  In this paper, we overview, evaluate, and demonstrate the sparse processing
particle image velocimetry (SPPIV) as a real-time flow field estimation method
using the particle image velocimetry (PIV), whereas SPPIV was previously
proposed with its feasibility study and its real-time demonstration is
conducted for the first time in this study. In the wind tunnel test, the PIV
measurement and real-time measurement using SPPIV were conducted for the flow
velocity field around the NACA0015 airfoil model. The off-line analysis results
of the test show that the flow velocity field can be estimated from a small
number of processing points by applying SPPIV, and also illustrates the
following characteristics of SPPIV. The estimation accuracy improves as the
number of processing points increases, whereas the processing time per step
increases in proportion to the number of processing points. Therefore, it is
necessary to set an optimal number of processing points. In addition, the
application of the Kalman filter significantly improves the estimation accuracy
with a small number of processing points while suppressing the processing time.
When the flow velocity fields with different angles of attack are used as the
training data with that of test data, the estimation using SPPIV is found to be
reasonable if the difference in angle of attack between the training and test
data is equal to or less than 2 deg and the flow phenomena of the training data
are similar to that of the test data. For this reason, training data should be
prepared at least every 4 deg. Finally, the demonstration of SPPIV as a
real-time flow observation was conducted for the first time. In this
demonstration, the real-time measurement is found to be possible at a sampling
rate of 2000 Hz at 20 or less processing points in the top 10 modes estimation
as expected by the off-line analyses.
