The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES
Transactions ID Number: 19-917
Full Name: Georg Fuchs
Position: Ph.D. Candidate
Age: ON
Sex: Male
Address: Wiedner Hauptstrasse 8-10/E325, 1040 Vienna, Austria
Country: AUSTRIA
Tel: +43 (1) 58801 - 328 13
Tel prefix: +43 1
Fax: +43 1 58801 30399
E-mail address: georg.fuchs@tuwien.ac.at
Other E-mails: georg.fuchs83@gmail.com
Title of the Paper: Local Jacobian based Galerkin Order Reduction for the Approximation of Large-Scale Nonlinear Dynamical Systems
Authors as they appear in the Paper: Georg Fuchs, Alois Steindl, Stefan Jakubek
Email addresses of all the authors: georg.fuchs@tuwien.ac.at,alois.steindl@tuwien.ac.at,stefan.jakubek@tuwien.ac.at
Number of paper pages: 10
Abstract: In automotive applications large-scale nonlinear dynamical models are utilized for hardware-in-the-loop simulations and model-based controller design. A projection-based order reduction of these models, on the one hand, yields substantial advantages in computational speed and on the other hand, simplifies the controller design procedure. In this work a mathematical-empirical approach is chosen for the order reduction of a real-time diesel engine model. It is based on recorded time-snapshots for typical system excitations. Flat and nonlinear Galerkin approximations are obtained by projection onto a lower-dimensional sub-space. In the nonlinear Galerkin approach a novel scheme for the reconstruction of the omitted states is introduced. It makes use of the local model parameters in the local Jacobian matrix, obtained from a linearization of the complete nonlinear model for various points of a local model network. The results from the application of the reduction meth!
ods to the engine model are presented and discussed for different reduced model orders and the benefits of the iteration scheme are demonstrated.
Keywords: Diesel engine modeling, Model order reduction, Singular value decomposition, Snapshot method, Galerkin methods, Local model network
EXTENSION of the file: .pdf
Special (Invited) Session: Order Reduction for a Realtime Engine Model Using Flat and Nonlinear Galerkin Methods
Organizer of the Session: 104-243
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