Modeling the aerodynamic performance of unmanned aerial vehicle (UAV) propellers with multifidelity method

In this study, an artificial neural network (ANN) based method is discussed to determine the aerodynamic performance of propellers used for Unmanned Aerial Vehicles (UAVs). Here, wind tunnel test data was used to obtain data for propellers without test data. First, wind tunnel test data was converted to a specific format using Python and modeling was done using ANN. With this modeling process, it was seen how close the model obtained with artificial neural networks produced results to the data obtained from wind tunnel tests. This study allows for more precise analysis of the aerodynamic performance of UAV propellers and optimization of their design. This approach provided a very accurate modeling of the aerodynamic performance of UAV propellers and took an important step towards determining the performance of propellers without wind tunnel test data. The obtained data constitutes a valuable resource for optimizing the design and performance of UAVs.