Principles of Operation 19
© 2018 Nortek AS
Measurement range:
The maximum and minimum distances that can be measured are approximately as listed below, but
note that it will depend a lot on the hardness of the bottom.
Vector: 4 cm - 45 cm
Vectrino+: 1 cm - 35 cm
Vectrino Profiler: 20 mm - 2 m. The Vectrino Profiler can be configured where to start collecting
bottom check data. Refer to SW user guide (available within the software) for more details.
We provide all of the data used to determine the peak location if users need to create their own
bottom detection algorithm. This is available as the probe check data in the Vector and Vectrino, and
in the Bottom Check fields in the Vectrino Profiler data structure.
Quality (relevant for Vectrino Plus)
Quality is a number indicating how valid the distance measurement is. Above 100 should be a good
distance measurement. It is always good to have an independent estimate of position though, so we
usually recommend using the probe check feature of the Vectrino software to get a ballpark number.
More information about the Distance Check can be found in the Using a Velocimeter chapter.
1.10 Velocimeter Accuracy
The word accuracy is often used loosely to cover some combinations of bias and short-term
uncertainty. Bias and short-term uncertainty are different, and they have different ramifications for the
results of your data collection. Here is the difference:
Bias is error that remains after taking long-term averages.
Short-term error is the random error of individual measurements, which can be removed by
averaging.
These errors have different sources and different consequences for the results. The following will
address each of these errors separately.
The velocity measurements are the average of many velocity estimates (called pings). The
uncertainty of each ping is dominated by the short-term error. This error is uncorrelated from ping to
ping, so by averaging together many pings, the measurement uncertainty is reduced. The short-term
error depends partly on internal factors, such as the size of the transmit pulse, the measurement
volume and the beam geometry. Beams parallel to the dominant flow will have smaller short-term
errors than beams at a steep angle relative to the flow.
The instrument’s software predicts errors based on the short-term error of a single ping and the
number of pings averaged together. Averaging multiple pings reduces errors according to the formula:
Where σ represents the standard deviation and N is the number of pings you average together. Note
that the software predicts only the instrumental error.
In many situations, external factors such as the environment itself dominate the short-term error.
This is true near an energetic surface and in turbulent flow such as boundary layers and rivers. In
situations like this, your data collection strategy should take into account the nature and the time
scales of the environmental fluctuations. Here are two scenarios:
Example: Waves. When measuring mean velocities in the presence of waves you should sample
velocity at roughly ¼ the interval of the dominant wave period, and you should sample through 6-10
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