Aeroelastic Model for Design of Composite Propellers
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Abstract
A tightly coupled aeroelastic design code for composite propeller blades was developed, verified, and used to perform design sensitivity studies. The design code features a structural model that accounts for geometric nonlinearities through the application of a corotational framework, nonlinear responses to loads, and a cross-sectional modelling approach to accurately represent the detailed 3D blade structure as a reduced-order Timoshenko beam element model. Blade element momentum (BEM) theory was used to evaluate aerodynamic loads, which are mapped onto the structural mesh. The nonlinear aeroelastic analysis routine uses Newton’s method to converge on a solution, with analytical derivatives for all applied loads. Excellent agreement with other analysis methods was shown during verification studies for all developed models. During validation, performance trends obtained from BEM were consistent with experimental results, with a maximum error of 20% at operating conditions under consideration during this research. The use of either symmetric-unbalanced or symmetric-balanced laminates was considered during sensitivity studies. Small variations in performance compared to the rigid propeller were observed from blades constructed out of symmetric-balanced laminates, as the minimal amount of bend-twist and extension-shear coupling resulted in small twist deformations. Conversely, propellers constructed out of symmetric-unbalanced laminates were shown to yield a noticeable variation in thrust and power compared to the rigid propeller due to the presence of bend-twist and extension-shear coupling, which results in coupling between twist and blade axis deformations. The presence of an aerodynamic wash-out effect was also found to alleviate blade loads, resulting in a lower power requirement at a given thrust setting, and an opposite trend was observed in the presence of a wash-in effect. The proposed analysis framework may be applied towards more extensive design studies or optimization in future projects.