D
Daniella E. Raveh
Researcher at Technion – Israel Institute of Technology
Publications - 115
Citations - 1996
Daniella E. Raveh is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Aeroelasticity & Flutter. The author has an hindex of 23, co-authored 95 publications receiving 1591 citations. Previous affiliations of Daniella E. Raveh include Georgia Institute of Technology.
Papers
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Reduced-Order Models for Nonlinear Unsteady Aerodynamics
TL;DR: In this paper, two reduced-order models (ROMs) for the evaluation of nonlinear aerodynamic forces based on CFD computations are presented, one based on the Volterra theory for nonlinear systems, and a new ROM that is based on step (indicial) responses.
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Reynolds-Averaged Navier-Stokes Study of the Shock-Buffet Instability Mechanism
TL;DR: In this article, a study of the shock buffet onset and instability mechanism via Reynolds-averaged Navier-Stokes simulations on several airfoils is presented, and the numerical setup and the AUmaras turbulence closure are validated based on wind-tunnel data from NACA 0012 and RA16SC1.
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Numerical Study of Shock Buffet on Three-Dimensional Wings
TL;DR: In this paper, the effects of three-dimensional flow, wing sweep, and span length on the shock-buffet characteristics were identified, and numerical validation was presented for OAT15A and RA16SC1 swept wings based on wind-tunnel experiments.
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Numerical Simulation and Reduced-Order Modeling of Airfoil Gust Response
Avi Zaide,Daniella E. Raveh +1 more
TL;DR: In this paper, a reduced-order model is used to predict the lift and pitching moment histories accurately throughout the subsonic and transonic regimes of an airfoil to arbitrary shaped gust inputs.
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Identification of computational-fluid-dynamics based unsteady aerodynamic models for aeroelastic analysis
TL;DR: Three approaches for reduced-order modeling of computational-fluid-dynamics-(CFD) based unsteady aerodynamics, employing system-identification methods, are presented, and used for generation of three models: a frequency-domain model, a time-domain autoregressive-moving-average model, and a discrete-time state-space model.