Prediction Of Brushless DC Motor And Propeller Efficiency Using An Artificial Neural Network Model - Fatih Bayram; İdris Kosova; Ahmet Yönetken (Düzce Üniversitesi Bilim ve Teknoloji Dergisi)
This study focuses on an artificial neural network model that allows users of brushless motor and propeller test rigs to compare the accuracy of data received in the interface software during testing. Brushless motors are widely used in modern aviation and industrial applications. Therefore, it is essential to analyze the factors affecting motor efficiency and accurately predict this data. This study involves the creation of an artificial neural network model from data to predict the percentage of motor efficiency for the brushless motors and propellers used in the test, whose length is measured in inches.Within the scope of this research, a useful tool is provided for users to flexibly test motor and propeller configurations and accurately analyze test results. The developed artificial neural network model has the ability to make reliable and accurate predictions for various motor-propeller configurations. Furthermore, the model is easy to use and offers expandable features. This study aims to create a valuable reference source for users of brushless motor and propeller test rigs to effectively analyze test data.