Analyzing the Impact of Population Size in AI-Based Reconstruction of the Thermal Parameter in Heat Conduction Modeling

The research shows a novel approach leveraging swarm algorithms, the artificial bee colony (ABC) and ant colony optimization (ACO), to rebuild the heat transfer coefficient, especially for the continuous border condition. The authors utilized their application software to do numerical computations, employing classical variants of swarm algorithms. The numerical calculations employed a functional determining error to assess the accuracy of the esti - mated result. The coefficient of the thermally conductive layer was recalibrated utilizing swarm methods within the range of 900–1500 W/m 2 K and subsequently compared to a predetermined reference value. A finite element mesh consisting of 576 nodes was used for the calculations. The study involved simulations with populations of 5, 10, 15, and 20 individuals. Furthermore, each scenario also considered noise of 0%, 2%, and 5% of the reference values. The results make it evident that the reconstructed values of the kappa coefficient, cooling curves, and temperatures for the ABC and ACO algorithms are physically correct. The consequences indicate a notable level of satisfaction and strong concurrence with the anticipated κ parameter values. The results from the numerical simulations demon - strate considerable promise for applying artificial intelligence algorithms to optimize production processes, analyze data, and facilitate data-driven decision-making. This contribution not only underscores the effectiveness of swarm intelligence in engineering applications but also opens new avenues for research in thermal process optimization.