Numerous experimental and numerical studies were performed in the past by various authors to reduce the leakage of labyrinth seals and thus increase the performance of turbo machines. Based on the experience of more than 20 years of research activities in this area at the ITS, the authors aim to improve the prediction quality for labyrinth seal performance by combining experimental, numerical and data mining methods. Special emphasis in this work lies on more complex and also worn labyrinth geometries and thus on a more universal optimization tool for labyrinth seals incorporating more realistic engine running conditions as well as wear mechanisms. Better understanding of labyrinth seal behavior based on the new correlations and models will thus lead to optimized geometries and improved designs.

The paper contains the results of experiments to determine the discharge coefficients of different straight-through labyrinth seals with three and five fins and two different fin geometries over a large range of pressure ratios as well as results from a stepped labyrinth seal with six fins in convergent and divergent flow direction. The collected data extends an existing data base of labyrinth seal performance already presented in the paper of Pychynski et al. [1]. This data base is used to create models to calculate labyrinth seal performance depending on up to 25 input parameters. The resulting models will be used as a basis for a universal optimization tool for labyrinth seals.

In the paper the new and versatile test rig for various kinds of labyrinth and gap seals is presented and an analysis of measurement accuracy will be given. The results of a first set of experiments performed with new (i.e. unworn) geometries are compared to experimental data of similar labyrinth geometries from previous investigations, showing an excellent agreement.

The results are then interpreted using Data Mining Methods to identify correlations between different input parameters and the labyrinth seal discharge coefficient. The paper will show that a data based approach can yield similar quality relations as empirical studies but is much less time consuming and more versatile. Several models with different sets of input parameters will be presented and compared as to their applicability in automated geometry optimization using a newly developed optimization tool.

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