AEROM: NASA’s Unsteady Aerodynamic and Aeroelastic Reduced-Order Modeling Software

Silva, Walter A.

doi:10.3390/aerospace5020041

Cited by 31 publications

(13 citation statements)

References 27 publications

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“…In this case, time-domain simulations may also be preferred. In Silva (85) , for example, the dynamic information of a linear transonic aeroelastic problem based on modal solutions is obtained by the system identification of CFD simulations.…”

Section: Alternative Approach To Extract Dynamic Informationmentioning

confidence: 99%

“…One option to tackle this problem would be to write the output data as a function of a linear combination of the aircraft linear modes. This was done, for example, by Silva (85) and studies for aeroelastic problems involving small deformations. However, for a VFA, a representation of the deformed equilibrium state with linear modes is no longer adequate.…”

Section: Combining Pod and Sys Id For Stability Analysismentioning

confidence: 99%

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Propeller influence on the aeroelastic stability of High Altitude Long Endurance aircraft

Teixeira

Cesnik

2020

Aeronaut. j.

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This work investigates the propeller’s influence on the stability of High Altitude Long Endurance aircraft, incorporating all resultant loads at the propeller hub, propeller slipstream, and gyroscopic loads. Such effects are usually neglected in the aeroelastic simulation of HALE aircraft. For that goal, a previously developed framework, which couples a geometrically nonlinear structural solver with an Unsteady Vortex Lattice method (uVLM) for lifting surfaces and a Viscous Vortex Particle (VVP) method for propeller slipstream, was employed to generate time-data series. Also, a method, based on a combination of Proper Orthogonal Decomposition and system identification, to extract dynamic information (frequencies, damping, and modes) of the aircraft from a time-series signal is proposed and successfully tested for a purely structural case, for which reference data is available. The method is then applied to investigate the stability of aeroelastic cases. The results demonstrate that the presence of propellers can influence the aeroelastic stability of a Very Flexible Aircraft.

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Section: Alternative Approach To Extract Dynamic Informationmentioning

confidence: 99%

Section: Combining Pod and Sys Id For Stability Analysismentioning

confidence: 99%

Propeller influence on the aeroelastic stability of High Altitude Long Endurance aircraft

Teixeira

Cesnik

2020

Aeronaut. j.

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“…AEROM [24], a suite of tools for generating aeroelastic ROMs, was used to obtain ROM-W. The general procedure was:…”

Section: Rom-wmentioning

confidence: 99%

Active Flutter Suppression Controllers Derived from Linear and Nonlinear Aerodynamics: Application to a Transport Aircraft Model

Waite

Stanford

Bartels

et al. 2018

2018 Applied Aerodynamics Conference

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Active flutter suppression has been demonstrated in simulation by many researchers, generally using methods based on linear aerodynamics and often with simplistic geometries. In this paper, active flutter suppression is demonstrated in a simulation using a Navier-Stokes aerodynamics code, FUN3D, and a realistic transport aircraft configuration. This is accomplished using simple observer-feedback controllers derived from linear aeroelastic models, including reduced order models built via FUN3D data. The development of these reduced order models is described here. It is shown that controllers derived from reduced order models of the nonlinear aerodynamics outperform controllers based on linear aerodynamics.

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“…Even in the context of output-based ROMs, there are many different strategies that can be followed. One can choose from linear state-space identification techniques, [1][2][3] Volterra-based approaches, [4][5][6] nonlinear modeling with artificial neural networks [7][8][9][10][11][12] or Gaussian processes, 13,14 and rational-function approximations, 15,16 for instance. This particular work is focused on reduced-order models generated by artificial neural networks, which are promising for real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Techniques for Improving Neural Network-based Aerodynamics Reduced-order Models

Qingzhao

Medeiros

Cesnik

et al. 2019

AIAA Scitech 2019 Forum

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Neural networks are applied to create reduced-order models (ROMs) for high-fidelity, nonlinear steady and unsteady CFD aerodynamic simulations by non-intrusively relating outputs (dependent variables) to inputs (independent variables), regardless of the complex physics involved. The present study is conducted with increasing complexity in the aerodynamic system and the corresponding neural network-based ROMs. The primary goal of this paper is to introduce the development and demonstration of new techniques for improving the predictive performance of the reduced-order models. In particular, the benefits of a physics-driven approach for selecting the training inputs and delay states are investigated. The adaptive ROM system is established by performing error estimation through two statistical techniques: cross-validation and bootstrapping.

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AEROM: NASA’s Unsteady Aerodynamic and Aeroelastic Reduced-Order Modeling Software

Cited by 31 publications

References 27 publications

Propeller influence on the aeroelastic stability of High Altitude Long Endurance aircraft

Propeller influence on the aeroelastic stability of High Altitude Long Endurance aircraft

Active Flutter Suppression Controllers Derived from Linear and Nonlinear Aerodynamics: Application to a Transport Aircraft Model

Techniques for Improving Neural Network-based Aerodynamics Reduced-order Models

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