Deep auto-encoder network in predictive design of Helmholtz resonator: On-demand prediction of sound absorption peak

In this paper, a large sample database is established by using a classical Helmholtz resonator, and the acoustic performance parameters and geometric structural parameters are trained by using the deep auto-encoder (DAE) model based on machine learning. Next, 20 groups of target peak points (α_E, f_E) are arbitrary selected inside and outside the sample database, and the corresponding geometric structural parameters (d_n, d_c, h_p, h_n) are obtained by training the DAE model, and the predicted peak points (α_D, f_D) are obtained. By comparing the target peak points (α_E, f_E) with the predicted peak points (α_D, f_D), the verification results show that the DAE model has very high prediction accuracy for samples from the database, while the prediction accuracy for samples out of the database is slightly lower. The proposed method represents a transformation of the design paradigm of functional acoustic devices, which can be further applied to the predictive design of more complex acoustic structures, and has great application potential in the field of vibration and noise reduction.