Variation Of Aerodynamic Coefficients Research Articles

Wind tunnel investigations of aerodynamic coefficients of road vehicles on bridge deck

To assess the safety of road vehicles running over a long span bridge in high wind, the knowledge of aerodynamic forces and moments on the vehicles is required. This could be obtained through wind tunnel investigations and expressed in terms of aerodynamic coefficients. However, very few investigations have been carried out to find aerodynamic coefficients of road vehicles on a bridge deck, compared with the aerodynamic coefficients of road vehicles on the ground. This paper therefore presents the results of wind tunnel tests carried out to determine aerodynamic coefficients of four types of road vehicles over a typical bridge deck. The aerodynamic coefficients of the same road vehicles on the ground are also presented to explore the effects of the bridge deck on aerodynamic coefficients. Different wind directions are taken into consideration to observe variations of aerodynamic coefficients with wind direction. Furthermore, the effects of vehicle position in different road lanes of the bridge deck are investigated. The test results show that the existence of bridge deck reduces side forces for all four types of vehicles remarkably but increases rolling moments to some extent. Such effects also vary with types of vehicles and lanes of the bridge deck where vehicles run.

Non-linear control of an uncertain hypersonic aircraft model using robust sum-of-squares method

In recent years, sum-of-squares (SOS) method has attracted increasing interest as a new approach for stability analysis and controller design of non-linear dynamic systems. In this study, the authors present a robust SOS/robust LMI method to design a non-linear controller for longitudinal dynamics of a hypersonic aircraft model with parametric uncertainties. To this end, the control design problem is first formulated as a robust SOS problem. Then, an LMI representation of the robust SOS problem is derived using a proposed algorithm and is solved via a stochastic ellipsoid method. As the simulation results show, the designed robust controller is capable of stabilising the aircraft and following pilot commands under up to 50% variation in aerodynamic coefficients.

A Reverse Approach for Aerodynamic Analysis of a Typical UAV

A reverse approach has been followed to handle the problem of investigating the aerodynamic characteristics of a typical unmanned aerial vehicle model which are assumed unknown. Both numerical and experimental investigations have been conducted using wind tunnel and CFD facilities. The tests have been conducted in a low speed wind tunnel for a half section of real size tailless model. The aerodynamic loads have been measured and the loads were varied with pitch performed in range of (-2 to +26°) angles of attack with pre-selected test wind speeds between (25 – 40) m/s. The test was carried out with different elevon deflection angles selected as (0-10-20-30°). The pressure tabs to investigate the chord-wise pressure distribution at Mean Aerodynamic Chord (MAC) for the sake of comparison between wind tunnel and CFD results. The test section conditions have been taken as inputs for the simulation with CFD Fluent software. Results revealed that the model was statically stable and the variation of aerodynamic coefficients to wind speed, angle of attack and elevon deflection is displayed. The pressure distribution along the MAC demonstrated the expected behaviour of the chord-wise pressure distribution during stall and laminar flow around the airfoil.

Uncertainty Identification of Supersonic Wind-Tunnel Testing

In this study, a methodology for the identification of uncertainty of supersonic wind tunnel testing is developed. Instead of conventional multi-tunnel testing, multi-position testing is performed across the characteristic net of supersonic flow. From the variations in aerodynamic coefficients in body axes, the uncertainty caused by flow quality is identified about models of interest. Then, after the consideration of practically independent parameters, uncertainties of aerodynamic coefficients in stability axes are combined. Their validities are confirmed by correlation results of tunnel-to-tunnel comparison tests.

Wake transition in flow past a circular cylinder

The transition of the wake of a circular cylinder is investigated numerically via a stabilized finite element method for 150≤Re≤350. Both the flow and aerodynamic coefficients are studied. The onset of the three-dimensionality of the flow takes place via the mode-A instability at Re=200. At this Re, the flow exhibits pure mode-A type flow structures for t<1800. At larger times, the vortex dislocations appear spontaneously and destroy the spanwise periodicity in the flow. This confirms the hypothesis that the fully developed mode-A flow structures cannot exist without vortex dislocations. The appearance of dislocations leads to time variation in the vortex shedding frequency. They also lead to a reduction in the global aerodynamic parameters such as drag coefficient, rms value of lift coefficient, and dominant vortex shedding frequency. The vortex dislocations repetitively appear and disappear from the flow. The aerodynamic coefficients achieve a relatively lower value at the time instant when vortex dislocations appear in the flow. This leads to a low frequency modulation in the time variation of aerodynamic coefficients. The onset of mode-A is hysteretic. This is demonstrated in the present work via computations perhaps for the first time for increasing and decreasing Re. The transition from mode-A to mode-B vortex structures is gradual and not hysteretic. Mode-B is devoid of vortex dislocations and, therefore, the aerodynamic coefficients achieve a relatively larger value. The discontinuity in the variation of aerodynamic coefficients with Re is captured very well by the present computations. Unlike mode-A, the flow structures of the mode-B instability are restricted to the near wake.

