Machine-learning-based model for predicting surface impedance of microslit panels in grazing flow

Accurately predicting the acoustic properties of noise-reduction materials in real-world, flow-exposed environments remains a significant challenge in engineering. Classical models of microslit panels are unsuited for grazing flow conditions due to complex, nonlinear flow-acoustic interactions. To overcome this challenge, this paper proposes a machine-learning-based surface impedance prediction model for microslit panels. A unit cell comprising a microslit panel with a backing cavity is selected as the research subject. Parameters including geometric features, flow and acoustic conditions are used to create a dataset. The correction term, which accounts for the flow effect on the surface impedance, can be derived through flow-acoustic simulations using COMSOL Multiphysics. The resulting dataset is then fed into a backpropagation neural network model. The combined machine learning model, constructed by the trained backpropagation neural network model and classical microslit panel formulas, is experimentally validated and demonstrates rapid surface impedance prediction ability. Using this model, this study further conducts a statistical analysis of the coupling effects of various features on surface impedance. Our framework offers a powerful tool for the rational design and analysis of high-performance acoustic liners, with substantial application potential for aerospace and environmental noise control. Sidong Zhang and colleagues develop a Machine-Learning-based Surface Impedance Prediction model that enables fast acoustic characterization prediction of microslit panels under grazing flow. This work demonstrates that the effect of flow on acoustic resistance is predominantly positive, while its effect on acoustic reactance is negative, with a few cases where the reversal of the influence of structural parameters occurs and exhibiting complex flow-acoustic coupling phenomena.