Final work : Aerodynamic design and optimization of a propeller for a regional electric aircraft

Abstract

The prime focus of this thesis work is to engineer an optimization framework capable of aerodynamic design optimization of propellers. The starting point of the study is a baseline design which is created by using a code based on Blade-Element Momentum and Vortex methods. Optimization is performed using a nature inspired Evolutionary algorithm (EA). The current work is a multi-objective problem which involves maximizing thrust and propeller efficiency. To regard individual designs in terms of objective functions, the criterion employed is Pareto Optimality. A reduction of design variable space is done and the blade angle at three different spanwise locations are studied for optimization. The distributions of other design parameters are set identical to the one employed by the NASA SR7L propeller. The predicted designs are evaluated using steady state RANS computations. It took 10 design optimization iterations for the Pareto front to converge. It is shown that the predictive capability of the meta-model consistently improved over the last few design loops as more design are evaluated using 3D CFD and used as input to the meta-model. Using the final Pareto front, the design closest to the target thrust, denoted Design A, is selected for further analysis which is compared to three other designs namely, the baseline design, the maximum efficiency and the maximum thrust design. It is observed that the maximum thrust design has higher losses in terms of stronger tip vortices and more pronounced corner separations near the hub. The Pareto front illustrates that between the objective functions, there is a trade to be made. Higher efficiency comes at the cost of lower thrust, and vice versa. As such, although the maximum efficiency design has lower losses (and hence higher efficiency), it results in a lower thrust generation. The chosen design is well poised between both extremes generating the required thrust and at the same time being efficient. The efficiency gain compared to the baseline design is 0.1%. This can be improved by obviating various simplifications considered in this work and also by introducing design considerations like sweep.