Abstract

The detrimental effects of surface roughness on turbulent boundary layers in turbines, and compressors, are well known. Prediction can be problematic, especially for surfaces which are not similar to sand grains. Several publications have proposed additional parameters, slope, skew, etc. to augment a wallnormal measure. Here, we introduce a new roughness parameter, the mean separation, which explicitly measures the average separation between each local minima and its closest local maxima. Scans of turbine blade surfaces have mean separations which are up to six times that of the sand grain surface with the same wallnormal measure. High resolution, < 0.1 μm, area scanning tools and 3-D printing techniques have enabled the development of a “Scan-Scale-Print-Measure” methodology. A surface scan is scaled-up, 3-D printed, applied to a flat-plate and then the turbulent boundary layer is measured. The methodology has enabled parametric studies where the mean separation was increased whilst keeping either the wallnormal or the feature size constant. Both studies demonstrated a surface roughness-loss that was strongly dependent on the mean separation and different to a sand grain correlation. It also showed that for large mean separations the roughness-loss decreased to zero. A study into pits-and-peaks showed that, at the same wallnormal, peaks generated a roughness-loss twice that of pits. An engine representative surface produced only half the roughness-loss that would be attributed to a sand grain surface. The roughness loss was estimated within 5% using the parametric studies.

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