L16 Using the Mean Wind Pressure as the Metric
For this analysis, we used the Mean of the sensor reading as the metric
for the L16, defined as:
There are 39 sensors, and the results produce a 17x39
matrix (the raw matrix is also available).
The columns of the matrix (1 to 39) represent the sensors.
The rows represent the measured L16 effects
(1-16) and the noise estimate (row 17). The results are given in
the correct units (Pa for the pressure sensors and m/s
for the velocity sensors).
All Results
The image below is a graphical representation of the effects matrix.
That is, rows in the image correspond to particular effects, while
columns correspond to particular sensors. Brighter values represent
stronger effects. Note that the dominant value is the noise estimate in
the dome sensors. This is caused by the bad
data for these channels.
Pressure Results
The image below is a graphical representation of the effects matrix for
only the pressure sensors (sensors 1-24). In this image, it is clear
that some sensors are more strongly affected by various effects.
However, the major feature of the image is that rows 1, 2, 5, 6, 9, and
13 appear to be possibly significant. Row 1 is just the average value
of the tests, so is not important, but Row 2 is the AoA, row 5 is the
UVG, row 6 is the AoA/UVG interaction, row 9 is the DVG, and row 13 is
the UVG/DVG interaction.
SNR Results
While some effects appear to be significant in the raw
analysis, it is more important to compare the values with row 17, which
is the noise estimate. The image below is a graphical representation of
the signal to noise ratio for each of the sensors. Since the noise
estimate on sensor 23 was very low, it is difficult to see detail in
that image, so there is also another color stretch of the same image
shown. The results confirm the effects described above.
Normal Scaling:
Stretched to reduce scaling issues caused by sensor 23:
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