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Institute
BTU
Untersuchung des Geräuschverhaltens von Kraftfahrzeug-Schwingungsdämpfern und Elastomerlagern
(2017)
Abstract Automated structural design optimization should take into acc
ount risk of failure which depends on eigenmodes, since eigenmode shap
es determine failure risk by their characteristic stress concentration
pattern, as well as by their specific interaction with excitations. T
hus, such a process needs to be able to identify eigenmodes with low e
rror rate. This is a rather challenging task, because eigenmodes depen
d on the geometry of the structure which is changing during the design
process, and on boundary conditions which are not clearly defined due
to uncertainties in the assembly and running conditions. The present
investigation aims to find a proper classification method for eigenmod
es of compressor airfoils. Specific data normalization and data depend
ent initialization of a neural network using principle-component direc
tions as initial weight vectors have led to the development of a class
ification and decision procedure enabling automatic assignment of prop
er uncertainty bands to eigenfrequencies of a specific eigenmode shape
. Application to compressor airfoils of a stationary gas-turbine with
hammer-foot and dove-tail roots demonstrates the high performance of t
he proposed procedure.
Verbesserte Vorauslegung des Kerntriebwerks mithilfe eines kaskadierten Optimierungsprozesses
(2018)