Talk Abstract:
Uncertainty in Mode Shape Data and its Influence on the Comparison
of Test and Analysis Models
Robert
V. Lust and John
A. Cafeo
Vehicle Analysis and Dynamics Lab
General Motors Research & Development and Planning
Warren, Michigan
Bob_Lust@notes.gmr.com
john_cafeo@gmr.com
In the design and development of automobiles, laboratory modal
tests are typically used to improve and verify finite element
analysis models that support design decisions. We know from
experience that there is variation and uncertainty associated
with both the test and the simulation results. This variation
(or uncertainty) complicates the comparison of test and simulation
results and, if not considered, can result in unnecessary attempts
to reconcile the differences between the test and analysis models.
It can also lead to erroneous design decisions.
In this paper we discuss a general view of variation and uncertainty
as it relates to finite element analysis model correlation and
reconciliation. Within this framework, and as a part of an experiment
designed to quantify test-to-test variability in the modal analysis
of automobiles, we propose a generic model for uncertainty in
the components of experimental mode shapes. The uncertainty
in the mode shape components is characterized in terms of a
probability distribution and its associated parameters. After
developing the uncertainty model we demonstrate its utility
through an application to a typical test/analysis data correlation
exercise. Specifically, we demonstrate and discuss the use the
uncertainty model as a basis for comparing test and analysis
mode shapes using the Modal Assurance Criteria (MAC).
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