DEVELOPMENT OF FIVE-HOLE PROBE PROCESSING TECHNIQUES FOR ANGLE OF ATTACK, ANGLE OF SIDESLIP, AND AIRSPEED APPLIED TO EXPERIMENTAL DATA
In this thesis, the Artificial Neural Networks (ANN) technique was implemented to predict the flow parameters from five-hole probe measurement. The ANN technique is used to estimate the angle of attack, angle of sideslip, and flow speed. The experimental data of this work were obtained from a low...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/79467 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | In this thesis, the Artificial Neural Networks (ANN) technique was implemented
to predict the flow parameters from five-hole probe measurement. The ANN
technique is used to estimate the angle of attack, angle of sideslip, and flow
speed. The experimental data of this work were obtained from a low-speed
wind tunnel testing. In the testing, the velocity of the tunnel varied from 10 to
50 m/s in increments of 10 m/s. The angle of attack varies from -30° to 90° and
the angle of sideslip is varied from -20° to 20°. From the measured data, 80%
are randomly chosen to build the ANN model, while the remaining 20% are
used to validate the model. The accuracy of the ANN technique is compared to
other known methods. The first method uses the formula provided by the
manufacturer of the five-hole probe and the second method uses 5th-order
polynomial fitting. The novelty of the process in this paper is in the use of single
ANN model to predict all three flow parameters. This single model can be used
throughout the range of angle of attack, sideslip, and speed.
The findings reveal that the ANN model accurately forecasts flow parameters
in the low angle of attack range, surpassing the formula and polynomial
methods in mean absolute and maximum absolute error. Furthermore, the ANN
method significantly outperforms the other two methods in obtaining flow
parameters in high angle of attack regions. The proposed model can provide
mean absolute and maximum absolute error of 0.164 and 2.605, respectively
for angles of attack, of 0.584 and 14.522, respectively for angle of sideslip, and
of 0.024 and 0.242, respectively for speed. Further improvement of the ANN
model can be obtained by segmenting the applicability region. The initial result
shows that 95% improvement is achievable.
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