Realtime data-driven sensing of oscillatory crossflow using a fixed-wing drone

Flying into a highly turbulent atmosphere for fixed-wing drone remains challenging, as they are susceptible to atmospheric disturbances, such as twisters, due to small size, and that their onboard control surfaces and flight sensors are limited. To enable accurate and efficient reduced-order flow modeling, where efficiency is measured both in terms of the computational cost, and the amount of training data required, we built a fixed-wing drone equipped with two customized multi-hole probes that measure flight speed, angle of attack and sideslip, along with embedded wing pressure sensors. The model was mounted on a six-axis force transducer in a wind tunnel. We focused on introducing low frequency oscillatory yaw disturbance as a representative disturbance generated by a nearby twister system and trained the airplane to learn the signatures of the flow structure with onboard sensing and perform inference in real time. We hypothesize that the airplane would be able to infer the distance and orientation of the twister, thus, optimizing trajectory based on current prediction. i.e. whether to fly straight into it or plan a different route to avoid it.