By Topic

Hybrid model reduction for compressible flow controller design

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xiaoqing Ge ; Dept. of Electr., Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA ; Wen, J.T.

Computational fluid dynamics (CFD) has been a powerful simulation tool to gain insight and understanding of fluid dynamic systems. However, it is also extremely computationally intensive and thus unsuitable for control design and iteration. Various model reduction schemes have been proposed in the past to approximate the Navier-Stokes equation with a low-dimensional model. There are essentially two approaches: input/output model identification and proper orthogonal decomposition (POD). The former captures mostly the local behavior near a steady state and the latter is highly dependent on the snapshots of the flow state used to extract the projection. This paper presents a hybrid model reduction approach that attempts to combine the best features of the two approaches. We first identify an input/output linear model by using the subspace identification method. We next project the difference between CFD response and the identified model response onto a set of POD basis. This trajectory is then fit to a nonlinear dynamical model to augment the input/output linear model. The resulting hybrid model is then used for control design with the controller evaluated with CFD. The proposed methodology has been applied to a 2D compressible flow passing a contraction geometry. The result indicates that near the steady state used for linear system identification, the linear system based design works well. However, far away from the steady state, the hybrid system shows much better performance.

Published in:

Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on

Date of Conference:

12-15 Dec. 2011