- Dowell, Earl H. and Hall, Kenneth C. and Romanowski, Michael C., Reduced order aerodynamic modeling of how to make CFD useful to an aeroelastician,
American Society of Mechanical Engineers, Aerospace Division (Publication) AD, vol. 53-3
pp. 149 - 163 .
(last updated on 2007/04/10)
In this article, we review the status of reduced order modeling of unsteady aerodynamic systems. Reduced order modeling is a conceptually novel and computationally efficient technique for computing unsteady flow about isolated airfoils, wings, and turbomachinery cascades. Starting with either a time domain or frequency domain computational fluid dynamics (CFD) analysis of unsteady aerodynamic or aeroacoustic flows, a large, sparse eigenvalue problem is solved using the Lanczos algorithm. Then, using just a few of the resulting eigenmodes, a Reduced Order Model of the unsteady flow is constructed. With this model, one can rapidly and accurately predict the unsteady aerodynamic response of the system over a wide range of reduced frequencies. Moreover, the eigenmode information provides important insights into the physics of unsteady flows. Finally, the method is particularly well suited for use in the active control of aeroelastic and aeroacoustic phenomena as well as in standard aeroelastic analysis for flutter or gust response. Numerical results presented include: 1) comparison of the reduced order model to classical unsteady incompressible aerodynamic theory, 2) reduced order calculations of compressible unsteady aerodynamics based on the full potential equation, 3) reduced order calculations of unsteady flow about an isolated airfoil based on the Euler equations, and 4) reduced order calculations of unsteady viscous flows associated with cascade stall flutter, 5) flutter analysis using the Reduced Order Model. The presentation will include our most recent results including the use of A-one Orthogonal Decomposition as an alternative or complement to eigenmodes.
Computational fluid dynamics;Mathematical models;Unsteady flow;Wings;Cascades (fluid mechanics);Turbomachinery;Eigenvalues and eigenfunctions;Algorithms;