This paper presents a methodology for optimizing an aeronautical propeller to minimize power consumption. A multi-objective approach using blade element momentum (BEM) theory and evolutionary algorithms is employed to optimize propeller design by minimizing power consumption during takeoff and top-of-climb. Three different evolutionary algorithms generated a Pareto front, from which the optimal propeller design is selected. The selected propeller design is evaluated under optimal operational conditions for a specific mission. In this context, two operational approaches for the optimized propellers during flight missions are evaluated. The first approach considers the possibility of only three values for the propeller rotation, while the second allows continuous changes in the rotational speed and pitch angle values, known as the multi-rotational-speed approach. In the second approach, a modal analysis of the propeller is performed using rotating beam theory. The natural frequencies of vibration, constrained by the Campbell diagram, enable an operational analysis and ensure structural integrity by preventing resonance between propeller blades and the rotational procedures. The multi-rotational approach is conducted with and without frequency constraints, resulting in general flight energy reductions of 1.40% and 1.47%, respectively. However, substantial power savings are achieved, namely up to $10\%$ during critical flight states, which can have a significant impact on future engine design and operability. The main contributions of the research lie in analyzing the multi-rotational approach with vibrational constraints of the optimized propeller. This research advances sustainable aviation practices by focusing on reducing power consumption while maintaining performance.

Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design exploration. This paper introduces a data-driven surrogate modeling framework based on a feedforward neural network to predict natural vibration frequencies of propeller blades with high accuracy and a dramatically reduced runtime. A dataset of 1364 airfoil geometries was parameterized, meshed, and analyzed in ANSYS 2024 R2 across a range of rotational speeds and boundary conditions to generate modal responses. A TensorFlow/Keras model was trained and optimized via randomized search cross-validation over network depth, neuron counts, learning rate, batch size, and optimizer selection. The resulting surrogate achieves $R^2>0.90$ and $NRMSE<0.08$ for the second and higher-order modes, while reducing prediction time by several orders of magnitude compared to full finite-element workflows. The proposed approach seamlessly integrates with CAD/CAE pipelines and supports rapid, iterative optimization and real-time decision support in propeller design.

This work presents the studies obtained using OpenFOAM OpenSource Computational Fluid Dynamics (CFD) Software. Experiments were performed to predict lift coefficient and drag coefficient curves for the NACA2412 profile. Subsequently, the results obtained were compared with the results of the bibliography and discussed.

Este estudo tem como objetivo utilizar dois softwares distintos, o JAVAPROP, baseado na teoria do elemento de pá e momento, e o CFX/ANSYS, que utiliza dinâmica de fluidos computacional para encontrar os parâmetros de desempenho aerodinâmico da hélice SR2. Os valores do coeficiente de potência são encontrados para casos onde a taxa de avanço é fixa e o número de Mach é variável e para casos com número de Mach fixo e taxa de avanço variável. Os resultados obtidos com ambos os métodos são comparados com valores obtidos através de resultados experimentais em túnel de vento. Os resultados obtidos no JAVAPROP são melhores nas faixas médias de razão de avanço e número Mach, enquanto perde qualidade nos valores de borda. Os resultados do CFX/ANSYS mostram tendências semelhantes em toda a faixa. Uma segunda malha mais refinada é proposta e seus resultados estão mais próximos dos experimentais.