However average cn values of 15 still far exceed

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Unformatted text preview: eviations occur with positive 9 yaw error, where the tower shadow and yaw error effects on α are complementary in φ. At 80% span (Fig. 13), the changes in CN with V∞ and ϕ are less pronounced than those at 30% span. However, average CN values of 1.5 still far exceed quasi-static wind tunnel performance data. Deviation in normal force is also less prominent than for the 30% span location, but still of significant magnitude. As at 30%, the standard deviation distribution is asymmetrical with respect to yaw error, with the maximum occurring at 25° yaw error. CN mean values respond differently for the two blade geometries. For the untwisted blade, the entire CN response centroid is shifted to higher wind speeds at increasing span locations (not shown). This effect appears to simply reflect the decrease in α with increasing span for any wind speed. Equivalent angles of attack are achieved with higher wind speeds. A strong similarity exists in both cycle averaged and dynamic response at incremental span locations of 47% and 63% (not shown) with peak magnitudes diminishing slightly (≈10%) at 80% span. In contrast, CN mean values for the twisted blade do not shift. At low wind speeds, CN contours are quite similar across all span locations, reflecting the uniformity in αi at the designed wind speed. Increasing wind speed broadens the bands at any span location but does not shift the overall response curve. Again, this is consistent with the nonuniform increase in α with higher wind speeds over the span. Dynamic activity, as measured by the CN standard deviation, decreases with increasing span for both geometries. However, twisted blade dynamic activity is much greater. Blade root CM data obtained from strain gage measurements are plotted as CM means and standard deviations for both the untwisted and twisted blade geometries (Figs. 14 and 15). Standard deviation of root flap bending is greatest at increased V∞ for both positive and negative values of ϕ. This unsteady behavior correlates best with the high incidence of cycles where Cp < -8.0 in Fig. 8. Logically, the high percentage of cycles with large pressure fluctuations at 80% span will produce the greatest standard deviation in the root flap load. Interestingly, however, 80% span for the untwisted blade shows similar pressure cycle activity without the resulting CM behavior. Whether this effect reflects data density variance, variance due to quasi-static mean CN participation at various spans, or a true three-dimensional dynamic effect resulting from geometry or separation effects has not been resolved at this time. Figure 14: Average and standard deviation for CM for untwisted blade. Figure 15: Average and standard deviation for CM for twisted blade. 10 CONCLUSIONS Aerodynamic performance was compared for two rectangular planform wind turbine blades having S809 airfoil cross-sections. These blades differed only in three-dimensional geometry, with one blade being untwisted and the other having an optimized twist distribution. These comparisons were based on field test data acquired at the National Wind Technology Center. Both blades exhibited two-dimensional aerodynamic performance at inflow conditions where the local blade angle was below static stall. Significant variations in performance were noted under near- and post-stall conditions. Peak pressure distributions and mean aerodynamic loads were far in excess of two-dimensional wind tunnel data. Individual pressure time series for select test cycles indicated both three-dimensional flow effects as well as dynamic stall contributed to these enhanced loads. Both blades were more dynamically active under nearand post-stall operation. This effect increased with both mean velocity and yaw error. Certain combinations of V∞ and ϕ exacerbated the effect and were correlated to cycles with high peak pressure coefficients at 80% span. The twisted blade was observed to be more dynamically active than the rectangular blade. It is not clear if this effect reflects data density variance, variance as a r...
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This note was uploaded on 11/13/2011 for the course AEE 495 taught by Professor O.uzol during the Spring '11 term at Middle East Technical University.

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