Pitching moment

About: Pitching moment is a research topic. Over the lifetime, 3213 publications have been published within this topic receiving 38721 citations.

Wind Turbine Performance Under Icing Conditions

Abstract: The effects of rime ice on horizontal axis wind turbine performance were estimated. For typical supercooled fog conditions found in cold northern regions, four rime ice accretions on the S809 wind turbine airfoil were predicted using the NASA LEWICE code. The resulting airfoil/ice profile combinations were wind tunnel tested to obtain the lift, drag, and pitching moment characteristics over the Reynolds number range 1--2 {times} 10{sup 6}. These data were used in the PROPID wind turbine performance prediction code to predict the effects of rime ice on a 450-kW rated-power, 28.7-m diameter turbine operated under both stall-regulated and variable-speed/variable-pitch modes. Performance losses on the order of 20% were observed for the variable-speed/variable-pitch rotor. For the stall-regulated rotor, however, a relatively small rime ice profile yielded significantly larger performance losses. For a larger 0.08c-long rime ice protrusion, however, the rated peak power was exceeded by 16% because at high angles the rime ice shape acted like a leading edge flap, thereby increasing the airfoil C{sub l,max} and delaying stall.

Flight Dynamics and Control of Air-Breathing Hypersonic Vehicles: Review and New Directions

Abstract: The current air-breathing hypersonic flight (AHF) technology programs focus on development of flight test vehicles and operational vehicle prototypes that utilize airframe-integrated scramjet engines. A key issue in making AHF feasible and efficient is the control design. The non-standard dynamic characteristics of air-breathing hypersonic flight vehicles (AHFVs) together with the aerodynamic effects of hypersonic flight make the system modeling and controller design highly challenging. Moreover the wide range of speed during operation and the lack of a broad flight dynamics database add significant plant parameter variations and uncertainties to the AHF modeling and control problem. In this paper, first, different approaches to this challenging problem presented in the literature are reviewed. Basic dynamic characteristics of AHFVs are described and various mathematical models developed for the flight dynamics of AHFVs are presented. Major nonlinearity and uncertainty sources in the AHF dynamics are explained. The theoretical and practical AHF control designs in the literature, including the control schemes in use at NASA research centers, are examined and compared. The review is supported by a brief history of the scramjet and AHF research in order to give a perspective of the AHF technology. Next, the existing gaps in AHF control and the emerging trends in the air-breathing hypersonic transportation are discussed. Potential control design directions to fill these gaps and meet the trends are addressed. The major problem in AHF control is the handling of the various coupling effects, nonlinearities, uncertainties, and plant parameter variations. As a potential solution, the use of integrated robust (adaptive) nonlinear controllers based on time varying decentralized/triangular models is proposed. This specific approach is motivated by the promise of novel techniques in control theory developed in recent years. ∗This work was supported in parts by Air Force Office of Scientific Research under Grant #F49620-01-1-0489 and by NASA under grant URC Grant #NCC4-158. †Student Member AIAA, graduate student, Electrical Engineering Department. ‡Member AIAA, professor, Mechanical Engineering Department. §Professor, Electrical Engineering Department Nomenclature The following notation is used throughout the paper, unless otherwise stated. a∞ : free stream velocity of sound ĀD : diffuser exit/inlet (area) ratio c : reference length CD : drag coefficient CL : lift coefficient Cm : pitching moment coefficient (pmc) Cm(q) : pmc due to pitch rate Cm(α) : pmc angle of attack Cmα : ∂Cm/∂α Cm(δe): pmc due to δe CT : thrust coefficient fs : stoichiometric ratio for hydrogen, 0.029 h : vehicle altitude I (In) : the (n× n) identity matrix Iyy : vehicle y-axis inertia per unit width m : vehicle mass m : vehicle mass per unit width ṁair : air mass flow rate ṁf : fuel mass flow rate M : pitching moment M∞ : vehicle flight Mach Number nx : acceleration along the vehicle x-axis nz : acceleration along the vehicle z-axis P : pressure q : pitch rate Q : generalized elastic force re : radial distance from Earth’s center Re : radius of the Earth, 20,903,500 ft S : reference area T0 : temperature across the combustor Th : thrust u : speed along the vehicle x-axis V : vehicle velocity X : force along the vehicle x-axis Z : force along the vehicle z-axis α : angle of attack γ : flight path angle (γ = θ − α) δe : pitch control surface deflection δt : throttle setting ∆τ1 : fore-body elastic mode shape ∆τ2 : after-body elastic mode shape ζ1 : damping ratio of the first vibration mode η : generalized elastic coordinate 1 American Institute of Aeronautics and Astronautics 12th AIAA International Space Planes and Hypersonic Systems and Technologies 15 19 December 2003, Norfolk, Virginia AIAA 2003-7081 Copyright © 2003 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. ηf : fuel equivalence ratio, ṁf fsṁair θ : pitch angle μg : gravitational constant ρ : density of air ω1 : natural frequency of the first vibration mode 0n×m: the n×m zero matrix Subscripts A : due to aerodynamics E : due to external nozzle T : due to engine thrust 0 : trim condition ∞ : free stream condition

