The influence of blade stacking on turbine losses
Three linear cascades of highly loaded, low aspect ratio turbine blades have been tested in detail to investigate the mechanisms of secondary loss and the influence of non-radial stacking (lean) on these mechanisms. The blades in all three cascades had the same section but they were stacked perpendicular to the endwall in the first cascade, on a straight line inclined at 20° from perpendicular in the second and on a circular arc inclined at 30° from perpendicular at each end in the third cascade. The boundary layer over much of the end wall within the blade passage was found to be laminar. Despite this, skin friction coefficients and rates of loss generation are still very high because this boundary layer is extremely thin. Downstream mixing losses comprised a significant proportion of the overall loss. They arise mainly as a result of the dissipation of the kinetic energy of the secondary flow. If mixing is not complete before the subsequent bladerow is reached, acceleration in that bladerow is likely to increase the mixing loss. Lean has a marked effect upon blade loading and the state of boundary layers on the blade suction surfaces and the endwalls. The distribution of loss generation is therefore changed but the effect upon overall loss coefficient is minimal for this blade. The major benefit of compound lean is a reduction in the strength of secondary flows. The reasons for this are discussed. This reduces the downstream mixing losses and would also substantially reduce unsteadiness and spanwise variations of mean incidence at entry to the subsequent bladerow. In a turbine this would be likely to reduce losses in the downstream bladerow as well as making matching easier and improving off-design performance. It seems that the details of the separation of the inlet endwall boundary layer and formation of the horseshoe vortex have a strong influence on the subsequent development of the secondary flow. Numerical calculations must model this correctly in order to obtain good overall predictions.
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