This paper describes a fundamental experimental study, which involved systematic performance and flowfield measurements (PIV) to understand and optimize the hover performance of a MAV-scale helicopter rotor operating at Reynolds numbers lower than 30,000. The rotor parameters that were varied include blade airfoil profile, blade chord, number of blades, blade twist, planform taper and winglets at blade tip. Blade airfoil section had a significant impact on the hover efficiency and among the large number of airfoil sections tested, the ones with the lower thickness to chord ratios and moderate camber (4.5% to 6.5%) produced the highest rotor hover figure of merit. Increasing the solidity of the rotor by increasing the number blades (with constant blade chord) had minimal effect on efficiency; whereas, increasing the solidity by increasing blade chord for a 2-bladed rotor, significantly improved hover efficiency. Moderate blade twist (-10t o -20˚) and large planform taper (larger than 0.5) marginally improved rotor efficiency. Rotor blades with small winglets (height ≈ 6% of rotor radius) at the tip also improved hover performance. While using winglets, the flowfield measurements showed a diffused tip vortex, which could reduce the induced aerodynamic losses. Spanwise lift distribution obtained using sectional bound circulation computed from the measured flowfield correlated well with the load cell measurements. The optimal rotor designed based on the understanding gained from the present study produced a figure of merit of 0.67, which is the highest value of FM ever reported in the literature for micro-rotors operating at these low Reynolds numbers. INTRODUCTIONMicro Air Vehicles (MAVs) are small-scale aerial platforms, which are envisioned to have a wide range of both military and civilian applications. Being small and compact systems, MAVs offer several advantages such as portability, rapid deployment, real-time data acquisition capability, low radar cross section, low noise signatures and low production cost. The micro air vehicle concept was first proposed by DARPA back in 1997 [1] and according to DARPA's original definition, the size of these vehicles has to be within 6 inches (0.154 m) with a gross weight of 100 grams (including 20 grams payload) and a flight endurance of 60 minutes. However, even after substantial progress in the last two decades, the fact that none of the current MAVs are even close to achieving this endurance goal (60 mins), is a true testament to the difficulty of this problem. The key reasons for this are the inefficiencies associated with the low Reynolds number aerodynamic regime at which these vehicles operate and challenges in smallscale power generation and storage. Note that, one of the least understood aspect of small-scale flight is its aerodynamic performance, which is the focus of the current study.From an aerodynamics perspective, a key challenge for a MAV designer is the low lift-to-drag ratio of even the most optimized airfoil geometries at low Reynolds numbers. Several fi...
A three-dimensional constraint-driven dynamic rigid-link numerical model of a flapping wing structure with compliant joints (CJs) called the dynamic spar numerical model is introduced and implemented. CJs are modeled as spherical joints with distributed mass and spring-dampers with coupled nonlinear spring and damping coefficients, which models compliant mechanisms spatially distributed in the structure while greatly reducing computation time compared to a finite element model. The constraints are established, followed by the formulation of a state model used in conjunction with a forward time integrator, an experiment to verify a rigid-link assumption and determine a flapping angle function, and finally several example runs. Modeling the CJs as coupled bi-linear springs shows the wing is able to flex more during upstroke than downstroke. Coupling the spring stiffnesses allows an angular deformation about one axis to induce an angular deformation about another axis, where the magnitude is proportional to the coupling term. Modeling both the leading edge and diagonal spars shows that the diagonal spar changes the kinematics of the leading edge spar verses only considering the leading edge spar, causing much larger axial rotations in the leading edge spar. The kinematics are very sensitive to CJ location, where moving the CJ toward the wing root causes a stronger response, and adding multiple CJs on the leading edge spar with a CJ on the diagonal spar allows the wing to deform with larger magnitude in all directions. This model lays a framework for a tool which can be used to understand flapping wing flight.
The generalizability of a convolutional encoder-decoder based model in predicting aerodynamic flow field across various flow regimes and geometric variation is assessed. A rich master dataset consisting of 11,000+ simulations including cambered, uncambered, thin and thick airfoils simulated at varying angles of attack is generated. The various Mach and Reynolds number (Re) chosen allows analysis across compressible, incompressible, low and high Re flow regimes. Multiple studies are carried out with the model trained on datasets that are categorized based on the above parameters. In each study, the loss of prediction accuracy by training the model on a larger dataset (generalizability), versus a smaller categorically sorted dataset, is evaluated. Largely disparate flow features across the Re range lead to a 25.56% loss, while the generalization across Mach range led to an average of 23.95% loss. However, flow-field changes induced due to geometric variation exhibited a better generalization potential, through an increased accuracy of 12.4%. The encoder-decoder architecture allows extraction of relevant geometric features from largely different geometries (geometric generalization) providing a better out-of-sample prediction accuracy in comparison to physics-based generalization. It is shown that, through user-informed choice of training data (removal of geometrically similar samples), computational costs incurred in generating training data can be reduced. This is important for the application of such methods in the design optimization of platforms and components that require analysis of the fluid flows.
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