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Automated eigenmode classification for airfoils in the presence of fixation uncertainties

  • 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 aAbstract 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.show moreshow less

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Metadaten
Author: Ivo Martin, Dieter BestleGND
URL:https://www.sciencedirect.com/science/article/pii/S0952197617302361
DOI:https://doi.org/https://doi.org/10.1016/j.engappai.2017.09.022
ISSN:0952-1976
Title of the source (English):Engineering Applications of Artificial Intelligence
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2018
Tag:Principle-component analysis
Volume/Year:67
First Page:187
Last Page:196
Faculty/Chair:Fakultät 3 Maschinenbau, Elektro- und Energiesysteme / FG Technische Mechanik und Fahrzeugdynamik
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