Design optimization of a multi-layer porous wave absorber using an artificial neural network model

Abstract

The design optimization of a multi-layer porous wave absorber has been achieved through the supervised artificial neural network (ANN) and analytical model validated by CFD and experiments. The analytical model is established by means of a matched eigenfunction expansion method (MEEM) by applying the boundary condition with a quadratic relationship between the pressure drop and traversing fluid velocity at the porous plates. Along with the reflection coefficient as a target feature, input key features of the multi-layer porous wave absorber are selected through the parametric study. A dataset with 200 combinations of input design features is generated by the developed analytical model. Using the dataset, the ANN model is trained with a R2 score of 0.97. Predictions are generated for a large sample set with the trained ANN model. Top 1% combinations of input features that yield a minimum value of the reflection coefficient are used for the optimal design of the wave absorber. Based on the interquartile range (IQR) value of this 1% data, it is found that the most important design features are the porosity and submergence depth of the upmost plate and their optimal ranges for a double-layer wave absorber are 0.055≤d1/h≤0.067 and 0.117≤P1≤0.173. The optimal range of design features can be used as a guideline for the design of an effective wave absorber.