The subject of this paper is a flow-adaptive measurement grid algorithm developed for 1D and 2D flow field surveys with pneumatic probes in turbomachinery flows. The algorithm automatically determines the distribution and the amount of measurement points needed for an approximation of the pressure distribution within a predefined accuracy.

The algorithm is based on transient traverses, conducted back and forth in the circumferential direction. The dynamic response of the pressure-measuring system is disregarded during the traverses, which serve to detect changes in the pressure field. In contrast to previous investigations by the authors, a correction of the dynamic response is applied by deconvolving the transient measurement data using the information embedded in both transient measurements. In consequence, the performance of the algorithm is — to a large extent — independent of the transient traversing speed and the geometry of the pressure-measuring system. Insertion and removal strategies are incorporated in order to reduce measurement points and increase robustness towards differing flow field conditions. By approximation of the pressure distribution, the flow-adaptive measurement data is suited for the application of post-processing corrections without any constraints. The performance of the algorithm is demonstrated for 2D flow field surveys with a pneumatic 5-hole probe in an annular cascade wind tunnel. Compared to conventional techniques for data sampling, e.g., uniform measurement grids, the measurement grid points are automatically adjusted so that a consistent resolution of the flow features is achieved within the measurement domain. Furthermore, the application of the algorithm shows a significant reduction in the number of measurement points. Compared to the measurement duration based on uniform grids, the duration is reduced by at least 7%, while maintaining a high accuracy of the measurement.

The purpose of this paper is to demonstrate the performance of measurement grids adapted to local flow field conditions. Consequently, valuable measurement time can be saved without a loss in quality of the data obtained.

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