Aerodynamic Analysis of an Unmanned Aerial Vehicle
The use of Unmanned Aerial Vehicles (UAV) has increased in the past decade. The aerodynamic characteristics of a UAV, as given for example by the shape of its fuselage and the shape of its airfoil, are crucial for its performance — how far it can fly, how long it can stay in the air, and how much it can carry. Achieving an optimal set of aerodynamic characteristics is challenging.
In this Brief we demonstrate how ADINA CFD can help engineers analyze the airflow around a Predator-like drone to give insights into its aerodynamic characteristics.
Figure 1 shows the 3D CAD geometry of the drone. The drone is 8.2 meters long, with a wingspan of 14.8 meters.
Figure 1 CAD geometry of the drone
The drone’s CAD geometry was imported into the AUI, in which the CFD model was created. To eliminate far-field boundary effects, the fluid (air) outer boundaries need to be sufficiently far from the drone, as seen in Figure 2.
Figure 2 The large fluid zone required to eliminate far-field boundary effects
The analysis focused on analyzing the airflow during steady-state cruising. The drone’s cruising speed was assumed to be 150 km/h, which is a relatively small speed compared to the speed of sound in air, so the air was assumed to be incompressible in the model.
The airflow is turbulent as characterized by the large Reynolds number. The turbulence model chosen was the standard k-ω turbulence model. Two million FCBI-C fluid elements were used in the mesh. As shown in Figure 3, the mesh needs to be fine enough around the drone to achieve sufficient accuracy, but can be much coarser away from the structure to reduce the computational cost.
Figure 3 Detail of the mesh used in the analysis
The analysis was run on an 8-core machine using DMP.
Some results of interest are shown in Figures 4 to 7. The movie above shows the use of the AUI fast graphics mode (FGM) to see the pressure band plot on the surface of the drone. These results help engineers assess the drone’s aerodynamic characteristics.
Figure 4 Pressure band plot at the drone’s vertical mid-plane
Figure 5 Pressure band plot at a vertical cutting plane of the airfoil. The pressure at the bottom is larger than at the top. The pressure difference provides the lift for the drone.
Figure 6 Band plot of turbulence kinetic energy at the drone’s vertical mid-plane
Figure 7 Band plot of turbulence kinetic energy at a vertical cutting plane of the airfoil
ADINA CFD, combining a powerful computational performance with comprehensive pre- and post-processing on a single platform, is an excellent tool for solving problems in computational fluid dynamics.
Aerodynamics, drone, UAV, CFD, DMP, distributed memory processing, parallel performance, pre-processing, post-processing