Optimization of sound absorbing coatings with inner cylindrical cavity using genetic algorithm and Surrogate Model based on artificial neural network

This research introduces the optimal design for underwater sound-absorbing linings.To address the time and computational cost of the optimization process, a combined approach of the genetic algorithm and a surrogate model using artificial neural networks was implemented.The results conclusively demonstrate that the best cavity shape for the low-frequency domain is conical with an apex near the surface of the entering wave.In contrast, a conical shape in the opposite direction of the lowfrequency case is optimal for the entire frequency range.Selecting the best frequency range is crucial for the design and optimization process.The optimal response significantly outperformed other randomly established linings, validating the chosen approach.Additionally, increasing the number of optimization variables will undoubtedly alter the optimal solution, albeit with a considerable increase in problem complexity.