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The primary issues involved in the generation of linear, state-space equations of motion of a flexible wind tunnel model, the Active Flexible Wing (AFW), are discussed. The codes that were used and their inherent assumptions and limitations are also briefly discussed. The application of the CAP-TSD code to the AFW for determination of the model's transonic flutter boundary is included as well.
Wind tunnel model and flight tests of parawing lifting body landing system
A solid uninstrumented wing and a pressure instrumented wing having a tongue and groove type joint have been structurally modeled by using SPAR Structural Analysis System. The solid wing model can be analyzed by SPAR, using either solid elements or plate elements with reasonable accuracy. The instrumented wing models with tongue and groove joint can be modeled by using solid elements. A solid model representation for the wing with tongue and groove joint can be used for the cases where an error of 6 percent in deflections is acceptable.
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The 12-Foot Pressure Wind Tunnel at Ames Research Center is being restored. A major part of the restoration is the complete redesign of the aircraft model supports and their associated control systems. An accurate trajectory control servo system capable of positioning a model (with no measurable overshoot) is needed. Extremely small errors in scaled-model pitch angle can increase airline fuel costs for the final aircraft configuration by millions of dollars. In order to make a mechanism sufficiently accurate in pitch, a detailed structural and control-system model must be created and then simulated on a digital computer. The model must contain linear representations of the mechanical system, including masses, springs, and damping in order to determine system modes. Electrical components, both analog and digital, linear and nonlinear must also be simulated. The model of the entire closed-loop system must then be tuned to control the modes of the flexible model-support structure. The development of a system model, the control modal analysis, and the control-system design are discussed.
The 12-Foot Pressure Wind Tunnel at Ames Research Center is being restored. A major part of the restoration is the complete redesign of the aircraft model supports and their associated control systems. An accurate trajectory control servo system capable of positioning a model (with no measurable overshoot) is needed. Extremely small errors in scaled-model pitch angle can increase airline fuel costs for the final aircraft configuration by millions of dollars. In order to make a mechanism sufficiently accurate in pitch, a detailed structural and control-system model must be created and then simulated on a digital computer. The model must contain linear representations of the mechanical system, including masses, springs, and damping in order to determine system modes. Electrical components, both analog and digital, linear and nonlinear must also be simulated. The model of the entire closed-loop system must then be tuned to control the modes of the flexible model-support structure. The development of a system model, the control modal analysis, and the control-system design are discussed.
This paper presents the gust load alleviation (GLA) study of the aspect ratio 13.5 Common Research Model (CRM) wind-tunnel model. This study details the design of the GLA controller in preparation for wind-tunnel testing in the Transonic Dynamics Tunnel at NASA Langley Research Center. An aeroservolastic (ASE) model was first reduced using a model reduction method that takes advantage of the sinusoidal steady-state response. Then, the reduced model was used to design an extended-state Kalman filter which estimates the states and the sinusoidal gust input. The GLA control was then derived using the optimal control solution to a multi-objective cost function. The results of the GLA controller indicate a 75.6% reduction in wing-root strain while maintaining robust stability margins. The final paper will include performance under sub-optimal conditions, e.g., sensor noise, uncertainty in gust frequency, etc..
This paper presents the gust load alleviation (GLA) study of the aspect ratio 13.5 Common Research Model (CRM) wind-tunnel model. This study details the design of the GLA controller in preparation for wind-tunnel testing in the Transonic Dynamics Tunnel at NASA Langley Research Center. An aeroservolastic (ASE) model is first reduced using a model reduction method that takes advantage of the sinusoidal steady-state response. Then, the reduced model is used to design an extended-state Kalman filter which estimates the states and the sinusoidal gust input. The GLA control is then derived using the optimal control solution to a multi-objective cost function. The results of the GLA controller indicate a 69.07% reduction in wing-root strain without sensor noise and 68.45% reduction with sensor noise, while maintaining robust stability margins. An adaptive GLA controller is developed for uncertain gust frequency and shows a 71.03% reduction in wing root strain compared to the non-adaptive control reduction of just 39.19%.
A protective shell mechanism for wind tunnel models was developed and tested. The mechanism is passive in operation, reliable, and imposes no new structural design changes for wind tunnel models. Methods of predicting the release time and the measured loads associated with the release of the shell are given. The mechanism was tested in a series of wind tunnel tests to validate the removal process and measure the pressure loads on the model. The protective shell can be used for wind tunnel models that require a step input of heating and loading such as a thin skin heat transfer model. The mechanism may have other potential applications.
Inviscid, thermal conductive, spherically symmetric solar wind model - model solution by integration of mass, momentum, and energy conservation equations
Unexpected shocks on wind-tunnel-measured pressure signatures prompted questions about design methods, pressure signature measurement techniques, and the quality of measurements in the flow fields near lifting models. Some of these unexpected shocks were the result of component integration methods. Others were attributed to the three-dimension nature of the flow around a lifting model, to inaccuracies in the prediction of the area-ruled lift, or to wing-tip stall effects. This report discusses the low-boom model wind-tunnel data where these unexpected shocks were initially observed, the physics of the lifting wing/body model's flow field, the wind-tunnel data used to evaluate the applicability of methods for calculating equivalent areas due to lift, the performance of lift prediction codes, and tip stall effects so that the cause of these shocks could be determined.
