A new methodology was developed for flow regime identification in pipes.
The method utilizes the pattern recognition abilities of Artificial Neural Networks
and the unprocessed time series of a system-monitoring-signal.
The methodology was tested with synthetic data from a conceptual system,
liquid level indicating Capacitance signals from a Horizontal flow system
and with a pressure difference signal from a S-shape riser.
The results showed that the signals that were generated for the conceptual
system had all their patterns identified correctly with no errors what so ever.
The patterns for the Horizontal flow system were also classified very well
with a few errors recorded due to original misclassifications of the data. The
misclassifications were mainly due to subjectivity and due to signals that
belonged to transition regions, hence a single label for them was not adequate.
Finally the results for the S-shape riser showed also good agreement with the
visual observations and the few errors that were identified were again due to
original misclassifications but also to the lack of long enough time series for
some flow cases and the availability of less flow cases for some flow regimes
than others.
In general the methodology proved to be successful and there were a
number of advantages identified for this neural network methodology in comparison
to other ones and especially the feature extraction methods. These
advantages were: Faster identification of changes to the condition of the
system, inexpensive suitable for a variety of pipeline geometries and more
powerful on the flow regime identification, even for transitional cases.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/134 |
| Date | 03 1900 |
| Creators | Goudinakis, George |
| Contributors | Thompson, Chris |
| Publisher | Cranfield University, School of Engineering; Applied Mathematics and Computing Group |
| Source Sets | CRANFIELD1 |
| Language | en_UK |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
| Format | 1883 bytes, 7569841 bytes, text/plain, application/pdf |
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