
STRATEGIES FOR DEALING WITH LARGE-ORDER MODELS
The capabilities of computer resources and commercial finite element software have
continually increased making very large-order (∼10
6
degrees-of-freedom or more)
finite element models a practical reality. A variety of numerical analysis strategies
have been introduced to efficiently deal with these large-order models.
In 1965, what is popularly known as the Guyan reduction method
15
was intro-
duced. This method employs a static reduction transformation based on the model
stiffness matrix to consistently reduce the mass matrix. By subdividing the model
displacements into analysis (a) and omitted (o) subsets, the static reduction trans-
formation is
=
{u
a
} (28.109)
By applying this transformation to the dynamic system, an approximate reduced
dynamic system for modal analysis is defined as
[M
aa
]{ü
a
} + [K
aa
]{u
a
} = {0} (28.110)
where
[M
aa
] =
T
[K
aa
] =
T
(28.111)
The reduced approximate mass and stiffness matrices are generally fully populated,
in spite of the fact that the original system matrices are typically quite sparse. The
effective selection of an appropriate analysis set, {u
a
}, is a process requiring good
physical intuition. A recently introduced two-step procedure
16
automatically iden-
tifies an appropriate analysis set. The Guyan reduction method is no longer a
favored strategy for dealing with large-order dynamic systems due to the develop-
ment of powerful numerical procedures for very large-order sparse dynamic sys-
tems. It continues to be employed, however, for the definition of test-analysis
models (TAMs) which are used for modal test planning and test-analysis correla-
tion analyses (see Chap. 41). Numerical procedures, which are currently favored for
dealing with modern large-order dynamic system modal (eigenvalue) analyses, are
(1) the Lanczos method
17
(refined and implemented by many other developers)
and (2) subspace iteration.
8
Segmentation of Large-Order Dynamic Systems. Many dynamic systems,
such as aircraft, launch vehicle–payload assemblies, spacecraft, and automobiles,
naturally lend themselves to substructure segmentation (see Fig. 28.9). Numerical
analysis strategies, which exploit substructure segmentation, were originally intro-
duced to improve the computational efficiency of large-order dynamic system analy-
sis. However, advances in numerical analysis of very large-order dynamic systems
have reduced the need for substructure segmentation. The enduring utilization of
substructure segmentation, especially in the aerospace industry, is a result of the fact
that substructure models provide cooperating organizations with a standard means
for sharing and integrating subsystem data. It should also be noted that some
research efforts in the area of parallel processing are utilizing mature substructure
I
aa
−K
−1
oo
K
oa
K
ao
K
oo
K
aa,o
K
oa
I
aa
−K
−1
oo
K
oa
I
aa
−K
−1
oo
K
oa
M
ao
M
oo
M
aa,o
M
oa
I
aa
−K
−1
oo
K
oa
I
aa
−K
−1
oo
K
oa
u
a
u
o
FINITE ELEMENT MODELS 28.45
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