Publication: Forecasting lift and drag on a circular cylinder at Re=106 using point pressure data and a fuzzy ARTMAP neural network

All || By Area || By Year

Title	Forecasting lift and drag on a circular cylinder at Re=106 using point pressure data and a fuzzy ARTMAP neural network
Authors/Editors*	J. Ferrer-Gener, G.A. Kopp, Francesc Giralt and J. Galsworthy
Where published*	USA
How published*	Proceedings
Year*	2005
Volume
Number
Pages
Publisher
Keywords	circular cylinder, pressure measurements, simulation, neural classifier
Link
Abstract	The prediction or simulation of long term time series is important in many engineering applications. In the current work, the fuzzy ARTMAP neural network is used to simulate surface pressure time series for a long circular cylinder in cross flow at Re = 106. It is found that accurate lift and drag fluctuations can be obtained (through the integration of the pressure coefficients) only if the trained networks have an input structure containing information from all areas of the surface. This is due to the vortex shedding phenomenon. This may be a significant limitation to using this technique more generally for the task of obtaining aerodynamic load information for other bluff bodies.