Comprehensive Manual20
© 2018 Nortek AS
wave cycles.
Example: Turbulent flows. In boundary layers, a rough rule of thumb is that the root-mean-square
(RMS) turbulent velocity is 10% of the mean velocity. If, for example, your mean velocity is 1 m/s,
you could estimate turbulent fluctuations to be 10 cm/s. Obtaining 1 cm/s RMS uncertainty would
require at least 100 pings.
When averaging several pings to reduce the error, there is a difference between the resulting “mean
current” and the measured current. This deviation from the actual current measurement is called bias
. Velocimeter bias depend on the geometry of its probes and the stability of its internal oscillators.
Over most of the velocity range, bias can be measured in terms of an offset and a scale-factor error.
Offsets would appear as a non-zero velocity in still water. If probe geometry were entirely fixed and
repeatable, then we could determine the velocity scale factor based entirely on dimensions,
frequencies and our physical model. In practice, small variations in probes require that probes are
calibrated individually in the factory.
A factory-calibrated velocimeter should have a scale-factor bias that is less than 1% of the measured
velocity. A velocimeter will retain this accuracy as long as its probe remains unbent.
Bias increases near the extremes of a velocimeter's velocity range because velocities that would be
measured beyond the extremes wrap around to the opposite end of the range. This can be avoided
by keeping the maximum observed speed toward the middle of the velocimeter's maximum range.
The configuration parameter to adjust is the Velocity Range.
Uncertainty, variance and standard deviation
Uncertainty is often measured in terms of measurement variance or standard deviation. These two
are related:
variance = (standard deviation)^2
Standard deviation quantifies the amount of variation of a set of velocity values, around the true value
of the velocity. For example: Imagine taking measurements of an unvarying velocity. The best
estimate of the true velocity is the mean of the measurements, and the uncertainty of each of the
measurements is the standard deviation of the collection of measurements.
Averaging
When averaging a collection of velocity measurements, the average is a better estimate of the true
velocity than each individual estimate. The uncertainty of the average is reduced according to:
variance(mean) = variance(individual measurements)/N
std dev(mean) = std dev(individual measurements)/N^1/2
In other words, by reducing the variance of individual measurements by a factor of 2, you can collect
data twice as fast.
Measurement Load (Vector)
A 100% measurement load means that the instrument pings as fast as it is able to. Consequently, a
measurement load of 50% means that the instrument pings at half that rate. From signal theory we
know that the more pings there are within an averaging period, the better the estimate of the true
value we are measuring will get. By increasing the measurement load, the precision in the velocity
measurements increase, but alas at the expense of battery consumption. By decreasing the
measurement load it is the other way around.
The measurement load for the Vector is adjusted according to the velocity range selected.
Variance and Noise Level
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