Wind Tunnel Results for a Distributed Flush Airdata System

Abstract

AbstractThe multihole probe (MHP) is an effective instrument for relative wind measurements from small unmanned aircraft systems (sUAS). Two common drawbacks for the integration of commercial MHP systems into low-cost sUAS are that 1) the MHP airdata system cost can be several times that of the sUAS airframe; and 2) when extended from the airframe, the pressure-measuring probe is often exposed to damage during normal operations. A flush airdata system (FADS) with static pressure sensing ports mounted flush with the airframe skin provides an alternative to the MHP system. This project implements a FADS with multiple static pressure sensors located at selected locations on the airframe. Computational fluid dynamics simulations are used to determine the airframe locations with the highest pressure change sensitivity to changes in the airframe angle of attack and sideslip angle. Wind tunnel test results are reported with nonlinear least squares and neural networks regression methods applied to the pressure measurements to estimate the instantaneous angle of attack and sideslip. Both methods achieved mean errors of less than . A direct comparison of the regression methods show that the neural network method provides a more accurate relative wind angle estimate than the nonlinear least squares method.