This paper discusses a method for the identification and application of reduced-order models based on linear and nonlinear aerodynamic impulse responses. The Volterra theory of nonlinear systems and an appropriate kernel identification technique are described. Insight into the nature of kernels is provided by applying the method to the nonlinear Riccati equation in a non-aerodynamic application. The method is then applied to a nonlinear aerodynamic model of an RAE 2822 supercritical airfoil undergoing plunge motions using the CFL3D Navier-Stokes flow solver with the Spalart-Allmaras turbulence model. Results demonstrate the computational efficiency of the technique
The presentation begins with a brief description of the motivation and approach that has been taken ...
In this work, a nonlinear state-space-based identification method is proposed to describe compactly ...
In this work, a nonlinear state-space-based identification method is proposed to describe compactly ...
This paper discusses a method for the identification and application of reduced-order models based o...
This paper discusses the mathematical existence and the numerically-correct identification of linear...
This paper discusses the mathematical existence and the numerically-correct identification of linear...
This dissertation discusses the mathematical existence and the numerical identification of linear an...
This dissertation discusses the mathematical existence and the numerical identification of linear an...
Reduced order models are needed for reliable, efficient and accurate prediction of aerodynamic force...
In computational aeroelasticity, unsteady aerodynamics is computationally expensive compared to the ...
The theory of Volterra integral series for nonlinear systems is applied to the prediction of the aer...
Recent advances and challenges in the generation of reduced order aerodynamic models using computati...
In this work, a nonlinear state-space-based identification method is proposed to describe compactly ...
Volterra series is one of the powerful system identification methods for representing the nonlinear ...
In this work, a nonlinear state-space-based identification method is proposed to describe compactly ...
The presentation begins with a brief description of the motivation and approach that has been taken ...
In this work, a nonlinear state-space-based identification method is proposed to describe compactly ...
In this work, a nonlinear state-space-based identification method is proposed to describe compactly ...
This paper discusses a method for the identification and application of reduced-order models based o...
This paper discusses the mathematical existence and the numerically-correct identification of linear...
This paper discusses the mathematical existence and the numerically-correct identification of linear...
This dissertation discusses the mathematical existence and the numerical identification of linear an...
This dissertation discusses the mathematical existence and the numerical identification of linear an...
Reduced order models are needed for reliable, efficient and accurate prediction of aerodynamic force...
In computational aeroelasticity, unsteady aerodynamics is computationally expensive compared to the ...
The theory of Volterra integral series for nonlinear systems is applied to the prediction of the aer...
Recent advances and challenges in the generation of reduced order aerodynamic models using computati...
In this work, a nonlinear state-space-based identification method is proposed to describe compactly ...
Volterra series is one of the powerful system identification methods for representing the nonlinear ...
In this work, a nonlinear state-space-based identification method is proposed to describe compactly ...
The presentation begins with a brief description of the motivation and approach that has been taken ...
In this work, a nonlinear state-space-based identification method is proposed to describe compactly ...
In this work, a nonlinear state-space-based identification method is proposed to describe compactly ...