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Thermal stresses influence behavior of a mechanical structure. Provided that in addition to small deformations one assumes the temperate independent secant coefficient of thermal expansion, linear model reduction should work for a coupled thermomechanical problem. Model reduction for a thermomechanical ANSYS model with inhomogeneous temperature distribution has been perfromed in a paper listed below.

In our view, possible applications of model reduction for thermomechanical models can be related to the development of ultraprecision machines. Design of such machines is impossible without real-time error compensation for many side effects, among which deformation due to inevitable thermal gradients plays the central role. Modern trend to develop appropriate control systems includes simulation of finite element thermomechanical models. Model reduction can reduce the dimension of finite element models and develop compact accurate thermomechanical models for system-level simulation automatically.

*E. Zukowski, J. Wilde, E. B. Rudnyi, J. G. Korvink.*

Model Reduction for Thermo-Mechanical Simulation of Packages.

THERMINIC 2005, 11th International Workshop on Thermal Investigations of ICs
and Systems, 27 - 30 September 2005, Belgirate, Lake Maggiore, Italy,
p. 134 - 138.

Model reduction is a new numerical technique that allows us to obtain an accurate low-dimensional representation of high-dimensional finite elements models. Additionally, in the case of linear models the time to perform model reduction is comparable to solution of a stationary problem and hence model reduction can be employed as a fast solver for a transient or harmonic problem during the optimization process. The goal of the paper is to explore new possibilities and develop a methodology to apply model reduction during design of new packages. The described approach is limited ourselves to thermo-mechanical finite element models developed in ANSYS.

Evgenii B. Rudnyi, CADFEM

My e-mail is erudnyi at cadfem point de. Phone is +49 8092 7005 82.

Designed by

Masha Rudnaya