This invention discloses a relative
acoustic impedance prediction method for composite microperforated plate structures based on a
surrogate model, belonging to the field of vibration and
noise control technology. This invention considers the structural parameters of variable cross-section series-parallel composite microperforated plate structures and uses Latin
hypercube sampling to generate parameter samples. Relative
acoustic impedance data for different structures are obtained through numerical
simulation. Fourier series functions are constructed using the relative
acoustic impedance data to solve for the fitting coefficients, thus achieving a fit to the relative acoustic impedance. A multi-input / output
multilayer perceptron neural network is built to predict the fitting coefficients based on the structural parameters, forming a relative acoustic impedance prediction
surrogate model with the fitting coefficients as an intermediate bridge. The relative acoustic impedance prediction method of this invention effectively solves the contradictory problems of high accuracy but long time consumption in numerical
simulation of acoustic superstructures, and high requirements for
mathematical theory and low accuracy in analytical solution derivation, thus balancing the accuracy and efficiency of acoustic
superstructure design.