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 ne tworks is investigated for the calibration of multi-hole pressure probes an d the prediction of associated flow angularity patterns in test flow fields . Both types of network are studied in detail for their calibration and pre diction characteristics. The current formalism can be applied to any multi hole probe, however the test results for the most commonly used five-hole C one and Prism probe types alone are reported in this paper.