Direct adaptive neural flight control system for an unstable unmanned aircraft

A direct adaptive controller design using neural network is proposed for an unstable unmanned research aircraft similar in configuration to combat aircraft. The control law to track the pitch rate command is developed based on system theory. Neural network with linear filters and back propagation through time learning algorithm is used to approximate the control law. The bounded signal requirement to develop the neural controller is circumvented using an off-line finite time training scheme, which provides the necessary stability and tracking performances. On-line learning scheme is implemented to compensate for uncertainties due to variation in aerodynamic coefficients, control surface failures and also variations in center of gravity position. The performance of the proposed control scheme is validated at different flight conditions. The disturbance rejection capability of the neural controller is analyzed in the presence of the realistic gust and sensor noises. Hardware-in-loop simulation is also carried out to study the behavior of control surface deflections in real-time.

Direct Adaptive Neural Flight Controller for F-8 Fighter Aircraft

A novel neural network approach based on model-following direct adaptive control system design is proposed to improve damping and also to follow pilot commands accurately. The control law is derived using system theory and an implicit function theorem. A neural network with a linear filter is used to approximate the control law. The neural controller is trained using offline (finite interval of time) and online learning strategies. The neural controller was trained offline using the error signal between the aircraft response and pilot command (reference model). The offline-trained neural controller provides the necessary damping and tracking performances. The neural controller is adapted online for variations in aerodynamic coefficients or control surface deficiencies caused by control surface damage. A discrete time linear dynamic longitudinal model of a high-performance aircraft (F-8) is considered to demonstrate the effectiveness of the proposed control scheme. The performance results of the proposed control scheme are compared with the recently developed dynamic inversion technique and fully tuned radial basis function network. The neural controller performance is also evaluated for steady climb-and-hold maneuver in a nonlinear six-degree-of-freedom aircraft model.

신경회로망을 이용한 이산 비선형 재형상 비행제어시스템

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

Reconfigurable Flight Control System Design Using Adaptive Neural Networks

An adaptive controller design method based on neural network is proposed for reconfigurable flight control systems in the presence of variations in aerodynamic coefficients or control effectiveness deficiencies caused by control surface damage. The neural network based adaptive nonlinear controller is developed by using the backstepping technique for command following of the angle of attack, sideslip angle, and bank angle. On-line learning neural networks are implemented to compensate the control effectiveness decrease and guarantee the robustness to the uncertainties due to aerodynamic coefficients variations. The main feature of the proposed controller is that the adaptive controller is designed by assuming that all of the nonlinear functions of the system have uncertainties, whereas most of the previous works assume that only some of the nonlinear functions are unknown. Neural networks learn through the weight update rules that are derived from the Lyapunov control theory. The closed-loop stability of the error states is also investigated. A nonlinear dynamic model of a high performance aircraft is used to demonstrate the effectiveness of the proposed control law.

백스테핑기법과 신경회로망을 이용한 적응 재형상 비행제어법칙

A neural network based adaptive controller design method is proposed for reconfigurable flight control systems in the presence of variations in aerodynamic coefficients or control effectiveness decrease caused by control surface damage. The neural network based adaptive nonlinear controller is developed by making use of the backstepping technique for command following of the angle of attack, sideslip angle, and bank angle. On-line teaming neural networks are implemented to guarantee reconfigurability and robustness to the uncertainties caused by aerodynamic coefficients variations. The main feature of the proposed controller is that the adaptive controller is designed with assumption that not any of the nonlinear functions of the system is known accurately, whereas most of the previous works assume that only some of the nonlinear functions are unknown. Neural networks loam through the weight update rules that are derived from the Lyapunov control theory. The closed-loop stability of the error states is also investigated according to the Lyapunov theory. A nonlinear dynamic model of an F-16 aircraft is used to demonstrate the effectiveness of the proposed control law.