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ANN-Based Instantaneous Simulation of Particle Trajectories in Microfluidics
Naiyin Zhang1, Kaicong Liang2, Zhenya Liu2
1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Micromachines
|December 23, 2022
Summary
Artificial neural networks (ANNs) offer a faster way to analyze particle trajectories in microfluidic devices for cell analysis. This method reduces computational costs and simulation time for researchers.
Area of Science:
- Biomedical Engineering
- Computational Science
Background:
- Microfluidic devices are crucial for cell analysis, but their design is complex and time-consuming.
- Accurate simulation of particle trajectories within microfluidic channels is essential for reliable study outcomes.
Purpose of the Study:
- To develop a computationally efficient method for analyzing particle trajectories in microfluidic devices.
- To reduce the time and labor involved in microfluidic device design and simulation.
Main Methods:
- Proposed an artificial neural network (ANN) model with three dense layers.
- The ANN analyzes particle trajectories at critical intersections and integrates them with trajectories in straight channels.
- Compared ANN predictions with COMSOL simulation results.
Main Results:
- ANN predictions showed high consistency with COMSOL simulation data.
- The ANN method significantly shortened simulation times.
- The computational expense was substantially lowered compared to traditional simulations.
Conclusions:
- The proposed ANN method is a viable and efficient tool for predicting particle trajectories in microfluidics.
- This approach provides researchers with instant simulation results, accelerating microfluidic device development.
- The method enhances the practicality of microfluidics in cell analysis by optimizing the design process.
