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Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Institute for Dynamic Systems and Controls, ETH Zurich, 8092 Zurich, Switzerland
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Drones 2019, 3(4), 77; https://doi.org/10.3390/drones3040077
Received: 18 September 2019 / Revised: 4 October 2019 / Accepted: 5 October 2019 / Published: 8 October 2019
Two low-order, parametric models are developed for the forces and moments that a rotating propeller undergoes in forward flight. The models are derived using a first-principles-based approach, and are computationally efficient in the sense of being represented by explicit expressions. The parameters for the models can be identified either using supervised learning/grey-box fitting from labelled data, or can be predicted using only the static load coefficients (i.e., the hover thrust and torque coefficients). The second model is a multinomial model that is derived by means of a Taylor series expansion of the first model, and can be viewed as a lower-order lumped parameter model. The models and parameter generation methods are experimentally tested against 19 propellers tested in a wind tunnel under oblique flow conditions, for which the data is made available. The models are tested against 181 additional propellers from existing datasets. View Full-Text
Keywords: propeller; parametric model; grey-box identification; oblique flow dataset propeller; parametric model; grey-box identification; oblique flow dataset
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Gill, R.; D’Andrea, R. Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications. Drones 2019, 3, 77.

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