Nano Science and Technology Institute
Nanotech 2005 Vol. 1
Nanotech 2005 Vol. 1
Technical Proceedings of the 2005 NSTI Nanotechnology Conference and Trade Show, Volume 1
Chapter 10: Micro and Nano Fluidics Design and Phenomena

Microfluidic Injector Models Based on Neural Networks

Authors:R. Magargle, J.F. Hoburg and T. Mukherjee
Affilation:Carnegie Mellon University, US
Pages:616 - 619
Keywords:microfluidic, electrokinetic, lab-on-a-chip, injector, neural network
Abstract:An injector modeling methodology based on neural networks is presented. The new aspects of this approach are (1) the full description of the dynamic injector behavior and (2) the applicability of the approach to a much broader range of injector types. Examples are shown for the cross, doublt-tee, and gated-cross injectors. Accuracy is on the order of 10^-4 for mean squared error, with four orders of magnitude increase in speed relative to numerical simulation. These fast and accurate injector models present a much more feasible approach to CAD than numerical simulation, when interconnected with other microfluidic system component block models such as the mixer and separator.
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