An electric propulsion model for propeller-driven aircraft is developed with the aim of minimising the power consumption for a given airspeed and thrust. Blade Element Momentum Theory (BEMT) is employed for propeller performance predictions fed with aerodynamic aerofoil data obtained from a proposed combined Computational Fluid Dynamics (CFD)–Montgomerie method, which is also validated. The Two-Dimensional (2D) aerofoil data are corrected to consider compressibility, three-dimensional, viscous and Reynolds-number effects. The BEMT model showed adequate fitting with experimental data from the University of Illinois Urbana Champaign (UIUC) database. Additionally, Goldstein optimisation via vortex theory is employed to design pitch and chord distributions minimising the induced losses of the propeller. Particle swarm optimisation is employed to find the optimal value for a wide range of geometrical and operational parameters considering some constraints. The optimisation algorithm is validated through a study case where an existing optimisation problem is approached, leading to very similar results. Some trends and insights are obtained from the study case and discussed regarding the design of an optimal propulsion system. Finally, CFD simulations of the study case are carried out, showing a slight relative error of BEMT.

The aerodynamic efficiency in airfoil theory is defined as the ratio between the lift and drag force, which is the main objective function to be maximized in a wide kind of vehicle design due to its strong relationship between fuel consumption and range. This work employs the 4-digits NACA parameterization, a recently developed 6-parameters method, and the PARSEC technique with a correction of the matrices available in the literature, to compare the computational cost and the ability to achieved higher efficiency of these parameterizations. A genetic algorithm and particle swarm optimization routines are developed and implemented in Matlab, also a sine-cosine algorithm is tested, where Xfoil and the open-source computational fluid dynamic software OpenFOAM are coupled with the optimization algorithms. Finally, a Reynolds number impact study is performed related to the airfoil shape and the angle of attack which maximizes the aerodynamic efficiency. The results showed a faster convergence for the particle swarm optimization and the highest aerodynamic efficiency achieved by the 6-parameter method. Furthermore, with a higher Reynolds number, a higher angle of attack for the optimum lift-to-drag ratio as well a less camber is obtained.

An aero-structural algorithm to reduce the energy consumption of a propeller-driven aircraft is developed through a propeller design method coupled with a Particle Swarm Optimization (PSO). A wide range of propeller parameters is considered in the optimization, including the geometry of the airfoil at each propeller section. The propeller performance prediction tool employs a convergence improved Blade Element Momentum Theory fed by airfoil aerodynamic characteristics obtained from XFOIL and a validated OpenFOAM. A stall angle correction is estimated from experimental NACA 4-digits data and employed where convergence issues emerge. The aerodynamic data are corrected to account for compressibility, three-dimensional, viscous, and Reynolds number effects. The coefficients for the rotational corrections are proposed from experimental data fitting. A structural model based on Euler-Bernoulli beam theory is employed and validated against Finite Element Analysis, while the impact of centrifugal forces is discussed. A case of study is carried out where the chord and pitch distributions are compared to minimal losses distribution from vortex theory. Wind tunnel tests were performed with printed propellers to conclude the feasibility of the entire routine and the differences between XFOIL and CFD optimal propellers. Finally, the optimal CFD propeller is compared against a commercial propeller with the same diameter, pitch, and operational conditions, showing higher thrust and efficiency.

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