Aircraft Propeller Design through Constrained Aero-Structural Particle Swarm Optimization

Abstract

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.

High-fidelity aerodynamic and aerostructural optimization of UAV propellers

“…They considered both the aerodynamic and aeroacoustic performance. Jose et al [6] employed a particle swarm optimization algorithm to optimize an aircraft propeller for minimum energy consumption. Yang et al [7] utilized the discrete adjoint approach to optimize a propeller for maximum efficiency.…”