Abstract
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.
Highlights
Aerodynamic models of a rotating propeller in forward flight can be broadly classified into two categories
There is a lack of literature on the latter category, as noted in [4,5,6,7], especially for vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) due to the wide operating regime spanning hover, axial, and oblique flows
The first contains load measurements at various operating conditions V, Ω and β for 19 propellers collected at the “Large Subsonic Wind Tunnel” at the Institute of Fluid Dynamics at ETH Zurich
Summary
Aerodynamic models of a rotating propeller in forward flight can be broadly classified into two categories. Section 4: an algorithm for which the nine parameters can be predicted using only the static thrust and torque coefficients (i.e., the hover model) This approach is assessed with the full wind tunnel dataset. The contribution of this section over a brute-force, black-box multinomial surface fitting approach is that, based on the first-principles model, some terms disappear in the Taylor expansion, resulting in a reduced multinomial that is physically motivated. This has not been done in the literature to the best of the authors’ knowledge, and the approach is assessed with the wind tunnel data. The assessment against the wind tunnel data for each section assesses the quality of the models, and provides a typical range of values for the parameters
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