Structure-preserving model reduction for mechanical systems

نویسندگان

  • Sanjay Lall
  • Petr Krysl
  • Jerrold E. Marsden
چکیده

This paper focuses on methods of constructing of reduced-order models of mechanical systems which preserve the Lagrangian structure of the original system. These methods may be used in combination with standard spatial decomposition methods, such as the Karhunen–Loève expansion, balancing, and wavelet decompositions. The model reduction procedure is implemented for three-dimensional finite-element models of elasticity, and we show that using the standard Newmark implicit integrator, significant savings are obtained in the computational costs of simulation. In particular simulation of the reduced model scales linearly in the number of degrees of freedom, and parallelizes well. © 2003 Elsevier B.V. All rights reserved.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Semantic Preserving Data Reduction using Artificial Immune Systems

Artificial Immune Systems (AIS) can be defined as soft computing systems inspired by immune system of vertebrates. Immune system is an adaptive pattern recognition system. AIS have been used in pattern recognition, machine learning, optimization and clustering. Feature reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encoun...

متن کامل

Efficient structure-preserving model reduction for nonlinear mechanical systems with application to structural dynamics

This work proposes a model-reduction methodology that both preserves Lagrangian structure and leads to computationally inexpensive models, even in the presence of high-order nonlinearities. We focus on parameterized simple mechanical systems under Rayleigh damping and external forces, as structural-dynamics models often fit this description. The proposed model-reduction methodology directly app...

متن کامل

Preserving Lagrangian structure in nonlinear model reduction with application to structural dynamics

This work proposes a model-reduction methodology that preserves Lagrangian structure (equivalently Hamiltonian structure) and achieves computational efficiency in the presence of high-order nonlinearities and arbitrary parameter dependence. As such, the resulting reduced-order model retains key properties such as energy conservation and symplectic timeevolution maps. We focus on parameterized s...

متن کامل

Structure-Preserving Model Reduction

A general framework for structure-preserving model reduction by Krylov subspace projection methods is developed. The goal is to preserve any substructures of importance in the matrices L, G, C, B that define the model prescribed by transfer function H(s) = L∗(G+sC)−1B. Many existing structure-preserving model-order reduction methods for linear and second-order dynamical systems can be derived u...

متن کامل

Stable model reduction: A Projection Approach for NONLiNeAr Systems in the more General Descriptor Form

The ability to generate accurate reduced-order models (ROMs) of nonlinear dynamical systems, such as analog circuits and micro-electro-mechanical systems (MEMS), is a crucial first step in the automatic design and optimization of such systems. One popular approach to model order reduction (MOR) of highly nonlinear systems employs trajectory-based methods, such as the piecewise-linear (PWL) appr...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2003