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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...

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Related Experiment Video

Updated: Jan 24, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Learning from droplet flows in microfluidic channels using deep neural networks.

Pooria Hadikhani¹, Navid Borhani², S Mohammad H Hashemi^2,3

¹Optics Laboratory, School of Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland. pooria.hadikhani@epfl.ch.

Scientific Reports

|June 1, 2019

Summary

This study introduces a non-intrusive optical method using neural networks to analyze droplet flow in microfluidic chips. The technique accurately measures fluid concentration and flow rate, with potential for other properties.

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Area of Science:

Microfluidics
Optical Measurement Techniques
Machine Learning Applications

Background:

Microfluidic devices enable precise control and analysis of small fluid volumes.
Non-intrusive measurement methods are crucial for preserving fluid properties and device integrity.
Accurate quantification of fluid composition and flow dynamics is essential in various scientific and industrial applications.

Purpose of the Study:

To develop a non-intrusive method for measuring fluidic properties in microfluidic chips.
To utilize optical monitoring of droplet flow and neural networks for data extraction.
To demonstrate the application of this method for quantifying mixture concentrations and flow rates.

Main Methods:

Optical monitoring of droplet behavior within a microfluidic chip.
Employing deep neural networks (DNNs) trained on a large dataset of droplet images.
Developing algorithms to extract fluid properties from image data.

Main Results:

Accurate quantification of component concentrations in a water/alcohol mixture with 0.5% accuracy.
Precise measurement of mixture flow rate with a resolution of 0.05 ml/h.
Demonstrated potential for measuring other fluid properties like surface tension and viscosity.

Conclusions:

The developed optical and neural network-based method offers a powerful non-intrusive approach for microfluidic analysis.
This technique provides high accuracy and resolution for critical fluidic parameters.
The method is adaptable for characterizing a broader range of fluid properties, enhancing microfluidic research and applications.