Project Team
Students
Meerav Shah
Computer Science
University Park
Faculty Mentors
Dr. Jose Palacios
University Park
Aerospace Engineering
Project
https://sites.psu.edu/mcreu/files/formidable/2/2024-07-22/Using-Torque-and-RPM-loss-to-estimate-the-presence-of-icing-clouds-with-UAVs.pdf
Project Video
Project Abstract
Drones struggle to fly in icing and cloudy conditions, especially when the two are combined. This research project focuses on the automation of drones to monitor rotational speed (RPM) and torque loss when traversing cloud and icing conditions. As drone technology advances, their deployment in various sectors, including delivery services and environmental monitoring, is rapidly increasing.
However, adverse weather conditions, particularly icing, pose significant risks to drone performance and safety. By equipping autonomous drones with advanced sensors and data analysis algorithms, the project aims to precisely measure the impact of cloud and icing environments on drone performance using the Han-Palacios correlation between icing conditions and torque loss to estimate the volume of water within the clouds and assess general icing severity. As soon as the ice starts to form, the thrust decreases and an autonomous UAV finds it difficult to maintain speed or hold altitude.
This loss of torque and degradation in RPM has a direct correlation with the ice formation on the propellers. Monitoring these metrics can give us a better understanding of the cloud’s characteristics like the liquid water content of the cloud and the mean volume diameter of the droplets. In the future, we also want to develop solutions to mitigate flight issues caused by icing.
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