Among underwater noise sources around submerged bodies, turbulence-induced noise has not been well investigated because of the difficulty of predicting it. In computational aeroacoustics, a number of studies has been conducted using the Ffowcs Williams–Hawkings (FW–H) acoustic analogy without consideration of quadrupole source term due to the unacceptable calculation cost. In this paper, turbulence-induced noise is predicted, including that due to quadrupole sources, using a large eddy simulation (LES) turbulence model and a developed formulation of permeable FW–H method with an open source computational fluid dynamics (CFD) tool-kit. Noise around a circular cylinder is examined and the results of using the acoustic analogy method with and without quadrupole noise are compared, i.e. the FW–H method without quadrupole noise versus the permeable FW–H method that includes quadrupole sources. The usability of the permeable FW–H method for the prediction of turbulence-noise around submerged bodies is shown

As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this study, a system that can monitor the health of in situ AUV thrusters using a convolutional neural network (CNN) was developed. As input data, an acoustic signal that comprehensively contains the mechanical and hydrodynamic information of the AUV thruster was adopted. The acoustic signal was pre-processed into two-dimensional data through continuous wavelet transform. The neural network was trained with three different pre-processing methods and the accuracy was compared. The decibel scale was more effective than the linear scale, and the normalized decibel scale was more effective than the decibel scale. Through tests on off-training conditions that deviate from the neural network learning condition, the developed system properly recognized the distribution characteristics of noise sources even when the operating speed and the thruster rotation speed changed, and correctly diagnosed the state of the thruster. These results showed that the acoustic signal-based CNN can be effectively used for monitoring the health of the AUV’s thrusters.

Recent research has shown that quadrupole noise has a significant influence on the overall characteristics of flow-induced noise and on the performance of underwater appendages such as sonar domes. However, advanced research generally uses the Ffowcs Williams–Hawkings analogy without considering the quadrupole source to reduce computational cost. In this study, flow-induced noise is predicted by using an LES turbulence model and a developed formulation, called the formulation Q1As method to properly take into account the quadrupole source. The noise around a circular cylinder in an underwater environment is examined for two cases with different velocities. The results from the method are compared to those obtained from the experiments and the permeable FW–H method. The results are in good agreement with the experimental data, with a difference of less than 1 dB, which indicates that the formulation Q1As method is suitable for use in predicting quadrupole noise around underwater appendages

Recent advances in vision–language models (VLMs) have transformed the field of robotics. Researchers are combining the reasoning capabilities of large language models (LLMs) with the visual information processing capabilities of VLMs in various domains. However, most efforts have focused on terrestrial robots and are limited in their applicability to volatile environments such as ocean surfaces and underwater environments, where real-time judgment is required. We propose a system integrating the cognition, decision making, path planning, and control of autonomous marine surface vehicles in the ROS2–Gazebo simulation environment using a multimodal vision–LLM system with zero-shot prompting for real-time adaptability. In 30 experiments, adding the path plan mode feature increased the success rate from 23% to 73%. The average distance increased from 39 m to 45 m, and the time required to complete the task increased from 483 s to 672 s. These results demonstrate the trade-off between improved reliability and reduced efficiency. Experiments were conducted to verify the effectiveness of the proposed system and evaluate its performance with and without adding a path-planning step. The final algorithm with the path-planning sub-process yields a higher success rate, and better average path length and time. We achieve real-time environmental adaptability and performance improvement through prompt engineering and the addition of a path-planning sub-process in a limited structure, where the LLM state is initialized with every application programming interface call (zero-shot prompting). Additionally, the developed system is independent of the vision–LLM archetype, making it scalable and adaptable to future models.

A turbulent boundary layer develops on the surface of submerged bodies in motion; these layers consist of complex flows and interact with the structure causing turbulent boundary layer induced noise due to fluid-structure interaction. Recently, although the research on such noise has attracted great interest in the naval fields, owing to the focus on the competitive development of low-noise naval ships, the limitations corresponding to the application of methods developed in aeroacoustics for underwater structures having lower convection speed of turbulence and faster sound speed along with insufficient environments to conduct experiments restrained to subjects of simple structures at high frequency. To overcome the abovementioned limitations and study the noise characteristics for ship hull design, in this research, methods to analyze the noise radiated due to turbulent flow on the complex underwater structure are developed using energy flow analysis methods for vibro-acoustic calculations. For estimation of the input hydrodynamic forces, wall pressure fluctuation spectrum on the surface is obtained from turbulent boundary layer properties to acquire sufficient resolutions. The vibrational response of the structure is calculated using energy flow analysis incorporating the finite element method for structural forces estimated as input power. The acoustical response coupled with the vibrational response is obtained using the calculated vibrational energy density with the boundary element method in combination with the energy flow analysis, taking advantages of the fact that the methods share the common energy variables. Developed methods are validated with a case of broadband noise radiated from a plate. Using the procedures, numerical estimation and analysis of acoustic performance are performed for trimaran ship hull designs with steady-state computational fluid dynamics to demonstrate the method’s usability as an assessment tool in the early design stage.