Reduced-Order Modeling of Hypersonic Vehicle Unsteady Aerodynamics

Skujins, Torstens; Cesnik, Carlos E. S.

doi:10.2514/6.2010-8127

Cited by 14 publications

(16 citation statements)

References 21 publications

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“…Skujins and Cesnik [57][58][59], for example, predicted unsteady loads due to the oscillation of multiple vehicle elastic modes using convolution of modal step responses along with a correction factor which takes into account different oscillation amplitudes, fight conditions, and nonlinear aerodynamic effects due to multi-modal oscillations. Glaz et al [33] also described a reduced-order nonlinear unsteady aerodynamic modeling approach suitable for analyzing pitching/plunging airfoils subject to fixed or time-varying freestream Mach numbers.…”

Section: Current Aerodynamic Modeling Effortsmentioning

confidence: 99%

Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics

Ghoreyshi

Jirasek

Cummings

2014

Progress in Aerospace Sciences

118

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, "Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics" (2014 Contents ABSTRACTRecent advances and challenges in the generation of reduced order aerodynamic models using computational fluid dynamics are presented. The models reviewed are those that can be used for aircraft stability and control analysis and include linear and nonlinear indicial response methods, Volterra theory, radial basis functions, and a surrogate-based recurrence framework. The challenges associated with identification of unknowns for each of the reduced order methods are addressed. A range of test cases, from airfoils to full aircraft, have been used to evaluate and validate the reduced order methods. The motions have different amplitudes and reduced frequencies and could start from different flight conditions including those in the transonic speed range. Overall, these reduced order models help to produce accurate predictions for a wide range of motions, but with the advantage that model predictions require orders of magnitude less time to evaluate once the model is created.Published by Elsevier Ltd.

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Section: Current Aerodynamic Modeling Effortsmentioning

confidence: 99%

Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics

Ghoreyshi

Jirasek

Cummings

2014

Progress in Aerospace Sciences

118

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“…In addition these models typically over-predict the turbulent kinetic energy. 4 This paper develops low dimensional models that capture the important dynamics of a mixing layer flow collected experimentally, and lays the foundation for future research in implementing a closed-loop control system. In addition, challenges specific to generating ROMs from particle image velocimetry (PIV) are discussed, with approaches to these challenges explored.…”

Section: Introductionmentioning

confidence: 99%

Reduced Order Modeling of a Dielectric Barrier Discharge Controlled Shear Layer using Minimum Basis Rotations

Chabot

Caraballo

Little³

2015

45th AIAA Fluid Dynamics Conference

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This work focuses on the development of reduced order models for flow control application in a mixing layer. Reduced order models are obtain by applying the Proper Orthogonal Decomposition (POD) method to empirical data sets of the baseline and open loop forced mixing layer to determine the spatial basis of the flow. The forcing is introduced using dielectric barrier discharge (DBD) plasma actuators. The time evolution of the modal amplitude for the models are obtained by a Galerkin Projection of the Navier-Stoke (NS) equations on the spatial basis (POD Modes). This POD-Galerkin based model has well known tendencies to produce models that vastly over predict turbulent kinetic energy in the flow. Several eddy-viscosity models, as well a basis transformation, are simulated in multiple combinations to elicit trends in effectiveness. The results indicate that models derived from lower order POD modes tend to show more accurate results in terms of both frequency spectrum and predicted energy. Additionally, nonlinear scaling of eddy viscosity terms and the basis transformation show improvement over simple linear correctors.

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“…Recently, ROMs have been gaining popularity due to advances in data recording, increased computing speeds, and their reduced computational cost compared to direct numerical simulation of the NS equations (Gunes & Rist, 2004, Cordier et al, 2010. Despite these advances, ROMs still fall short in that most work only for a specific system with particular characteristics such as Mach number, dynamic pressure, and other flow conditions (Skujins & Cesnik, 2010).…”

Section: Introductionmentioning

confidence: 99%

Reduced Order Modeling of Flow over a NACA 0015 Airfoil For Control Application

Caraballo

Sullivan

Little³

2014

7th AIAA Flow Control Conference

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Reduced order models that can capture and predict general flow characteristics are essential for closed-loop flow control around aerodynamic bodies. This research tests Galerkin based reduced order models using a proper orthogonal decomposition method to obtain a set of modes derived from experimental data to predict the flow over a NACA-0015 airfoil. Three methodologies are used to derive the models: the original POD Galerkin projection method, a modified version that includes a viscous dissipation term, and a new approach that uses a transfer parameter term to adjust the POD modes. Each of the models is tested on the airfoil at incident angles ranging from to for the baseline flow and a flow forced at 1250 Hz by nanosecond dielectric barrier discharge plasma actuators. The results showed that the two modified reduced order models were most successful in predicting the time evolution coefficient using between 8 and 12 POD modes for their calculations. At this point, there was no clear indication that one method worked better than the other for the baseline or forced flow, or for a particular angle of attack of the airfoil. The behavior of the transfer parameter versus the angle of attack was investigated in order to find a predictable trend that would reduce the overall computational time, but no clear trend has been observed at this point.

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Reduced-Order Modeling of Hypersonic Vehicle Unsteady Aerodynamics

Cited by 14 publications

References 21 publications

Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics

Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics

Reduced Order Modeling of a Dielectric Barrier Discharge Controlled Shear Layer using Minimum Basis Rotations

Reduced Order Modeling of Flow over a NACA 0015 Airfoil For Control Application

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