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

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.