Unsteady airflow classification by artificial neural networks

Author

McGibney, S. ; Zaknich, A.

Author_Institution

Dept. of Electr. & Electron. Eng., Western Australia Univ., Nedlands, WA, Australia

Volume

3

fYear

1999

fDate

1999

Firstpage

1094

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

Keywords

aerodynamics; computational fluid dynamics; laminar flow; laminar to turbulent transitions; multilayer perceptrons; pattern classification; real-time systems; turbulence; artificial neural networks; automatic real-time classifier; background; flow transitions; gas flow state classification; heuristically determined discriminate features; laminar flow; multilayer perceptron classifier; turbulent flow; unsteady airflow classification; Artificial intelligence; Artificial neural networks; Fluid flow; Fluid flow measurement; Intelligent networks; Probes; Signal processing; Temperature sensors; Velocity measurement; Wire;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-5871-6

Type

conf

DOI

10.1109/ICONIP.1999.844688

Filename

844688