An Improved Nonlinear Aerodynamic Derivative Model of Aircraft at High Angles of Attack

The classical aerodynamic derivative model is widely used in flight dynamics, but its application is extremely limited in cases with complicated nonlinear flows, especially at high angles of attack. A modified nonlinear aerodynamic derivative model for predicting unsteady aerodynamic forces and moments at a high angle of attack is developed in this study. We first extend the higher-order terms to describe the nonlinear characteristics and then introduce three more influence parameters, the initial angle of attack, the reduced frequency, and the oscillation amplitude, to correct the constant aerodynamic derivative terms that have higher-order polynomials for these values. The improved nonlinear aerodynamic derivative model was validated by using the NACA 0015 airfoil and the F-18 model. The results show that the improved model has a higher prediction ability at high angles of attack and has the ability to predict the aerodynamic characteristics of other unknown states based on known unsteady aerodynamic data, such as the initial angle of attack, reduced frequency, and oscillation amplitude.

A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.