Simulation of Small Fixed-Wing and Multirotor UASs Using Realistic Small-Scale Wind Fields

Intuitively, it is clear that small unmanned aerial systems (UAS) – flying at relatively low airspeeds - will be more sensitive to small-scale wind structures. Figure 1 provides a concrete example, showing the vertical acceleration response of a small fixed-wing UAV (black) and a mid-size commercial transport aircraft (red) to the vertical wind component. It can be seen that the wavelengths of the winds that are most important to the UAV are in the meters to tens of meters range. This implies that in order to analyze the impact of small-scale wind structures on UAS in a quantitative fashion, it is imperative to model the winds accurately at these small scales.

NCAR/RAL has developed the capability to model realistic wind fields via LES methods and synthetic turbulence fields via analytic/numerical methods. In order to enforce computational stability, numerical weather models typically filter out the smallest scales of the flow. This filtering can be on the order of 5-10 grid spacing, i.e., if the grid spacing is, the scale at which the winds are truly accurate is 5-10. An empirical method was developed to merge the vertical component of 25 m LES wind fields with synthetic meter-scale isotropic turbulence fields. In this case, with the LES simulation being over a relatively flat terrain and the altitude of the UAV simulation being well-above the surface (300m), there are only two parameters needed to merge the two fields, the turbulence intensity level and a correlation length scale. This is because the wind field at smaller scales will be mostly isotropic. These parameters were calculated from the LES data, and then used to set the parameters of the synthetic turbulence field. A low-pass filter is then applied to the LES data, and then it is interpolated to the desired grid. A complimentary high-pass filter applied to the turbulence field generated on the desired grid. Then the two fields are added together pointwise. Figure 2 shows the result of the merging process, with the red curve illustrating a segment of the LES vertical wind (converted to a time series with respect to a UAV flight path), and the black curve showing the merged LES/subgrid turbulence. It can be seen that the LES-alone data provides the large-scale variation in the winds, but does not capture the small-scale turbulence. On the other hand, the merged wind field accounts for both characteristics.

The LES-alone and merged subgrid/LES winds were then used as input to a three degree of freedom (airspeed, height and pitch), small UAS flight simulation. Figure 3 shows the result of simulating the flight of a small fixed-wing UAS through the wind field shown in Figure 2. The left-hand panel shows the height response, with the red curve being the results from the LES-alone winds, and the black curve those from the merged subgrid-LES field. The right-hand panel is the same as the left-hand one, except for the vertical acceleration response. From these figures, it is clear that the LES-alone wind component is insufficient for small UAS flight simulation – especially if quantities such as accelerations are important.

Ongoing work includes the implementation of a multirotor simulation capability, and the extension of the LES-subgrid merging capability to three dimensions and three wind components.

This work was sponsored by NASA’s UAS Traffic Management (UTM) project and the National Science Foundation.