Abstract
mining the resulting trend was not possible. This curve is significant in that it is consistent with the conclusion reached in the previous subsection. As the turbulence intensity (based on local measured mean velocity) is increased, signal biasing is increased. For the present data for all axial locations, local turbulent intensities above 0.3 led to significant differences between the present mean velocity data and those of Eggers. Next, the present turbulence intensity data for the Mach 2.22 jet are compared to data for Mach 0.30 and 0.45 jets.3 In Fig. 3, the nondimensionalized rms velocity (nondimensionalized by the maximum value) from a composite of Fig. 2 (a2/Ue) is compared to data of Davies et al. The three sets of data are in qualitative agreement. However, since the Mach number of the present data is significantly greater than unity, quantitative agreement was not expected. The present turbulence intensity data are seen to define a smaller mixing region width for the larger Mach number jets. This conclusion is consistent with the mean velocity data presented in Fig. 1. Conclusions Agreement of the presented mean LV velocity data with the pitot tube data is excellent. Differences between the present data and previous subsonic turbulence intensity data are attributed to compressibili ty effects. The velocity and turbulence intensity profiles also exhibit a high degree of selfsimilarity in the near wake. These profiles indicate that the width of the mixing region for the supersonic jet is smaller than that of the subsonic jet. Several forms of previously unacknowledged LV signal biasing also were identified, particularly for regions of the flow with large values of turbulence intensity. The present examination was limited to values of local turbulence intensity of 0.3 before biasing was evident. Theoretical predictions have been made for the magnitudes of the individual biases.n These results will be presented in a subsequent paper. In conclusion, the authors would like to see more experimental data to identify the magnitudes of LV biases when highly turbulent flows are measured. Acknowledgments
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