About: Lift-to-drag ratio is a research topic. Over the lifetime, 5014 publications have been published within this topic receiving 73730 citations. The topic is also known as: glide ratio.
Papers published on a yearly basis
TL;DR: In this article, a study was conducted to assess the feasibility of performing computerized wing design by numerical optimization, which combined a full potential, inviscid aerodynamics code with a conjugate gradient optimization algorithm.
Abstract: A study was conducted to assess the feasibility of performing computerized wing design by numerical optimization. The design program combined a full potential, inviscid aerodynamics code with a conjugate gradient optimization algorithm. Three design problems were selected to demonstrate the design technique. The first involved modifying the upper surface of the inboard 50% of a swept wing to reduce the shock drag subject to a constraint on wing volume. The second involved modifying the entire upper surface of the same swept wing (except the tip section) to increase the lift-drag ratio subject to constraints on wing volume and lift coefficient. The final problem involved modifying the inboard 50% of a low-speed wing to achieve good stall progression. Results from the three cases indicate that the technique is sufficiently accurate to permit substantial improvement in the design objectives.
01 Feb 1984
TL;DR: In this paper, a basic ground vehicle type of bluff body, the time averaged wake structure is analyzed for low and high wake flow for the low drag and high drag configurations is described.
Abstract: For a basic ground vehicle type of bluff body, the time averaged wake structure is analysed. At a model length based reynolds number of 4.29 million, detailed pressure measurements, wake survey and force measurements were done in a wind tunnel. Some flow visualisation results were also obtained. Geometric parameter varied was base slant angle. A drag breakdown revealed that almost 85% of body drag is pressure drag. Most of this drag is generated at the rear end. Wake flow exhibits a triple deck system of horseshoe vortices. Strength, existence and merging of these vortices depend upon the base slant angle. Characteristic features of the wake flow for the low drag and high drag configurations is described. Relevance of these phenomena to real ground vehicle flow is addressed.
TL;DR: A dynamically scaled mechanical model of the fruit fly Drosophila melanogaster is used to study how changes in wing kinematics influence the production of unsteady aerodynamic forces in insect flight, finding no evidence that stroke deviation can augment lift, but it nevertheless may be used to modulate forces on the two wings.
Abstract: We used a dynamically scaled mechanical model of the fruit fly Drosophila melanogaster to study how changes in wing kinematics influence the production of unsteady aerodynamic forces in insect flight. We examined 191 separate sets of kinematic patterns that differed with respect to stroke amplitude, angle of attack, flip timing, flip duration and the shape and magnitude of stroke deviation. Instantaneous aerodynamic forces were measured using a two-dimensional force sensor mounted at the base of the wing. The influence of unsteady rotational effects was assessed by comparing the time course of measured forces with that of corresponding translational quasi-steady estimates. For each pattern, we also calculated mean stroke-averaged values of the force coefficients and an estimate of profile power. The results of this analysis may be divided into four main points. (i) For a short, symmetrical wing flip, mean lift was optimized by a stroke amplitude of 180° and an angle of attack of 50°. At all stroke amplitudes, mean drag increased monotonically with increasing angle of attack. Translational quasi-steady predictions better matched the measured values at high stroke amplitude than at low stroke amplitude. This discrepancy was due to the increasing importance of rotational mechanisms in kinematic patterns with low stroke amplitude. (ii) For a 180° stroke amplitude and a 45° angle of attack, lift was maximized by short-duration flips occurring just slightly in advance of stroke reversal. Symmetrical rotations produced similarly high performance. Wing rotation that occurred after stroke reversal, however, produced very low mean lift. (iii) The production of aerodynamic forces was sensitive to changes in the magnitude of the wing’s deviation from the mean stroke plane (stroke deviation) as well as to the actual shape of the wing tip trajectory. However, in all examples, stroke deviation lowered aerodynamic performance relative to the no deviation case. This attenuation was due, in part, to a trade-off between lift and a radially directed component of total aerodynamic force. Thus, while we found no evidence that stroke deviation can augment lift, it nevertheless may be used to modulate forces on the two wings. Thus, insects might use such changes in wing kinematics during steering maneuvers to generate appropriate force moments. (iv) While quasi-steady estimates failed to capture the time course of measured lift for nearly all kinematic patterns, they did predict with reasonable accuracy stroke-averaged values for the mean lift coefficient. However, quasi-steady estimates grossly underestimated the magnitude of the mean drag coefficient under all conditions. This discrepancy was due to the contribution of rotational effects that steady-state estimates do not capture. This result suggests that many prior estimates of mechanical power based on wing kinematics may have been grossly underestimated.
01 Aug 1979
TL;DR: In this paper, the authors discuss the production of Thrust Airplane Performance Helicopters and V/STOL Aircraft Static Stability and Control Open-Loop DSC Controlled Motion and Automatic Stability.
Abstract: Fluid Mechanics Lift Drag Lift and Drag at High Mach Numbers The Production of Thrust Airplane Performance Helicopters and V/STOL Aircraft Static Stability and Control Open-Loop Dynamic Stability and Control Controlled Motion and Automatic Stability.
TL;DR: In this paper, a tuning-free Lagrangian scale-dependent dynamic subgrid-scale (SGS) model is used for the parametrisation of the SGS stresses, and the turbine-induced forces (e.g., thrust, lift and drag) are parametrised using two models: (a) the standard actuator-disk model (ADM-NR), which calculates only the thrust force and distributes it uniformly over the rotor area; and (b) the actuatordisk model with rotation, which uses the blade-element theory to
Abstract: Large-eddy simulation (LES), coupled with a wind-turbine model, is used to investigate the characteristics of a wind-turbine wake in a neutral turbulent boundary-layer flow. The tuning-free Lagrangian scale-dependent dynamic subgrid-scale (SGS) model is used for the parametrisation of the SGS stresses. The turbine-induced forces (e.g., thrust, lift and drag) are parametrised using two models: (a) the ‘standard’ actuator-disk model (ADM-NR), which calculates only the thrust force and distributes it uniformly over the rotor area; and (b) the actuator-disk model with rotation (ADM-R), which uses the blade-element theory to calculate the lift and drag forces (that produce both thrust and rotation), and distribute them over the rotor disk based on the local blade and flow characteristics. Simulation results are compared to high-resolution measurements collected with hot-wire anemometry in the wake of a miniature wind turbine at the St. Anthony Falls Laboratory atmospheric boundary-layer wind tunnel. In general, the characteristics of the wakes simulated with the proposed LES framework are in good agreement with the measurements in the far-wake region. The ADM-R yields improved predictions compared with the ADM-NR in the near-wake region, where including turbine-induced flow rotation and accounting for the non-uniformity of the turbine-induced forces appear to be important. Our results also show that the Lagrangian scale-dependent dynamic SGS model is able to account, without any tuning, for the effects of local shear and flow anisotropy on the distribution of the SGS model coefficient.
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