Aeroelastic Modeling and Simulation of High-Speed Flexible Vehicles

Kitson, Ryan C.; Cesnik, Carlos E. S.

doi:10.2514/6.2016-1324

Cited by 13 publications

(9 citation statements)

References 16 publications

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“…A vehicle representative of slender high speed maneuverable vehicles is used for this study that was previously developed by Kitson and Cesnik. 17 The basic dimensions and flight conditions for the vehicle are shown in Table 2 and are inspired by existing vehicles, such as the Sidewinder and AMRAAM. The outer mold line of the model, shown in Figure 4, was chosen to move the center of pressure aft of the center of gravity based on the lack of fin surfaces that are conventionally located at the tail end of the vehicle.…”

Section: Numerical Modelingmentioning

confidence: 99%

High Speed Vehicle Fluid-Structure-Jet Interaction Analysis and Modeling

Kitson

Cesnik

2017

58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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This paper presents high-fidelity solutions of the fluid-structure-jet interaction problem for slender high-speed vehicles along with two jet interaction modeling methods, one semiempirical and one CFD-based, to approximate the high-fidelity solution. The high-fidelity solutions of a representative high-speed vehicle with jet interaction and structural deformation show that the resultant loads are affected by the deformation. The semi-empirical jet interaction model is developed using previous work in the literature and approximates the main features of the jet interaction solution when compared to numerical and experimental results. The CFD-based jet interaction model is developed by using data-fusion of a previously developed aerodynamic loads surrogate, the semi-empirical jet interaction model, and CFD solutions of the jet interaction. This data-fusion model approximates the surface pressure of a representative high-speed vehicle with varying flow, structure, and jet parameters and can be used within a flight simulation framework. Overall, the work demonstrates a need to model the fluid-structure-jet interaction of high-speed vehicles and modeling methods that may be used to approximate the full solution.

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Section: Numerical Modelingmentioning

confidence: 99%

High Speed Vehicle Fluid-Structure-Jet Interaction Analysis and Modeling

Kitson

Cesnik

2017

58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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“…Evidently, all these different operating conditions have a significant impact on the vehicle dynamics and aeroelastic response. When it comes to the aeroelastic behavior, dynamic aeroelastic effects resulting from the interaction of the aerodynamic, elastic, and inertial forces require careful consideration throughout the design phase of the air vehicle and pose a major safety-critical factor in the qualification of aircraft into service [13][14][15][16][17][18]. Accurate modeling and prediction of the aeroelastic response is a complex and challenging task with the undesirable effects including significant vibrations (for passengers and/or crew), airframe fatigue, loss of control, degraded performance, or even complete destruction of the vehicle itself.…”

Section: Introductionmentioning

confidence: 99%

A stochastic global identification framework for aerospace structures operating under varying flight states

Kopsaftopoulos

Nardari

et al. 2018

Mechanical Systems and Signal Processing

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In this work, a novel data-based stochastic "global" identification framework is introduced for air vehicles operating under varying flight states and uncertainty. In this context, the term "global" refers to the identification of a model that is capable of representing the system dynamics under any admissible flight state based on data recorded from a sample of these states. The proposed framework is based on stochastic time-series models for representing the system dynamics and aeroelastic response under multiple flight states, with each state characterized by several variables, such as the airspeed, angle of attack, altitude, temperature, etc., forming a flight state vector. The method's cornerstone lies in the new class of Vectordependent Functionally Pooled (VFP) models which allow the explicit analytical inclusion of the flight state vector into the model parameters and, hence, system dynamics. This is achieved via the use of functional data pooling techniques for optimally treating -as a single entity-the data records corresponding to the various flight states. In this proof-of-concept study the flight state vector is defined by two variables, namely the airspeed and angle of attack of the vehicle. The experimental evaluation and assessment is based on a prototype bio-inspired self-sensing composite wing that is subjected to a series of wind tunnel experiments under multiple flight states. Distributed micro-sensors in the form of stretchable sensor networks are embedded in the composite layup of the wing in order to provide the sensing capabilities. Experimental data collected from piezoelectric sensors are employed for the identification of a stochastic "global" VFP model via appropriate parameter estimation and model structure selection methods. The estimated VFP model parameters constitute two-dimensional functions of the flight state vector defined by the airspeed and angle of attack. The identified model is able to successfully represent the wing's aeroelastic response under the admissible flight states via a minimum number of estimated parameters compared to standard identification approaches. The obtained results demonstrate the high accuracy and effectiveness of the proposed global identification framework, thus constituting a first step towards the next generation of "fly-by-feel" aerospace vehicles with state awareness capabilities.

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“…The aerodynamic interference model was based on a series of steady-state solutions, which would not capture any of the dynamic aspects during flight simulations. Kitson and Cesnik 16,17 have developed a flexible high speed vehicle model with a reaction control system to maneuver the vehicle during flight simulations within a coupled modeling and simulation framework. However, the JI model was based on steady-state solutions that do not account for the dynamic flight environment or the transient forces due to varying jet pressure.…”

Section: Introductionmentioning

confidence: 99%

“…The FUN3D CFD code 18 is used for the fluid dynamics and aeroelastic analysis with a previously developed slender high-speed vehicle model. 16 First, the effect of high angles of attack and structural deformation on JI is assessed. Next, time-accurate simulations of prescribed rigid-body motion and structural deformation at varying frequencies are presented that highlight the dynamic effect on jet interaction.…”

Section: Introductionmentioning

confidence: 99%