Robust adaptive mesh refinement for transonic conditions is one of the most difficult challenges to advancing CFD usage for certification by analysis. Transonic fluid-structure interaction cases represent an even more difficult challenge for adaptive mesh refinement. Production aircraft furthermore have structural models that have gaps and discontinuities that make the interdomain transfer of loads and deflections difficult. This adds to the challenge of adaptive mesh refinement. An initial attempt to create an adaptive mesh refinement process that refines regions of high flow gradient based around the HeldenMesh software is presented. The method is applied to the Integrated Adaptive Wing Technology Maturation (IAWTM) configuration. That has developed an aspect ratio 13.5 wing aeroelastic wind-tunnel model for testing in the Transonic Dynamics Tunnel (TDT). The high levels of deformation of this wing make the adaptation of meshes for the static shape difficult and has necessitated some creativity in how to efficiently adapt meshes. A time-domain identification of the unsteady system based on flexible modes using the adapted meshes is performed.
Simulations of an aeroelastically scaled wind-tunnel model were developed for hot-bench testing of a digital controller. The digital controller provided active flutter-suppression, rolling-maneuver-load alleviation, and plant estimation. To achieve an acceptable time scale for the hot-bench application, the mathematical model of the wind-tunnel model was reduced from 220 states to approximately 130 states while assuring that the required accuracy was preserved for all combinations of 10 inputs and 56 outputs. The reduction was achieved by focussing on a linear, aeroelastic submodel of the full mathematical model and by applying a method based on the internally balanced realization of a dynamic system. The error-bound properties of the internally balanced realization significantly contribute to its utility in the model reduction process. The reduction method and the results achieved are described.
Wind tunnel free-flight testing of reentry vehicle dynamics, noting capability for simultaneous two- dimensional viewing of model trajectories
This paper presents an approach to the development of a scaled wind tunnel model for static aeroelastic similarity with a full-scale wing model. The full-scale aircraft model is based on the NASA Generic Transport Model (GTM) with flexible wing structures referred to as the Elastically Shaped Aircraft Concept (ESAC). The baseline stiffness of the ESAC wing represents a conventionally stiff wing model. Static aeroelastic scaling is conducted on the stiff wing configuration to develop the wind tunnel model, but additional tailoring is also conducted such that the wind tunnel model achieves a 10% wing tip deflection at the wind tunnel test condition. An aeroelastic scaling procedure and analysis is conducted, and a sub-scale flexible wind tunnel model based on the full-scale's undeformed jig-shape is developed. Optimization of the flexible wind tunnel model's undeflected twist along the span, or pre-twist or wash-out, is then conducted for the design test condition. The resulting wind tunnel model is an aeroelastic model designed for the wind tunnel test condition.
Aeroservoelastic (ASE) analytical models of a SensorCraft wind-tunnel model are generated using measured data. The data was acquired during the ASE wind-tunnel test of the HiLDA (High Lift-to-Drag Active) Wing model, tested in the NASA Langley Transonic Dynamics Tunnel (TDT) in late 2004. Two time-domain system identification techniques are applied to the development of the ASE analytical models: impulse response (IR) method and the Generalized Predictive Control (GPC) method. Using measured control surface inputs (frequency sweeps) and associated sensor responses, the IR method is used to extract corresponding input/output impulse response pairs. These impulse responses are then transformed into state-space models for use in ASE analyses. Similarly, the GPC method transforms measured random control surface inputs and associated sensor responses into an AutoRegressive with eXogenous input (ARX) model. The ARX model is then used to develop the gust load alleviation (GLA) control law. For the IR method, comparison of measured with simulated responses are presented to investigate the accuracy of the ASE analytical models developed. For the GPC method, comparison of simulated open-loop and closed-loop (GLA) time histories are presented.
Aeroservoelastic (ASE) analytical models of a SensorCraft wind-tunnel model are generated using measured data. The data was acquired during the ASE wind-tunnel test of the HiLDA (High Lift-to-Drag Active) Wing model, tested in the NASA Langley Transonic Dynamics Tunnel (TDT) in late 2004. Two time-domain system identification techniques are applied to the development of the ASE analytical models: impulse response (IR) method and the Generalized Predictive Control (GPC) method. Using measured control surface inputs (frequency sweeps) and associated sensor responses, the IR method is used to extract corresponding input/output impulse response pairs. These impulse responses are then transformed into state-space models for use in ASE analyses. Similarly, the GPC method transforms measured random control surface inputs and associated sensor responses into an AutoRegressive with eXogenous input (ARX) model. The ARX model is then used to develop the gust load alleviation (GLA) control law. For the IR method, comparison of measured with simulated responses are presented to investigate the accuracy of the ASE analytical models developed. For the GPC method, comparison of simulated open-loop and closed-loop (GLA) time histories are presented.
Wind tunnel investigations of effect on static stability of modifications to swept wing fighter aircraft model