Performance improvement of small-scale rotors by passive blade twist control. (May 2015)

• Record Type: Journal Article
• Authors: Lv, Peng; Prothin, Sebastien; Mohd-Zawawi, Fazila; Benard, Emmanuel; Morlier, Joseph; Moschetta, Jean-Marc
• Is Part Of: Journal of fluids and structures. Volume 55(2015:May)
• Page Start: 25
• Page End: 41
• Publication Date: 2015-05
• Subjects: Small-scale -- Flexible blade -- Centrifugal force -- Tip mass -- Laser Displacement Sensor; Fluid-structure interaction -- Periodicals; Fluid mechanics -- Periodicals; Structural dynamics -- Periodicals; Structural analysis (Engineering) -- Periodicals; 620.106
• DOI: 10.1016/j.jfluidstructs.2015.01.008
• Languages: English
• ISSNs: 0889-9746

Abstract

Abstract: A passive twist control is proposed as an adaptive way to maximize the overall efficiency of the small-scale rotor blade for multifunctional aircrafts. Incorporated into a database of airfoil characteristics, Blade Element Momentum Theory is implemented to obtain the blade optimum twist rates for hover and forward flight. In order to realize the required torsion of blade between hover and forward flight, glass/epoxy laminate blade is proposed based on Centrifugal Force Induced Twist concept. Tip mass is used to improve the nose-down torsion and the stabilization of rotating flexible blade. The laminate blades are tested in hover and forward flight modes, with deformations measured by Laser Displacement Sensor. Two Laser Displacement Sensors are driven by the tracking systems to scan the rotating blade from root to tip. The distance from blade surface to a reference plane can be recorded section by section. Then, a polynomial surface fitting is applied to reconstruct the shape of rotating blade, including the analysis of measurement precision based on the Kline–McClintock method. The results from deformation testings show that nose-down torsion is generated in each flight mode. The data from a Fluid Structure Interaction model agrees well with experimental results at an acceptable level in terms of the trend predictions.