Development of Digital Flight Motion Methodology Based on Aerodynamic Derivatives Approximation

Abstract

The accuracy of efficient flight simulation depends on the quality of the aerodynamic data used to simulate aircraft dynamic motion. The accuracy of such data prediction depends strongly on motion variables, aerodynamic derivatives, and the coefficients used when the complete global aerodynamic database is being building. A surrogate model applied as a prediction method based on several measured points (exact function) used to predict unknown points of interest helps reduce time taken by the experiment or computation. Latin hypercube sampling searches the solution space for aerodynamic data to optimize the experimental design, so the key objective is to develop an aircraft’s efficient digital flight motion by solving equations of motion and predicting aerodynamic data using a surrogate model. To realize these goals, we use sample surrogate model data, acquired from empirical model USAF Stability and Control DATCOM. The database was built for two main variables, the angle of attack and the Mach number, along the longitudinal and lateral axes. Exact and predicted functions were compared by calculating the mean squared error (MSE). The digital flight was validated through mode motion analysis and a flight quality scale to prove flight mission capabilities. A comparison between results predicted by the surrogate model and the exact function showed that flight simulation analysis and prediction ability of the surrogate model are useful in future analyses.