Data Summary from Second AIAA Computational Fluid Dynamics Drag Prediction Workshop

Abstract: Results from the Second AIAA Drag PredictionWorkshop are summarized. The workshop focused on absolute and configuration delta drag prediction of the DLR, German Aerospace Research Center F6 geometry, which is representative of transport aircraft designed for transonic flight. Both wing–body and wing–body–nacelle–pylon configurations are considered. Comparisons are made using industry relevant test cases that include single-point conditions, drag polars, and drag-rise curves. Drag, lift, and pitching moment predictions from several different Reynolds averagedNavier–Stokes computational fluid dynamics codes are presented and compared to experimental data. Solutions on multiblock structured, unstructured, and overset structured grids using a variety of turbulence models are considered. Results of a grid-refinement study and a comparison of tripped transition vs fully turbulent boundary-layer computations are reported.

Aerodynamic Design Optimization Studies of a Blended-Wing-Body Aircraft

Abstract: The blended wing body is an aircraft configuration that has the potential to be more efficient than conventional large transport aircraft configurations with the same capability. However, the design of the blended wing is challenging due to the tight coupling between aerodynamic performance, trim, and stability. Other design challenges include the nature and number of the design variables involved, and the transonic flow conditions. To address these issues, a series of aerodynamic shape optimization studies using Reynolds-averaged Navier–Stokes computational fluid dynamics with a Spalart–Allmaras turbulence model is performed. A gradient-based optimization algorithm is used in conjunction with a discrete adjoint method that computes the derivatives of the aerodynamic forces. A total of 273 design variables—twist, airfoil shape, sweep, chord, and span—are considered. The drag coefficient at the cruise condition is minimized subject to lift, trim, static margin, and center plane bending moment constraints. ...

Dynamic flight stability of hovering insects

Abstract: The equations of motion of an insect with flapping wings are derived and then simplified to that of a flying body using the “rigid body” assumption. On the basis of the simplified equations of motion, the longitudinal dynamic flight stability of four insects (hoverfly, cranefly, dronefly and hawkmoth) in hovering flight is studied (the mass of the insects ranging from 11 to 1,648 mg and wingbeat frequency from 26 to 157 Hz). The method of computational fluid dynamics is used to compute the aerodynamic derivatives and the techniques of eigenvalue and eigenvector analysis are used to solve the equations of motion. The validity of the “rigid body” assumption is tested and how differences in size and wing kinematics influence the applicability of the “rigid body” assumption is investigated. The primary findings are: (1) For insects considered in the present study and those with relatively high wingbeat frequency (hoverfly, drone fly and bumblebee), the “rigid body” assumption is reasonable, and for those with relatively low wingbeat frequency (cranefly and howkmoth), the applicability of the “rigid body” assumption is questionable. (2) The same three natural modes of motion as those reported recently for a bumblebee are identified, i.e., one unstable oscillatory mode, one stable fast subsidence mode and one stable slow subsidence mode. (3) Approximate analytical expressions of the eigenvalues, which give physical insight into the genesis of the natural modes of motion, are derived. The expressions identify the speed derivative Mu (pitching moment produced by unit horizontal speed) as the primary source of the unstable oscillatory mode and the stable fast subsidence mode and Zw (vertical force produced by unit vertical speed) as the primary source of the stable slow subsidence mode.