Multi-objective optimization of multi-stage heat sink of electric aircraft using three-dimensional thermal network analysis

The efficiency, reliability, and safety of aircraft have been improved by substituting hydraulic and mechanical systems with electric systems. Furthermore, in future electric aircraft, where fan-driving engines are partially replaced by electric motors, solving the thermal management of heat generation from the motor-controlling power-devices will become a crucial problem. In this study, three-dimensional steady thermal network analysis (TNA) was carried out to analyze the temperature field in a heat sink, for motor-controller air cooling, which is expected to be used for the future electric aircraft. The numerical procedure used was verified by comparing the results of TNA with those of three-dimensional fluid-solid conjugate heat transfer analysis using finite volume method. After the verification, TNA was carried out for an aircraft flight scenario, where the optimum geometry of the heat sink was investigated, minimizing the following three objective functions: the pressure loss of air flow through the heat sink, the maximum local wall temperature of the heat sink at the surface of motor controller, and the weight of the heat sink. In this multi-objective optimization, the design of experiments technique was used to decide the space-filling points for the six design parameters. The three objective functions at each sampling point were calculated using TNA. A response surface was created for each objective function. A multi-objective optimization on the response surfaces, using a genetic algorithm, was iteratively carried out to find the Pareto optimal solutions for the air- cooled multi-stage heat sink. From the results of the Pareto optimal solutions, the effects of the design parameters on the objective functions were discussed. In addition, multiple regression analysis was performed for quantitative evaluation of the results of the Pareto optimal solutions, and the dominant design parameters for each objective function were identified.