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Hydrodynamic and Vibroacoustic Simulation Analysis of the Main Float in an Acoustic Submerged Buoy System

by Jie Liu, Zixuan Jiang, Libin Du, Zhichao Lv, Hanbing Cui, Xinyu Li and Guangxin Liang

J. Mar. Sci. Eng. 2025, 13(7), 1254; https://doi.org/10.3390/jmse13071254 - 28 Jun 2025

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During prolonged deployment, deep-sea acoustic submerged buoys may undergo displacement and torsional deformation of their main floating body under turbulent flows, which degrades the quality of acquired sensor data and introduces vibration-induced noise that interferes with acoustic measurements. This paper presents a novel structural design for acoustic buoy main bodies based on hydrodynamic principles. We performed fluid-structure interaction (FSI) simulations to evaluate the dynamic response characteristics of the structure in deep-sea conditions, including computational analysis of velocity and pressure field distributions surrounding the buoy. Leveraging pressure data derived from computational fluid dynamics (CFD) simulations, we developed an innovative vibration noise quantification methodology. This approach employs plane wave excitation with equivalent pressure magnitude to simulate hydrodynamic loading effects while incorporating tripartite coupling mechanisms among fluid, structural, and acoustic domains. The simulated vibration noise profiles establish environmental baseline noise levels for onboard acoustic monitoring instruments, thereby enhancing measurement fidelity. Full article

(This article belongs to the Special Issue Hydrodynamic Research of Marine Structures (2nd Edition))

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Abstract

Reliable and efficient sensing and tracking of multiple weak or time-varying frequency line components in underwater acoustic signals is the topic of this paper. We propose a method for automatic detection and tracking of multiple frequency lines in lofargram based on hidden Markov model (HMM). Instead of being directly subjected to frequency line tracking, the whole lofargram is first segmented into several sub-lofargrams. Then, the sub-lofargrams suspected to contain frequency lines are screened. In these sub-lofargrams, the HMM-based method is used for detection of multiple frequency lines. Using image stitching and statistical model method, the frequency lines with overlapping parts detected by different sub-lofargrams are merged to obtain the final detection results. The method can effectively detect multiple time-varying frequency lines of underwater acoustic signals while ensuring the performance under the condition of low signal-to-noise ratio (SNR). It can be concluded that the proposed algorithm can provide better multiple frequency lines sensing ability while greatly reducing the amount of calculations and providing potential techniques for feature sensing and tracking processing of unattended equipment such as sonar buoys and submerged buoys. Full article

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