Abstract
The use of probabilistic (PNN) and multilayer feedforward (MLFNN) neural networks is investigated for the calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of network are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this paper.
Article PDF
Similar content being viewed by others
Author information
Authors and Affiliations
Additional information
Received: 1 October 1998¶Received in revised form: 12 December 1998¶Accepted: 16 December 1998
Rights and permissions
About this article
Cite this article
Baskaran, S., Ramachandran, N. & Noever, D. Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition. Pattern Analysis & Applications 2, 92–98 (1999). https://doi.org/10.1007/s100440050018
Published:
Issue Date:
DOI: https://doi.org/10.1007/s100440050018