Dynamics of anchor last deployment of submersible buoy system

Zheng, Zhongqiang; Xu, Jiajun; Huang, Peng; Wang, Lei; Yang, Xiaoguang; Chang, Zongyu

doi:10.1007/s11802-016-2627-3

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…The effect of seasonality in biasing the learning of the ML and other models is important to quantify. Often, due to weather conditions, battery life and access constraints, buoys are deployed for a limited duration [22,23]. It is important, therefore, to examine the changes in WPR across months and in harsher and calmer conditions.…”

Section: Seasonal Variability In Model Trainingmentioning

confidence: 99%

Machine learning approach for optimal determination of wave parameter relationships

Barker

Murphy

2017

IET Renewable Power Generation

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Wave parameter relationships have long been determined using methods that give non-standard and often inaccurate results. With increased commercial activity in the marine sector, the importance of accurate wave parameter relationship determination has become increasingly apparent. The outputs of many numerical models and buoy datasets do not include all requisite wave parameters, and a typical approach is to use a constant conversion factor or relationship based on defined spectra such as the Bretschneider or the joint North Sea wave observation project (JONSWAP) spectrum to determine these parameters. Given that relationships between wave parameters vary significantly over both hourly and seasonal and annual timescales, the currently employed methods are lacking, as subtleties are missed by the simpler approach. This paper addresses the determination of wave parameter relationships using a machine learning (ML)-based model, identifying and selecting the optimal method for the conversion of wave parameters (T e , T 01) in coastal Irish Waters. This approach is then validated at two sites on the West coast of Ireland. The aim is to highlight the utility of ML in approximating the relationship between wave parameters; using both buoy and modelled data, and mapping the predicted outcomes for a wave energy converter based on a variety of ML and measure correlate predict approaches.

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Section: Seasonal Variability In Model Trainingmentioning

confidence: 99%

Machine learning approach for optimal determination of wave parameter relationships

Barker

Murphy

2017

IET Renewable Power Generation

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“…To optimize the motion performance of the platform and alleviate the vertical forces exerted on the lower end of the mooring lines, a series of weights and buoys were deliberately placed at strategic intervals along each segment of the mooring lines. The deployment dynamics of submersible buoy systems were investigated by Zheng Zhongqiang et al (2016) through the use of both numerical and experimental approaches [15]. Upon comparing the outcomes of the two approaches, it was determined that the numerical model effectively replicated the factual procedure and circumstances of the experiment.…”

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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“…The motion equations are developed in discrete node description and fully Cartesian coordinates. Zheng Zhongqiang et al (2012) used a numerical method to solve the ordinary differential equations and dynamics simulations achieved while the anchor is cast from onboard, then (2016) studies the deployment dynamics of submersible buoy systems with numerical and experimental methods [6,7]. Yang Yu et al (2019) investigated the effect of liquid sloshing of inner tanks on the dynamic responses of an underwater soft yoke mooring system.…”

Section: Introductionmentioning

confidence: 99%

“…Completed studies in the literature of the anchor last deployment of marine submersible buoy system were usually based on the multi-body dynamics method, which is a simple and effective method to get the tension force on cables [5][6][7]. However, as shown in Figure 2, the huge tension force on cables usually occurs at the moment when the buoy is dragged into the water, and the tension force on the cables is highly concerning due to the bouy's entry into the water.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation Research on the Anchor Last Deployment of Marine Submersible Buoy System Based on VOF Method

Chen

Liu

2022

JMSE

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Marine submersible buoy systems are widely-used equipment for ocean resource development. The marine submersible buoy system studied in this paper consists of the submersible buoy, the battery compartment, the anchor block, the mooring line, and the power supply cable. To study the mechanical behavior and obtain the speed variation of each component during the anchor last deployment, this paper establishes the free surface computational fluid dynamics model of marine submersible buoy systems based on the VOF method. This model includes the incompressible Navier–Stokes equations, the Renormalization-Group turbulence model, and the fractional areas/volume obstacle representation method. The free fluid surface is tracked using the VOF method. The lumped mass method is used to simulate the mooring line and power supply cable. The results showed that the tension forces increase when the mooring lines were straightened. Subsequently, the tension forces gradually decrease with oscillations. After the anchor block sinks to the sea floor, the positive buoyancy of the battery compartment and the buoy will cause large tension on the mooring line and power supply cable.

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Dynamics of anchor last deployment of submersible buoy system

Cited by 6 publications

References 11 publications

Machine learning approach for optimal determination of wave parameter relationships

Machine learning approach for optimal determination of wave parameter relationships

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

Numerical Simulation Research on the Anchor Last Deployment of Marine Submersible Buoy System Based on VOF Method

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