Towards an Efficient Aircraft Stability and Control Analysis Capability Using High-Fidelity CFD

Görtz, Stefan; McDaniel, David R.; Morton, Scott A.

doi:10.2514/6.2007-1053

Cited by 34 publications

(18 citation statements)

References 28 publications

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“…(4). Previous studies to generate training maneuvers for aerodynamic characteristics [41,21,45,43,40] are limited by the range of the motion frequency content. A ROM identified from such a maneuver has limitations with respect to Stability and Control (S&C) applications.…”

Section: Selection Of the Training Maneuversmentioning

confidence: 99%

Computational approximation of nonlinear unsteady aerodynamics using an aerodynamic model hierarchy

Ghoreyshi

Jirasek

Cummings

2013

Aerospace Science and Technology

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, "Computational approximation of nonlinear unsteady aerodynamics using an aerodynamic model hierarchy" (2013 Modeling nonlinear unsteady aerodynamic effects in the simulation of modern fighter aircraft is still a very challenging task. A framework for approximating nonlinear unsteady aerodynamics with a Radial Basis Function neural network is provided. Training data were generated from a hierarchy of aerodynamic models. At the highest level, solutions of the discretized Reynolds-Averaged Navier-Stokes (RANS) equations provide the quantitative and qualitative solution of flow around aircraft, although the results are expensive in terms of computational resources. The Euler simulations are less expensive and provide qualitative data up to moderate angles of attack. The integration of these data is promising for generating accurate aerodynamic models at moderate computational cost. To illustrate the method, an airfoil undergoing pitching and plunging motion is considered. The primary and secondary aerodynamic model data are computed using RANS and Euler equations, respectively. A description for a mapping between the aerodynamic loads and the motion parameters based on the implicit function theorem is described. The mapping is then augmented by adding the secondary data to the input dataset. The selection of training data is then discussed. Once the network is trained, it can compute the unsteady aerodynamic loads from motion descriptions on the order of a few seconds. The framework is examined for different motions, and in all cases, the ROM predictions closely represent the actual aerodynamic responses. It is also demonstrated that the aerodynamic hierarchy aids in the rapid development of a reduced-order model.

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Section: Selection Of the Training Maneuversmentioning

confidence: 99%

Computational approximation of nonlinear unsteady aerodynamics using an aerodynamic model hierarchy

Ghoreyshi

Jirasek

Cummings

2013

Aerospace Science and Technology

View full text Add to dashboard Cite

show abstract

“…10,11,[13][14][15][16][17][18][19][20][21] This was partially to validate that computational techniques have progressed to the point where turbulent Navier-Stokes solvers are now capable of capturing the unsteady nonlinear aerodynamic behavior that leads to the various static and dynamic instabilities mentioned above. However, the cost of this approach was computationally expensive CFD simulations with a slow turn-around time, which severely hindered development.…”

Section: Nomenclaturementioning

confidence: 99%

Improved Methodologies for Maneuver Design of Aircraft Stability and Control Simulations

Jirasek

Jeans

Martenson

et al. 2010

48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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With many modern fighter aircraft experiencing unpredicted flight dynamics during flight tests, recent research has focused on developing methodologies for incorporating computational fluid dynamics into the aircraft development process. The goal of this ap proach is to identify configurations susceptible to stability and control issues early in the design process. Previous research has primarily focused on full aircraft configurations, how ever, to increase the rate of development the current study focused on a two-dimensional NACA0012 airfoil. The two-dimensional NACA0012 airfoil has the advantage of reducing the computational cost by orders of magnitude compared to full scale aircraft simulations, while still providing complicated aerodynamics at high angles of attack. Computationally predicted lift coefficients from a number of newly developed training maneuvers were used to generate reduced order aerodynamic loads models. For evaluation, these models were compared to generated static and dynamic validation data. Methods of improving both the computational training maneuver and the reduced order modeling approach are suggested. Nomenclature

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“…(19). Previous studies to generate training maneuvers for aerodynamic characteristics [72][73][74][75][76] are limited by the range of the motion frequency content. A ROM identified from such a maneuver has limitations with respect to S&C applications.…”

Section: G System Identificationmentioning

confidence: 99%

Transonic Aerodynamic Load Modeling of X-31 Aircraft Pitching Motions

Ghoreyshi¹,

Cummings²,

Ronch³

et al. 2013

AIAA Journal

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The generation of reduced-order models for computing the unsteady and nonlinear aerodynamic loads on an aircraft from pitching motions in the transonic speed range is described. The models considered are based on Duhamel's superposition integral using indicial (step) response functions, Volterra theory using nonlinear kernels, radial basis functions, and a surrogate-based recurrence framework, both using time-history simulations of a training maneuver(s). Results are reported for the X-31 configuration with a sharp leading-edge cranked delta wing geometry, including canard/wing vortex interactions. The validity of the various models studied was assessed by comparison of the model outputs with time-accurate computational-fluid-dynamics simulations of new maneuvers. Overall, the reduced-order models were found to produce accurate results, although a nonlinear model based on indicial functions yielded the best accuracy among all models. This model, along with a time-dependent surrogate approach, helped to produce accurate predictions for a wide range of motions in the transonic speed range. = regression coefficients ε = kriging random process μ = air viscosity; kriging mean value ρ = density, kg∕m 3 σ 2 = covariance τ ij = viscous stress tensor components, Pa Φ i X = radial basis functions ω = circular frequency, rad∕s

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Towards an Efficient Aircraft Stability and Control Analysis Capability Using High-Fidelity CFD

Cited by 34 publications

References 28 publications

Computational approximation of nonlinear unsteady aerodynamics using an aerodynamic model hierarchy

Computational approximation of nonlinear unsteady aerodynamics using an aerodynamic model hierarchy

Improved Methodologies for Maneuver Design of Aircraft Stability and Control Simulations

Transonic Aerodynamic Load Modeling of X-31 Aircraft Pitching Motions

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