Multiobjective Optimization Design Method of Magnetic Circuit for Magnetic Flux Leakage Pipeline Inspection Gauges

Abstract

The magnetic circuit in magnetic flux leakage (MFL) testing serves as the fundamental mechanism for generating leakage signals from defects. Currently, the magnetic circuit design methodologies for MFL pipeline inspection gauges (PIGs) predominantly rely on empirical assumptions and finite element simulation trials, lacking efficient and accurate design guidance. To address the multiple magnetic circuit design requirements—including iterative design processes, near-saturation magnetization of the pipeline wall, and reliable in-pipeline passing capacity—a stepped multiobjective optimization method is proposed for the magnetic circuit design of MFL PIGs. A magnetic circuit model of the MFL PIG is established. The spatial leakage permeance method is employed to preliminarily calculate the magnetic circuit dimensions, and the stepped multiobjective optimization function combined with the Genetic Algorithm is used to realize iterative searching of magnetic circuit parameters and optimize the magnetic circuit dimensions. Compared with the magnet circuit designed by empirical methods, the optimized magnetic circuit achieves higher saturation magnetization of the pipe wall, improved utilization efficiency of permanent magnets, smaller yoke volume, and maximized lift-off air gap. Both Ansys Maxwell 3-D finite element method (FEM) simulations and pipeline detection experiments indicate that the amplitude of defect MFL signals for the optimized magnetic circuit increases by approximately 100%. The optimized magnetic circuit can detect micro-defects with a depth of 0.35 mm and possesses higher detection sensitivity. Compared with the traditional empirical magnetic circuit design strategy, the multiobjective optimization method achieves higher design efficiency and calculation accuracy. It is applicable to the magnetic circuit design of PIGs for pipelines with diverse wall thicknesses and diameters.