EIGR

Real Eigenvalue Extraction Data

Description: Defines data needed to perform real eigenvalue analysis.

Format:

Example:

Field	Definition	Type	Default
SID	Set identification number.	Integer > 0	Required
V1, V2	For vibration analysis: frequency range of interest. For buckling analysis: eigenvalue range of interest.	Real or blank, V1 < V2	See Remark 5
ND	Number of roots desired.	Integer > 0	See Remark 5
SCHECK	Sturm sequence check, one of the following character variables: YES or NO. See Remark 7.	Character	YES
NIVEC	Number of iteration vectors. See Remark 8.	Integer > 0	12
NORM	Method for normalizing eigenvectors, one of the following character variables: MASS, MAX, POINT MASS Normalize to unit value of the generalized mass. Not available for buckling analysis. MAX Normalize to unit value of the largest eigenvector displacement. POINT Normalize the component defined in fields 3 and 4 to a unit value.	Character	MASS - for vibration analysis MAX - for buckling analysis
G	Grid point identification number.	Integer > 0	Required for NORM = POINT
C	Component number of global coordinate.	1 ≤ Integer ≤ 6	Required for NORM = POINT
MAXITER	Maximum number of iterations. See Remark 9.	Integer ≥ 0	0
CTOL	Eigenvalue convergence tolerance.	Real or blank	1.0E-5
ADDITER	Number of additional iterations after convergence. See Remark 10.	Integer ≥ 0	1
ADDIVCV	Number of additional iteration vectors past the number of roots desired or the included range of interest that must also converge. See Remark 10.	Integer ≥ 0	5

Remarks:

1. Real eigenvalue extraction data sets must be selected with the Case Control command METHOD = SID.
2. The units of V1 and V2 are cycles per unit time in vibration analysis, and are eigenvalues in buckling analysis. In buckling, each eigenvalue is the factor by which the prebuckling state of stress is multiplied to produce buckling in the shape defined by the corresponding eigenvector.
3. NORM = MASS is ignored in buckling analysis and NORM = MAX will be applied.
4. Eigenvalues are sorted on order of magnitude for output. An eigenvector is found for each eigenvalue.
5. In vibration analysis, if V1 < 0.0, the negative eigenvalue range will be searched. (Eigenvalues are proportional to Vi squared; therefore, the negative sign would be lost.) This is a means for diagnosing improbable models. In buckling analysis, negative V1 and/or V2 require no special logic.
6. The roots are found simultaneously and sorted in increasing order for each subspace or Lanczos iteration. The number and type of roots to be found can be determined from the following table.

   V1	V2	ND	Number and Type of Roots Found
   V1	V2	ND	Lowest ND roots or all in range, whichever is smaller
   V1	V2	blank	All in range
   V1	blank	ND	Lowest ND roots in range [V1, + ∞ ]
   V1	blank	blank	Lowest root in range [V1, + ∞ ]
   blank	blank	ND	Lowest ND roots in range [- ∞ , + ∞ ]
   blank	blank	blank	Lowest root
   blank	V2	ND	Lowest ND roots below V2
   blank	V2	blank	All below V2

7. SCHECK controls whether a Sturm sequence check is performed. The Sturm sequence check determines if any roots were missed during eigenvalue extraction. Setting SCHECK equal to 0 or NO skips the Sturm sequence check and avoids an additional stiffness matrix factorization thus reducing analysis time. Setting SCHECK equal to 1 or YES performs the check and will output a warning message if any modes were missed.
8. NIVEC specifies the number of additional iteration vectors and is defaulted to 12. Increasing this value may result in a lower number of subspace iterations required but will require more memory and more solves per subspace iteration.
9. MAXITER is used to limit the number of subspace iterations to be performed. The default zero setting forces the eigensolver to iterate until convergence is reached.
10. ADDITER and ADDIVCV are used to prevent missing roots. ADDITER defines the number of additional iterations that will be forced even after all roots desired have converged. ADDIVCV defines how many roots past the desired number or range of interest must converge. A value greater than 1 is recommended when roots are closely spaced. Larger values may result in additional subspace iterations.