Parameter optimization of thermal network model for aerial cameras utilizing Monte-Carlo and genetic algorithm.

Area of Science:

• Aerospace Engineering
• Thermal Engineering
• Computational Modeling

Background:

• Precise temperature calculation is essential for thermal models, particularly in space instruments.
• Model refinement requires optimal thermal parameters to match experimental data.

Purpose of the Study:

• To introduce an optimization methodology for thermal network models.
• To enhance the accuracy of temperature predictions for aerial cameras.

Main Methods:

• Investigated internal convective heat transfer coefficients for cylindrical and planar structures.
• Employed Monte-Carlo simulation for transient temperature data reliability.
• Utilized a genetic algorithm to minimize root mean square error (RMSE) between calculated and measured temperatures.

Main Results:

• Achieved an optimized model with an RMSE of 1.07 ℃.
• Reduced maximum relative error from 33.8% to 3.1%.
• Demonstrated discrepancies within 2°C after external convection heat transfer coefficient correction during flight simulations.

Conclusions:

• The optimized thermal network model significantly enhances temperature prediction accuracy for aerial cameras.
• The methodology proves robust and effective for aerospace thermal management.
• Validated through flight simulations and thermal control experiments.