A surrogate assisted thermal optimization framework for design of pin-fin heat sink for the platform inertial navigation system

In this study, a thermal optimization framework for the platform inertial navigation system (PINS) is built. To reduce the local high temperature of the PINS, a pin-fin heat sink with staggered arrangement is designed. To reduce the dimension of the inputs and improve the optimization efficiency, a feasible global sensitivity analysis (GSA) based on kriging-high-dimensional model representation with DIviding RECTangles (DIRECT-KG-HDMR) sampling strategy is proposed. According to the GSA results, the filtered structural parameters are used to optimize the thermal performance of the heat sink, and several optimization algorithms (genetic algorithm, differential evolution, teaching-learning-based optimization, particle swarm optimization and efficient global optimization) are used for optimization. The steady thermal response of the PINS with the optimized heat sink is also studied, and the result shows that the maximum temperature of the high-temperature region of the platform is reduced by 1.08 degrees C compared with the PINS without the heat sink.

Automated summary

This study establishes a thermal optimization framework for the platform inertial navigation system (PINS), utilizing a pin-fin heat sink to reduce local high temperatures. A global sensitivity analysis method is proposed to optimize the thermal performance, resulting in a decreased temperature for the high-temperature region of the platform.