Research on the Prediction of Fiber Filtration Efficiency Based on"Physical Model+Data"Driven Neural Network
The rapid advancement of big data and artificial intelligence has opened up new avenues for exploring complex engineering problems.The process of dust particle removal in fiber filters is a highly intricate nonlinear kinetic process,making it challenging to develop an accurate prediction model for fiber filtration efficiency that takes into account the multifactorial nonlinear coupling.In this study,an artificial neural network driven by both physical models and data was employed to construct a predictive model for fiber filtration efficiency.Based on a simplified fiber filtration model,the Lagrangian particle dynamics methodwas employed to solve particle movement with consideration of the capture mechanismsof interception and diffusion.A database of fiber filtration efficiency was established with dependent variables including Pe number,interception parameter R,and fiber filling rate C.This database serves as training data samples for the neural network model,and the results show that the neural network model can be trained within a reasonable calculation time frame while accurately estimating fiber filtration efficiency.
Air filtrationParticle captureNumerical simulationNeural network
NETL
NSTL
万方数据
