Fast-response hot-wire flow sensors for wind and gust estimation on UAVs

TL;DR

This paper introduces MAST, a lightweight, robust MEMS hot-wire sensor system for real-time wind and gust estimation on UAVs, demonstrating high accuracy and bandwidth in wind tunnel tests.

Contribution

The paper presents a novel MEMS hot-wire sensor array and neural network model enabling fast, accurate wind measurement on UAVs, addressing limitations of existing sensors.

Findings

Average wind speed error: 0.14 m/s

Average wind direction error: 1.6 degrees

Bandwidth of 570 Hz for high-frequency flow phenomena

Abstract

Due to limitations in available sensor technology, unmanned aerial vehicles (UAVs) lack an active sensing capability to measure turbulence, gusts, or other unsteady aerodynamic phenomena. Conventional in situ anemometry techniques fail to deliver in the harsh and dynamic multirotor environment due to form factor, resolution, or robustness requirements. To address this capability gap, a novel, fast-response sensor system to measure a wind vector in two dimensions is introduced and evaluated. This system, known as `MAST' (for MEMS Anemometry Sensing Tower), leverages advances in microelectromechanical (MEMS) hot-wire devices to produce a solid-state, lightweight, and robust flow sensor suitable for real-time wind estimation onboard a UAV. The MAST uses five pentagonally-arranged microscale hot-wires to determine the wind vector's direction and magnitude. The MAST's performance was evaluated in a wind tunnel at speeds up to 5~m/s and orientations of 0 - 360 degrees. A neural network sensor model was trained from the wind tunnel data to estimate the wind vector from sensor signals. The average error of the sensor is 0.14 m/s for speed and 1.6 degrees for direction. Furthermore, 95% of measurements are within 0.36 m/s error for speed and 5.0 degree error for direction. With a bandwidth of 570 Hz determined from square-wave testing, the MAST stands to greatly enhance UAV wind estimation capabilities and enable capturing relevant high-frequency phenomena in flow conditions.