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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.
For many countries, gasturbine technology is one of the key technologies for the reduction of climate-damaging pollutant emissions. The profitability of such facilities, however, is highly dependent on the price for the utilized fossil fuel, which is why there is a constant need for increased efficiency. The potential of increasing the efficiency of the individual components is basically limited by factors which will reduce operating life. The goal of this thesis is to develop methods for improved automated structural design optimization, which shall be developed on the basis of compressor airfoils. Special attention is payed to avoid the excitation of failure critical eigenmodes by detecting them automatically. This is achieved by introducing a method based on self-organizing neural networks which enables the projection of eigenmodes of arbitrary airfoil geometries onto standard surfaces, thereby making them comparable. Another neural network is applied to identify eigenmodes which have been defined as critical for operating life. The failure rate of such classifiers is significantly reduced by introducing a newly developed initialization method based on principle components. A structural optimization is set up which shifts the eigenfrequency bands of critical modes in such a way that the risk of resonance with engine orders is minimized. In order to ensure practical relevance of optimization results, the structural optimization is coupled with an aerodynamic optimization in a combined process. Conformity between the loaded hot-geometry utilized by the aerodynamic design assessment and the unloaded cold-geometry utilized by the structural design assessment is ensured by using loaded-to-unloaded geometry transformation. Therefor an innovative method is introduced which, other than the established time-consuming iterative approach, uses negative density for a direct transformation taking only a few seconds, hence, making it applicable to optimization. Additionally, in order for the optimal designs to be robust against manufacturing variations, a method is developed which allows to assess the maximum production tolerance of a design from which onwards possible design variations are likely to violate design constraints. In contrast to the usually applied failure rate, the production tolerance is a valid requirement for suppliers w.r.t. expensive parts produced in low-quantity, and therefore is a more suitable optimization objective.