7379
• Main Authors: Shi, Shuomang, Jiang, Baiyu, Ludwig, Simone, Xu, Luyang, Wang, Hao, Huang, Ying, Yan, Fei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023; MDPI
• Subjects: Algorithms; Case studies; Computational Fluid Dynamics (CFD); Corrosion; Corrosion and anti-corrosives; Crude oil; Efficiency; Fines & penalties; Flow velocity; Fluid dynamics; Genetic Algorithm (GA); Genetic algorithms; Indoor air quality; Mutation; Optimization; Partial differential equations; Pipe lines; Pipelines; Sensors; Shear stress; Simulation methods; Structural Health Monitoring (SHM)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23177379

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.