# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 2 Literature Review  
Heuristic global optimization methods have found increasing application in the reduction and correlation of thermal models within the aerospace industry. Many engineershave employed these methods, often in-house, to address complex thermal modelling problems in spacecraft design. A notable example of this utilization is the work carried out by Airbus Defence and Space (formerly EADS Astrium), which was presented at an ESA conference [2]. In this research, [PERSON] of EADS Astrium, along with [PERSON], [PERSON], and [PERSON] from the Laboratoire d'Optimisation Globale, applied a Genetic Algorithm (GA) [3] to address challenges in thermal model correlation [2; 4; 5; 6]. This work focused on optimizing the correlation process, which aims to minimize the differences between theoretical model predictions and test measurements. The results demonstrated that GAs could effectively correlate multiple test cases (e.g., hot, cold, operational, and non-operational scenarios) by adjusting the fitness function. The correlation process typically required 2000 to 4000 iterations, achieving temperature differences of less than 0.1 \({}^{\circ}\)C between models, which is considered highly accurate. However, the implementation of GAs was acknowledged as a complex programming challenge. A robust bibliographic review accompanied this study, comparing various heuristic methods, including their advantages, disadvantages, and model characteristics [7; 8]. The authors also suggested that other heuristic algorithms, beyond the GA used in the study, should be tested in the thermal model correlation process [2]. The evolution of heuristic optimization methods in thermal model correlation over the years shows that several researchers have employed heuristic methods for thermal model reduction and correlation. The earliest study in this review, conducted by [PERSON] et al. (1994) [9], explored the use of deterministic design sensitivity and optimization techniques. These methods, primarily gradient based, were efficient for simpler models but struggled with complex, nonlinear systems common in spacecraft thermal analysis, often getting trapped in local optima. By 2005, the limitations of deterministic approaches led to the exploration of stochastic techniques. [PERSON] et al. (2005) [10] introduced stochastic methods, which do not rely on smoothness or convexity in the model, making them more adept at navigating complex, nonlinear search spaces. However, the increased computational cost of these methods remained a significant challenge, particularly for large datasets. In 2007, [PERSON] and [PERSON] [2] further advanced the field by applying Genetic Algorithms (GAs) to spacecraft thermal model correlation. GAs, as stochastic optimization methods, proved to be highly effective in exploring nonlinear, non-convex problems typical of thermal models. However, they required significant computational resources and careful parameter tuning, which introduced additional complexity, particularly in time-constrained projects. The flexibility of GAs in thermal modelling was further reinforced by [PERSON] et al. (2009) [11], who applied them to optimize heat transfer on concave surfaces. Despite their effectiveness, the trade-off between computational efficiency and algorithmic complexity remained a key concern, with parameter tuning continuing to be a critical factor in ensuring optimal performance. In 2011, [PERSON] et al. [12] introduced a Monte Carlo hybrid algorithm that combined the robustness of Monte Carlo simulations with deterministic methods. This hybrid approach effectively handled uncertainty in thermal models but came at the cost of increased computational demand, limiting its practicality for large-scale or real-time applications. [PERSON] et al. (2011) [13] also examined deterministic methods for spacecraft thermal models, concluding that while faster, these methods were insufficient for handling the nonlinearities prevalent in modern spacecraft systems. By 2012, [PERSON] et al.