Microfluidic Injector Models Based on Artificial Neural Networks

Abstract

Lab-on-a-chip (LoC) systems can be functionally decomposed into their basic operating devices. Common devices are mixers, reactors, injectors, and separators. In this paper, the injector device is modeled using artificial neural networks (NNs) trained with finite element simulations of the underlying mass transport partial differential equations (PDEs). This technique is used to map the injector behavior into a set of analytical performance functions parameterized by the system's physical variables. The injector examples shown are the cross, the double-tee, and the gated-cross. The results are four orders of magnitude faster than numerical simulation and accurate with mean square errors (MSEs) on the order of 10(exp -4). The resulting NN training data compare favorably with experimental data from a gated-cross injector found in the literature.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 15, 2005
Accession Number
ADA500967

Entities

People

  • James F. Hoburg
  • Ryan Magargle
  • Tamal Mukherjee

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Computer-Aided Design
  • Differential Equations
  • Electric Fields
  • Equations
  • Geometry
  • Injectors
  • Integrated Circuits
  • Lab-On-A-Chip
  • Microelectromechanical Systems
  • Microfluidics
  • Neural Networks
  • Parallel Computing
  • Partial Differential Equations
  • Simulations
  • Topology
  • Two Dimensional
  • Voltage

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Internal Combustion Engine (ICE) Technology.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks