A Hybrid PSO-WO Algorithm for Identification of Irregular Inner Wall Defects of a Body in a Thermal Environment

Accurate knowledge of the inner wall defect shape of industrial thermal equipment (ITE) plays a crucial role in safety inspections. However, direct observation and measurement are challenging due to the high-temperature environment within ITE. To address this issue, the identification of irregular inner wall defect shape based inverse technology is studied in this work. A novel particle swarm optimization (PSO) coupled with the whale optimization (WO) algorithm (HPWA) is developed as solver for inverse problems to identify the inner wall defect irregular shape. This hybrid approach enhanced the late-stage convergence efficiency of WO while avoiding the local optima issue commonly faced by PSO. The radial integral boundary element method (RIBEM) is used for solving the transient heat transfer problem and obtain transient temperature data at measurement points for inverse problem simulations. It was chosen for its capability to effectively handle complex boundary shapes by discretizing only the domain boundaries. Additionally, the effect of the distance between outer and inner boundaries and measurement duration on the inverse results are thoroughly analyzed. Results show that the PSO-WO algorithm is robust to measurement errors and becomes more accurate with measurement points closer to the actual inner boundary position. Extending the measurement time has little effect on inversion results when the measurement period is long enough.