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the data point to the left of the minimum-loss point. This is because the author made a logical deduction that the minimum-loss point lies between these two data points and closer to the left point. This hypothesis was supported by experimental data for the 90% speed line, to be shown in Figure 4.5. It was also supported by the correlations which report better qualitative trends on the stall side. In Figure 4.4, the Bloch model performed best among the models tested for the stalled regime, with an RMS error of only .0061. This result was followed closely by the Hearsey model and the Cetin correlation with RMS errors of only 0.0079 and 0.0082, respectively. Creveling showed the worst agreement with a RMS error of 0.039. Also in Figure 4.4, none of the correlations tested could predict accurately the losses in the choked regime. The Bloch model formed a vertical line when the cascade choked. Therefore, the Bloch model could not predict loss for incidences less than the minimum-loss incidence. To enable the Bloch model to predict loss in this choked regime, the author added a negatively sloped line which was a linear fit of Rotor 1-B data to the Bloch predicted minimum-loss point. This modification showed the best results, with a RMS error of 0.018. The next best correlation prediction was Cetin, with an RMS error of over 0.06. This magnitude of error in loss prediction would result in approximately a 20% error in total pressure rise in the absolute frame of reference. Figure 4.5 shows the graph of the off-minimum-loss correlations centered at the experimentally measured minimum-loss point for the Rotor 1-B 90% speed line. The range of relative inlet Mach numbers for this speed was 1.00 to 1.06. The experimentally measured loss data at this speed appears to be very predictable. 41PDF Image | Gas Turbine Design Axial Flow Compressors
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