Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications

This study addresses the challenge of accurately correlating detailed and reduced thermal models in aerospace applications by using heuristic global optimization methods. In the context of increasingly complex thermal systems, traditional manual correlation methods are usually a time-consuming task. This research employs a series of numerical simulations using methods such as Genetic Algorithms, Cultural Algorithms, and Artificial Immune Systems, with an emphasis on parameter tuning to optimize the reduced thermal model correlation. Results indicate that these heuristic methods can achieve high-accuracy correlations, with transient simulations exhibiting temperature differences below 3 ${}^{\circ }$C, thereby validating the hypothesis that heuristic methods can effectively navigate complex parameter optimizations. Moreover, a comparative analysis of fitness function performance across various optimization methods underscores both the potential and computational challenges inherent in these approaches. The findings suggest that while heuristic global optimization provides a robust framework for thermal model reduction and correlation, further refinement—particularly in scaling to larger, more complex models and adaptive parameter tuning—is necessary. Overall, this work contributes to the theoretical understanding and practical application of advanced optimization strategies in aerospace thermal analysis, paving the way for improved predictive reliability and more efficient engineering processes.