Simulative Evaluation of the Underwater Geodetic Network Configuration on Kinematic Positioning Performance

Li, Menghao; Lan, Yang; Liu, Yanxiong; Chen, Guanxu; Tang, Qiuhua; Han, Yang; Wen, Yuanlan

doi:10.3390/rs14081939

Cited by 2 publications

(1 citation statement)

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“…The aim was to reduce the response of the turbines to combined winds and waves. Menghao Li and colleagues (2022) proposed an underwater acoustic geodetic network configuration consisting of three seafloor base stations, one subsurface buoy, and one sea surface buoy, with the aim of offering high-precision kinematic positioning for underwater submersibles [29]. Atefeh Neisi et al (2022) presented two hybrid mooring systems aimed at enhancing the dynamic motion performance of floating platforms [30].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Optimization of the Anchor-Last Deployment of the Marine Submersible Buoy System Based on the Particle Swarm Optimization Algorithm

Chen

Liu

2023

JMSE

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Marine submersible buoy systems hold significant value as critical equipment in marine science research. This study examines a marine submersible buoy system that includes an anchor block, mooring line, battery compartment, power supply cable, and submersible buoy. The anchor-last deployment method is a conventional strategy for deploying marine submersible systems. Initially, the other components are positioned on the sea surface, followed by the deployment of the anchor block from the ship’s deck. The anchor block will pull the battery compartment and submersible buoy into the water and eventually sink to the seabed. In this deployment process, ocean currents have a relatively large impact on the anchor block’s landing position. Increasing the weight of the anchor block will make the anchor block land on the seabed sooner, which can minimize the impact of ocean currents. However, an overabundance of weight can generate a significant strain on both the cables, potentially resulting in cable breakage. In order to find the parameters that can make the anchor block reach the seabed as soon as possible and ensure that the tension force of the cables does not exceed the maximum, a dynamic model of the deployment process is established based on computational fluid dynamics (CFD) and solved using the Runge–Kutta method of the fourth order. Particle swarm optimization is employed to optimize the key parameters. The penalty function is used to constrain the particle space. The findings indicate that the utilization of particle swarm optimization is efficacious for optimizing the parameters of submersible buoy systems for marine applications. Optimized parameters allow the anchor block to reach the seafloor quickly and the tension on the cables to not exceed the given value.

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Section: Introductionmentioning

confidence: 99%