Unsteady airflow classification by artificial neural networks

Abstract

A multilayer perceptron classifier is applied to the classification of gas flow states. A number of suitable discriminate features are determined heuristically for the categorization of gas flow states, including the background (machinery and wind tunnel noise), laminar flow (sinusoidal signal), transition 1 (frequency-resonant shifts), transition 2 (instantaneous changes in phase and turbulent characteristics) and turbulent flow (random noise). This technique can be used to develop an automatic real-time classifier for gas flow.