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2013.35: Detecting the Causes of Ill-Conditioning in Structural Finite Element Models

2013.35: Ramaseshan Kannan, Stephen Hendry, Nicholas J. Higham and Francoise Tisseur (2013) Detecting the Causes of Ill-Conditioning in Structural Finite Element Models.

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Abstract

In 2011, version 8.6 of the finite element-based structural analysis package Oasys GSA was released. A new feature in this release was the estimation of the $1$-norm condition number $\kappa_1(K)={\|K\|}_1{\|K^{-1}\|}_1$ of the stiffness matrix $K$ of structural models by using a $1$-norm estimation algorithm of Higham and Tisseur to estimate \(\|K^{-1}\|_1 \). The condition estimate is reported as part of the information provided to engineers when they carry out linear/static analysis of models and a warning is raised if the condition number is found to be large. The inclusion of this feature prompted queries from users asking how the condition number impacted the analysis and, in cases where the software displayed an ill conditioning warning, how the ill conditioning could be ``fixed''. We describe a method that we have developed and implemented in the software that enables engineers to detect sources of ill conditioning in their models and rectify them. We give the theoretical background and illustrate our discussion with real-life examples of structural models to which this tool has been applied and found useful. Typically, condition numbers of stiffness matrices reduce from $O(10^{16})$ for erroneous models to $O(10^8)$ or less for the corrected model.

Item Type:MIMS Preprint
Subjects:MSC 2000 > 15 Linear and multilinear algebra; matrix theory
MSC 2000 > 65 Numerical analysis
MIMS number:2013.35
Deposited By:Dr Françoise Tisseur
Deposited On:10 July 2013

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