Simulation and algorithmic implementation of ultrasonic phased-array imaging for curved lead-seal surfaces
Abstract
In ultrasonic phased-array sector scanning of lead-seal defects in high-voltage cable terminations, incomplete contact between the probe and the curved lead-seal surface may cause acoustic-beam spreading, focal shift, weak defect echoes, image distortion, and near-surface artefacts. To address these problems, this study proposes a curvature-adaptive ultrasonic phased-array imaging method for curved lead-seal surfaces. First, an acoustic-field simulation model of the curved lead-seal structure was established to analyze beam propagation through the couplant layer and the lead seal. Then, a delay law based on Fermat’s principle and Snell’s law was derived to account for the curved interface and the two-layer propagation path. The calculated delays were applied during post-processing delay-and-sum beam synthesis, followed by Hilbert-envelope extraction and angular coordinate correction. Numerical results show that the curved interface causes beam spreading and focal-position shift compared with a flat interface, which explains the localization deviation in conventional phased-array imaging. Laboratory experiments were further conducted on lead-seal specimens with internal defects, interlayer defects, and combined internal–interlayer defects. For the internal defect with an actual depth of 15.0 mm, the positioning error was reduced from 13.33% to 6.67%. For the interlayer defect with an actual depth of 25.0 mm, the positioning error was reduced from 10.00% to 4.00%. The results demonstrate that the proposed method improves defect localization accuracy, morphology restoration, and imaging consistency compared with conventional phased-array imaging. This study provides a simulation-supported and experimentally validated method for curved lead-seal ultrasonic imaging, although further validation is still required for naturally formed irregular cracks, variable coupling conditions, and field cable-terminal applications.
Copyright (c) 2026 Jian Cheng, Liguang Hu, Fengyin Zhang, Quan Zhang, Lianbing Wang

This work is licensed under a Creative Commons Attribution 4.0 International License.
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