Hydroacoustic optimization with using Noise-GAN

Abstract

In recent years, noise pollution has a significant impact on marine organisms, hence requiring some restrictions and safety protocols. The primary objective of these restrictions is to minimize the noise generated by human-driven vehicles in aquatic environments. As a result, hydroacoustical studies are becoming increasingly integrated into design studies. The objective of this study is to provide an innovative approach to the design of hydrofoils, are considered a crucial component in the field of hydroacoustic engineering. The objective of this approach is to create a cutting-edge optimization tool through the integration of machine learning and hydroacoustic performance calculations. This article presents the development of a novel method called Noise-GAN, combines Generative Adversarial Networks (GAN) algorithms with hydroacoustic optimization capabilities. The GAN algorithm is used as a dimensionality reduction method in the optimization process, allowing for quick exploration of the design space and obtaining unique profile geometries during optimization. This method is used to generate optimal hydrofoil geometry for various conditions. The hydroacoustic and hydrodynamic performances of the generated geometry are analyzed to determine the applicability and performance of the method. The results are compared to two existing dimensionality reduction method to enhance comprehension of the performance. In order to gain insight into the performance of generated geometries compared to traditional geometries, the results are also compared to the performance data of the profiles available in the literature. This article also includes comparing the performance results of the NACA0009 profile, which is typically employed in hydrofoil research, with the optimal geometries to evaluate of the method and the performance of the generated geometries. In order to determine the real-life applicability of the hydrofoil geometries generated by the method, optimization studies were performed at the five different Angle of Attack (AoA) conditions within the range of 0o - 15o. In this way, the feasibility of the method at different AoA values was also examined. The results of all these comparisons demonstrate that the Noise-GAN method, which was developed to generate hydroacoustically optimized hydrofoil geometry, is highly effective in a variety of respects. The optimization tool developed in this study generates hydrodynamically efficient and unique hydrofoil profiles while optimizing for hydroacoustical performance. The results of this study are presented in this paper.