Design optimization of a multi-layer porous wave absorber using an artificial neural network model. (1st December 2022)

• Record Type: Journal Article
• Authors: George, Arun; Poguluri, Sunny Kumar; Kim, Jeongrok; Cho, Il Hyoung
• Is Part Of: Ocean engineering. Volume 265(2022)
• Publication Date: 2022-12-01
• Subjects: Multi-layer porous plate -- Matched eigenfunction expansion method -- Wave absorbing -- Artificial neural network -- Design optimization; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2022.112666
• Languages: English
• ISSNs: 0029-8018

Abstract

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 R 2 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 ≤ d 1 / h ≤ 0.067 and 0.117 ≤ P 1 ≤ 0.173 . The optimal range of design features can be used as a guideline for the design of an effective wave absorber. Highlights: The analytical model for horizontal multi-layer porous plates is developed using a matched eigenfunction expansion method. The energy dissipation at porous plates is considered by an equivalent linearized quadratic energy dissipation model. The design optimization of a multi-layer porous wave absorber is performed through the artificial neural network model. The optimal range of key design features can be used as a guideline for the design of an effective wave absorber.