A novel ant lion optimizer-salp swarm algorithm for inverse heat conduction problem in pipeline fluid temperature recognition

In this paper, the inverse heat transfer problem is studied in a two-dimensional cross-section of a horizontal pipe to estimate the unknown time-varying fluid temperature close to the inner wall of the pipe. The uneven mixing of fluids at different temperatures leads to temperature fluctuations in the inner wall of the pipeline, which could result in wall stress changes and structural thermal fatigue. An inversion algorithm based on the ant lion optimizer-salp swarm algorithm (ALO-SSA) is proposed. The temperature data are utilized as input data for the inverse heat transfer problem (IHCP). The fluid temperature fluctuations are most dramatic near the inner wall at 0° and 30°. ALO-SSA algorithm exhibits significantly lower average error compared to the other two contrasting algorithms, with an estimated average error of 0.0395 at 0° and 0.0413 at 30° for temperature estimation. Experimental analysis showed that the algorithm has good speed and accuracy for solving similar inverse heat transfer problems.