Jurij Sodja

The aim of the paper is to experimentally validate a numerical design methodology for optimizing composite wings subject to gust and fatigue loading requirements and to assess the effect of fatigue on the aeroelastic performance of the wing. Traditionally, to account for fatigue in composite design, a knockdown factor on the maximum stress allowable is applied, resulting in a conservative design. In the current design methodology, an analytical fatigue model is used to reduce the conservativeness and exploit the potential of composite materials. To validate the proposed analytical model, a rectangular composite wing is designed and manufactured to be critical in strength, buckling and fatigue. An experimental campaign comprising wind tunnel and fatigue tests is performed. In the wind tunnel, both static and dynamic aeroelastic experiments are conducted to validate the numerical dynamic aeroelastic model. The fatigue test is used to validate the analytical fatigue model and to understand the effect of fatigue on aeroelastic properties of the wings. The results from experimental campaign validated both the aeroelastic predictions as well as fatigue predictions of the numerical design methodology. However the fatigue process resulted in degradation of the wing stiffness leading to change in the aeroelastic response of the wing.

The mathematical model and experimental verification of flexible propeller blades are presented in this paper. The propeller aerodynamics model is based on an extended blade-element momentum model, while the Euler–Bernoulli beam theory and Saint–Venant theory of torsion are used to account for bending and torsional deformations of the blades, respectively. The proposed blade-element momentum model extends the standard blade-element momentum theory with the aim of providing a quick and robust model of propeller action capable of treating high-aspect-ratio propeller blades with a blade axis of arbitrary geometry. Based on the proposed mathematical model, a static flexible propeller blade design procedure and its associated analysis algorithm are established. Dynamic aeroelastic phenomena like propeller flutter and divergence are not covered by the presented mathematical model, design, and analysis algorithm. Experimental validation was carried out with an objective of evaluating the performance of the developed mathematical model and the design strategy. Both theoretical and experimental results are presented along with pertinent concluding remarks.

Abstract The presented study investigates the design and development of an autonomous morphing wing concept developed in the scope of the SmartX project, which aims to demonstrate in-flight performance optimisation with active morphing. To progress this goal, a novel distributed morphing concept with six translation induced camber morphing trailing edge modules is proposed in this study. The modules are interconnected using elastomeric skin segments to allow seamless variation of local lift distribution along the wingspan. A fluid-structure interaction optimisation tool is developed to produce an optimised laminate design considering the ply orientation, laminate thickness, laminate properties and actuation loads of the module. Analysis of the kinematic model of the integrated actuator system is performed, and a design is achieved, which meets the required continuous load and fulfils both static and dynamic requirements in terms of bandwidth and peak actuator torque with conventional actuators. The morphing design is validated using digital image correlation measurements of the morphing modules. Characterisation of mechanical losses in the actuator mechanism is performed. Out-of-plane deformations in the bottom skin and added stiffness of the elastomer are identified as the impacting factors of the reduced tip deflection.

B = number of propeller blades CD = drag coefficient CL = lift coefficient CP = power coefficient, P= n D CT = thrust coefficient, T= n D c = chord D = propeller diameter G = normalized circulation function GG = Goldstein’s normalized circulation function GP = Prandtl’s normalized circulation function J = advance ratio, v0=nD M = Mach number n = propeller revolutions per second, =2 P = power Q = torque R = propeller radius Re = Reynolds number RH = propeller hub radius R1 = wake radius r = radial coordinate S = propeller disk area, R T = thrust ua = axial induced velocity u = angular induced velocity v = resultant velocity v0 = advance velocity w = axial vortex displacement velocity y = nondimensional wall distance = angle of attack = blade pitch angle = circulation = drag-to-lift ratio = propeller efficiency = propeller blade pitch, v0t0 2 v0= 1 = wake pitch = speed ratio, v0= R 1 = wake speed ratio, v0 w = R1 = nondimensional radius, r=R 1 = nondimensional wake radius, r=R1 = fluid density = flow angle = propeller angular velocity