Development of a design tool for the horizontal stabilizer of a helicopter using artificial neural networks

The design of a helicopter is an intricate and challenging process. Decisions made during the preliminary design phase can significantly impact subsequent design stages, making it crucial to base these decisions on a solid foundation. A range of methods, including hand calculations, finite element analyses, and experimental tests, can be employed to establish the conceptual design parameters. However, these methods often come with the drawbacks of being time-intensive and costly, especially when testing various structures during the early design phase. To address this issue, this study introduces an artificial neural network-based design tool to evaluate the static structural characteristics of a helicopter’s horizontal stabilizer. The tool was built in Python using the Keras library. The required database for the training of the artificial neural network model was established using finite element analyses of the horizontal stabilizer subjected to the aerodynamic load for diverse design variables. The model’s performance was evaluated, and the model’s outputs were compared to the results derived from the finite element analyses. Moreover, the Hammersley sampling methodology was employed to reduce the size of the database without compromising on accuracy. The study also assessed the impact of decreasing the amount of data fed into the network model.