论文信息 - Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition

Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition

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.

Narayanan Ramachandran | Subbiah Baskaran | David Noever

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