An eigenvalue buckling analysis is generally used to estimate the critical
buckling loads of stiff structures (classical eigenvalue buckling). This type
of analysis is a linear perturbation procedure, and buckling loads are
calculated relative to the base state of the structure. For more information,
see
Eigenvalue buckling prediction.
Display the Edit Step dialog box following the
procedure outlined in
Creating a step
(Procedure type:Linear perturbation;
Buckle), or
Editing a step.
On the Basic tabbed page, configure settings such
as eigensolver extraction method and maximum number of iterations as described
in the following procedures.
The Other tabbed page displays the default
matrix storage option. You cannot change this setting because
Abaqus/Standard
provides eigenvalue extraction for only symmetric matrices. In eigenvalue
buckling prediction procedures
Abaqus/Standard
symmetrizes all contributions to the stiffness matrix.
Configure settings on the Basic tabbed page
In the Edit Step dialog box, display the
Basic tabbed page.
In the Description field, enter a short
description of the analysis step.
Abaqus
stores the text that you enter in the output database, and the text is
displayed in the state block by the Visualization module.
Choose either the Lanczos eigensolver or the
Subspace iteration eigensolver.
The Lanczos method is generally faster when a large number of
eigenmodes is required for a system with many degrees of freedom. However, this
method does have some limitations (see the warning at the bottom of the
Basic tabbed page). The subspace iteration method may be
faster when only a few (less than 20) eigenmodes are needed. For more
information, see
Selecting the eigenvalue extraction method.
In the Number of eigenvalues requested field,
enter the number of eigenvalues that you want to be estimated. Significant
overestimation of the actual number of eigenvalues can create very large files.
If you underestimate the actual number of eigenvalues,
Abaqus/Standard
will issue a corresponding warning message.
If you selected the Lanczos eigensolver, do the
following:
Toggle on Minimum eigenvalue of interest to
enter a lower limit to the range of eigenvalues
Abaqus/Standard
will extract. If you toggle on this option, enter the value in the field
provided.
Toggle on Maximum eigenvalue of interest to
enter an upper limit to the range of eigenvalues
Abaqus/Standard
will extract. If you toggle on this option, enter the value in the field
provided.
If you specify a range of eigenvalues,
Abaqus/Standard
will extract eigenvalues until either the requested number of eigenvalues has
been extracted in the given range or all the eigenvalues in the given range
have been extracted.
Choose a Block size option:
Choose Default to use the default block
size of 7, which is usually appropriate.
Choose Value to enter a particular block
size in the field provided. In general, the block size for the Lanczos method
should be as large as the largest expected multiplicity of eigenvalues.
Specify your preference for the Maximum number of block
Lanczos steps:
Choose Default to allow
Abaqus/Standard
to determine the number of block Lanczos steps within each Lanczos run. The
default value is usually appropriate.
Choose Value to enter a limit to the
number of Lanczos steps within each Lanczos run. In general, if a particular
type of eigenproblem converges slowly, providing more block Lanczos steps will
reduce the analysis cost. On the other hand, if you know that a particular type
of problem converges quickly, providing fewer block Lanczos steps will reduce
the amount of in-core memory used.
If you selected the Subspace eigensolver, do the
following:
Toggle on Maximum eigenvalue of interest to
enter an upper limit to the range of eigenvalues
Abaqus/Standard
will extract. If you toggle on this option, enter the value in the field
provided.
Abaqus/Standard
will extract eigenvalues until either the requested number of eigenvalues has
been extracted or the last eigenvalue extracted exceeds the maximum eigenvalue
of interest.
Enter a value for the number of Vectors used per
iteration. In general, the convergence is more rapid with more
vectors, but the memory requirement is also larger. Thus, if you know that a
particular type of eigenproblem converges slowly, providing more vectors by
using this option might reduce the analysis cost.
Enter a value for the Maximum number of
iterations. The default is 30.
Click OK to save the step and to close the
Edit Step dialog box.