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New Method to Measure Wind Speed Could Unlock Drones’ Potential

A fundamentally different approach to wind estimation using unmanned aircraft than the vast majority of existing methods. This method uses no on-board flow sensor and does not attempt to estimate thrust or drag forces.

Dr. Avinash Muthu Krishnan, assistant professor of Aeronautical Science; graduate researcher Jeremy Copenhaver; and Dr. Marc Compere, associate professor of Mechanical Engineering, work on a weather-sensing drone that uses GPS, instead of onboard flow sensors, to measure wind speed. (Image: Embry-Riddle/David Massey)

Traditionally, remotely piloted aircraft systems, or drones, have used onboard flow sensors to measure wind effects, producing in-flight metrics on which operators rely. Leveraging GPS instead, however, might provide more robust measurements, leading to safer, more efficient flights, according to Embry-Riddle Aeronautical University researchers.

As most drones weigh less than 55 pounds, even mild gusts of wind can disrupt their flight, which makes finding creative solutions to monitor and predict hyperlocal weather conditions essential to flying without disruption or unplanned landings.

Dr. Marc Compere, associate professor of Mechanical Engineering at Embry-Riddle’s Daytona Beach Campus, recently described a new approach to measuring wind effects — in the international, peer-reviewed Drones journal — along with Drs. Kevin Adkins and Avinash Muthu Krishnan, both professors of Aeronautical Science.

“The GPS method does not attempt to estimate thrust or drag forces,” said Compere, principal investigator on the project. “Using only GPS and orientation sensors, our new strategy estimates wind vectors in an Earth-fixed frame during turning maneuvers.”

“It’s all about safety,” Adkins added. “The lower atmosphere is a new and incredibly dynamic portion of the environment for sustained aviation operations; traditionally, this is an area we have passed through quickly during ascent and descent. Wind poses unique challenges to controllability and the aircraft’s range and endurance.”

Ultimately, the team’s goal is to make the flight of uncrewed aircraft more predictable in locations that have never seen UAS flight, which will boost flight reliability and help advance the technology throughout the industry, from drone-based package delivery all the way to urban air mobility, or “air taxis.”

But the implications of wind on uncrewed aircraft systems (UAS) extend far beyond piloting requirements. Scientists need accurate wind measurements to research atmospheric conditions and air quality; wind can offer emergency workers clues as to a wildfire’s growth patterns; and wind affects the weather.

“Sensing wind is a critical part of society that not many people are aware of,” Compere added.

The Wind-Arc method, as it’s called to leverage GPS data in UAS flights, is not new to the industry, but the Embry-Riddle research team has made the tactic more viable through the incorporation of computer simulations. Software is used to forecast flight conditions — including heading angle, airspeed and wind speed trends — and then produce real-time statistics of how an aircraft would respond to nuanced changes in the environment.

“The very nature of wind makes it a difficult quantity to measure,” Compere said. “But our simulation provides an ability that no experimental approach can provide: We can simulate wind with exact known inputs and test the method with exact known outputs.”

Currently, the team uses expensive, custom-built aircraft outfitted with onboard flow sensors to collect weather data, but the process is complex and time-consuming. Those problems go away with the use of GPS, though, and GPS technology comes standard in store-bought drones.

“Scaling and simplicity is what measuring wind with just a GPS and a compass provides,” Compere said. “All drones already have these sensors. They’re built-in from the factory.”

Adkins added that the GPS approach also opens the possibility of crowdsourcing weather data from multiple users at once.

“The sheer number of potential observations can produce a far superior product, especially in urban environments where things change more quickly,” he said. “The use of GPS makes both business and safety sense.”

In previous work, the team simulated UAS routes along the Daytona Beach Campus to help push drone-delivery tech forward. They also used UAS to monitor and improve air quality, among many other projects.

Next, they’re hoping to commercialize the Wind-Arc Method. A patent is pending.

This work was performed by Kevin Adkins, Marc Compere and Avinash Muthu Krishnan for Embry-Riddle Aeronautical University. For more information, download the Technical Support Package (free white paper) below. TSP-02241

Topics:
Aerospace Aircraft Aircraft Aviation Electronics Measurements Measuring Instruments Test & Measurement Unmanned Air Vehicles/Systems (UAV/UAS) Unmanned aerial vehicles Vehicles
This Brief includes a Technical Support Package (TSP).
Document cover
Go with the Flow: Estimating Wind Using Uncrewed Aircraft

(reference TSP-02241) is currently available for download from the TSP library.

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    Aerospace & Defense Technology Magazine

    This article first appeared in the February, 2024 issue of Aerospace & Defense Technology Magazine (Vol. 9 No. 1).

    Read more articles from this issue here.

    Read more articles from the archives here.

    Overview

    The document titled "Go with the Flow: Estimating Wind Using Uncrewed Aircraft" by Marc D. Compere and colleagues presents a novel approach to estimating wind using uncrewed aerial vehicles (UAVs). Traditional methods of measuring wind often rely on fixed sensors, which can be limited in their effectiveness, especially in dynamic environments. This research introduces the Wind-Arc method, which leverages the capabilities of UAVs equipped with GPS and orientation sensors to estimate wind vectors during flight maneuvers.

    The authors highlight the challenges associated with wind measurement, noting that wind is a complex quantity that is difficult to quantify accurately. They emphasize that errors in wind estimation can arise from various factors, including the UAV's movement and environmental conditions. The Wind-Arc method aims to address these challenges by utilizing the UAV's flight dynamics to infer wind characteristics, particularly during turning maneuvers.

    The document details the methodology behind the Wind-Arc approach, including the mathematical modeling and algorithms used to process the data collected during flights. The authors conducted simulations to validate the effectiveness of their method, demonstrating its potential to provide accurate wind estimates even in conditions where traditional methods may fail.

    In addition to the theoretical framework, the study includes practical flight tests that showcase the application of the Wind-Arc method in real-world scenarios. The results from these tests indicate that the method can yield reliable wind estimates, which could be beneficial for various applications, including agriculture, disaster management, and environmental monitoring.

    The authors also discuss the implications of their findings, suggesting that the Wind-Arc method could enhance the capabilities of UAVs in collecting meteorological data. This advancement could lead to improved weather forecasting, better understanding of microclimates, and enhanced operational efficiency in various sectors that rely on accurate wind data.

    Overall, the document presents a significant contribution to the field of drone technology and meteorology, offering a new tool for wind estimation that could have wide-ranging applications. The research underscores the potential of UAVs to gather critical environmental data, paving the way for future innovations in remote sensing and atmospheric studies.

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