Microsystem Technologies

, Volume 23, Issue 6, pp 2297–2305 | Cite as

Feature coefficient prediction of micro-channel based on artificial neural network

  • Liu HuangEmail author
  • Weirong Nie
  • Xiaofeng Wang
  • Teng Shen
Technical Paper


In order to study the flow damping in micro-channels, unsteady Bernoulli equation was adopted to derive the motion equation. Artificial neural network (ANN) was adopted to predict the feature coefficient in the motion equation. Firstly, the motion equation of liquid column, flow in micro-channel, under inertial force, was derived. Then, the numerical mapping relationship between the feature parameters and the feature coefficient of micro-channel was modeled using ANN. Moreover, a hybrid optimization algorithm was developed to train the ANN model, which based on back propagation, particle swarm optimization and genetic algorithm. Finally, by taking the rectangular cross section straight micro-channel as an example, the theoretical approach was demonstrated. The training samples were generated by computational fluid dynamics simulation. The results were verified by the centrifugal testing of a prototype. The mean deviation between the theoretical and experiment is 4.7 %. The theoretical approach was proved practicable.


Particle Swarm Optimization Artificial Neural Network Artificial Neural Network Model Inertial Force Computational Fluid Dynamic Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research is supported by the National Natural Science Foundation of China, No.51475245. And thanks for the fabrication assistance of Wenhao Chip Technology Co. Ltd, in Suzhou, Jiangsu, China.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Mechanical EngineeringNanjing University of Science and TechnologyNanjingChina

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