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

It is crucial to precisely calculate temperature utilizing thermal models, which require the determination of thermal parameters that optimally align model outcomes with experimental data. In many instances, the refinement of these models is undertaken within space instruments. This paper introduces an optimization methodology for thermal network models, with the objective of enhancing the accuracy of temperature predictions for aerial cameras. The investigation of internal convective heat transfer coefficients for both cylindrical and planar structures provides an estimation of convective thermal parameters. Based on the identification of thermally sensitive parameters and the reliability evaluation of transient temperature data through the Monte-Carlo simulation, the genetic algorithm is employed to search for global optimal parameter values that minimize the root mean square error (RMSE) between calculated and measured node temperatures. As a result, the optimized model shows significantly improved accuracy in temperature prediction, attaining an RMSE of 1.07 ℃ and reducing the maximum relative error between predicted and experimental results from 33.8 to 3.1%. Furthermore, the flight simulation and thermal control experiments validate the robustness of the optimized model, demonstrating that discrepancies between the observed and predicted temperatures are within 2 °C after re-correcting the external convection heat transfer coefficient value.