Order reduction is an efficient means to enable a system-level simulation.
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| Eurosime course on MOR for ANSYS |
New book Fast Simulation of Electro-Thermal MEMS: Efficient Dynamic Compact Models. Book at Amazon or at Springer.
Order reduction is an efficient means to enable a system-level simulation.
MOR for ANSYS = Model Order Reduction for ANSYS
Suggested pronunciation is "more for ANSYS".
MOR for ANSYS speeds up transient or harmonic simulation significantly.
MOR for ANSYS functions with the traditional ANSYS Inc. solver products such as ANSYS Structural, ANSYS Mechanical, ANSYS Multiphysics and the ANSYS University versions of ANSYS Multiphysics. Note that MOR for ANSYS does not function with the ANSYS Workbench environment, ANSYS CFX or ANSYS ICEM CFD products.
Content:
MOR for ANSYS is a command-line tool built on top of the ANSYS-supplied library to read ANSYS binary files [1]. The information from ANSYS is read from FULL and EMAT files.
The block Arnoldi algorithm [2] for a first order problem.
Three methods to treat a second-order system:
Efficient linear algebra based on:
MOR for ANSYS can write the original high-dimensional system from ANSYS in the Matrix Market format. You can find matrices generated by MOR for ANSYS at http://www.imtek.uni-freiburg.de/simulation/benchmark/.
MOR for ANSYS is written in C++.
Quick overview: "mor4ansys: Model Reduction for ANSYS Engineering Models".
More detail: "Model Order
Reduction for Large Scale Engineering Models Developed in ANSYS" and
"Model Order Reduction of
MEMS for Efficient Computer Aided Design and System Simulation".
Engineering introduction to model reduction:
"Review: Automatic Model
Reduction for Transient Simulation of MEMS-based Devices".
Step-by-step computational lab to learn MOR for ANSYS. You compare computational time as well as accuracy for transient simulation of a full-scale thermal model in ANSYS and a reduced model obtained by MOR for ANSYS. Download, Browse, Manual.
Step-by-step computational lab to perform model reduction in the case of harmonic pre-stressed simulation of a electromechanical model. Download, Browse, Manual.
Tutorial on parametric model reduction (preserving film coefficients in the symbolic form). Download, Browse, Manual.
MOR for ANSYS 2.5 is distributed through CADFEM.
Software Post4MOR for Mathematica to make simulation with a reduced model. Download, Browse.
Pascal Maglie's Matlab scripts to read a reduced model to Simulink and compare simulation results with ANSYS resutls: Download, Browse.
Evgenii B. Rudnyi.
Tel. (+49 8092) 7005 82
E. B. Rudnyi and J. G. Korvink.
Model Order Reduction for Large Scale Engineering Models Developed in
ANSYS.
Lecture Notes in Computer Science, v. 3732, pp. 349-356, 2006.
Title: Applied Parallel Computing. State of the Art in Scientific Computing: 7th International Conference, PARA 2004, Lyngby, Denmark, June 20-23, 2004. Revised Selected Papers.
Final paper at Springer.
A discussion group where you can post questions is at http://groups.google.com/group/mor4ansys. You can subscribe on-line or by sending a dummy e-mail to mor4ansys-subscribe@googlegroups.com.
[1] Guide to interfacing with Ansys, ANSYS
Inc.
One can also access ANSYS binary files from within Mathematica with my Mathlink application.
[2] R. W. Freund,
Krylov-subspace methods for reduced-order modeling in circuit simulation,
Journal of Computational and Applied Mathematics, Vol. 123, pp. 395-421,
2000.
See also http://cm.bell-labs.com/cm/cs/doc/nam.html.
[3] E. B. Rudnyi, J. Lienemann, A. Greiner,
and J. G. Korvink,
mor4ansys: Generating Compact
Models Directly from ANSYS Models.
Technical Proceedings of the 2004 Nanotechnology Conference and Trade Show,
Nanotech 2004, March 7-11, 2004, Boston, Massachusetts, USA, vol. 2, p.
279-282.
[4] J. S. Han, E. B. Rudnyi, J. G. Korvink.
Efficient optimization of transient dynamic problems in MEMS devices using
model order reduction.
Journal of Micromechanics and Microengineering, 2005, v. 15, N 4, p. 822-832.
Paper at IOP.
[5] Z. J. Bai, K. Meerbergen, Y. F. Su.
Arnoldi methods for structure-preserving dimension reduction of second-order
dynamical systems.
In: P. Benner, V. Mehrmann, D. Sorensen (eds), Dimension Reduction
of Large-Scale Systems, Lecture Notes in Computational Science and
Engineering. Springer-Verlag, Berlin/Heidelberg, Germany, v. 45, p.
173-189, 2005.
Book at Springer,
see also http://www.cs.ucdavis.edu/~bai/newpubs.html.
[6] B. Salimbahrami,
Structure Preserving Order Reduction of Large Scale Second Order Models,
Technische Universitaet Muenchen, PhD Thesis, 2005.
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