Effect of Particle Factors on the Reflux and Blockage of a Deep‐Sea Six‐Stage Pump Based on CFD‐DEM

Hu, Qiong; Zhu, Jingyan; Deng, Liwen; Chen, Jun; Wang, Yangyang

doi:10.1002/adts.202300931

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Based on the task requirements and actual conditions of deep-sea mining, countries around the world have successively proposed various design schemes for deep-sea mining systems [4][5][6]. Deep-sea mining systems typically consist of three main components: the seabed mining sub-system, the pipeline lifting sub-system [7][8][9], and the surface storage and transportation sub-system. The seabed mining subsystem is the most critical and complex link in the entire deep-sea mining system.…”

Section: Introductionmentioning

confidence: 99%

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Xing,

Wang,

Liu

2024

JMSE

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The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the path planning of mining vehicle cluster. To improve the security and coverage of deep-sea mining systems, this paper proposes a cluster-coverage path planning strategy based on a traditional algorithm and Deep Q Network (DQN). First, we designed a deep-sea mining environment modeling and map decomposition method. Subsequently, the path planning strategy design is based on traditional algorithms and DQN. Considering the actual needs of deep-sea mining missions, the mining vehicle cluster path planning algorithm is optimized in several aspects, such as loss function, neural network structure, sample selection mechanism, constraints, and reward function. Finally, we conducted simulation experiments and analysis of the algorithm on the simulation platform. The experimental results show that the deep-sea mining cluster path planning strategy proposed in this paper performs better in terms of security, coverage, and coverage rate.

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Section: Introductionmentioning

confidence: 99%

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Xing,

Wang,

Liu

2024

JMSE

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Hydrodynamic Parameter Identification of Deep‐Sea Mining Vehicle during Deployment and Retrieval Using a Nonlinear Filter

Guan,

Qin,

et al. 2024

Advcd Theory and Sims

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The aim of this paper is to propose a novel method for identifying the hydrodynamic parameters of a deep‐sea mining vehicle during deployment and retrieval. The proposed approach combines numerical simulation with a nonlinear filter. Initially, a dedicated hydrodynamic model for the deployment and retrieval of the mining vehicle is constructed. The identification process commences with simulations based on computational fluid dynamics (CFD). This approach utilizes CFD to simulate the motion of the deep‐sea mining vehicle during deployment and retrieval, employing an implicit solution approach to analyze its motion in Heave and Yaw degrees of freedom under periodic external forces. Consequently, this provides hydrodynamic performance data. Subsequently, the unscented Kalman filter (UKF) estimator is applied to optimally solve an augmented matrix that incorporates both motion data and hydrodynamic parameters, yielding numerical values for the hydrodynamic parameters. Simulation results demonstrate that, in comparison to motion performance obtained by the CFD method, the hydrodynamic model derived from UKF enables an effective prediction of the motion of the deep‐sea mining vehicle, with prediction errors consistently below 6%.

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Research Progress of Advanced Design Method, Numerical Simulation, and Experimental Technology of Pumps in Deep-Sea Resource Exploitation

Ji,

He,

et al. 2024

Water

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Amid the escalating global demand for raw materials, the gradual exhaustion of terrestrial mineral resources, and the rise in extraction costs and energy consumption, the development of deep-sea mineral resources has become a focal point of international interest. The pipeline lifting mining system, distinguished by its superior mining efficiency and minimized environmental impact, now accounts for over 50% of the total energy consumption in mining operations. Serving as the “heart” of this system, the deep-sea lifting pump’s comprehensive performance (high pressure tolerance, non-clogging features, elevated lift capacity, wear resistance, corrosion resistance, and high reliability, etc.), is critical to transport efficiency, operational stability, and lifespan of the mining system. As a mixed transport pump for solid and liquid media under extreme conditions, its internal flow structure is exceedingly complex, incorporating gas–liquid–solid multiphase flow. A precise understanding of its internal flow mechanisms is essential for breaking through the design limitations of deep-sea lifting pumps and enhancing their operational stability and reliability under various working conditions and multiphase media, thereby providing technical support for advancing global marine resource development and offshore equipment upgrades. This paper comprehensively reviews the design theory, optimization methods, numerical simulations, and experimental studies of deep-sea lifting pumps. It discusses the application of various design optimization techniques in hydraulic lifting pumps, details the multiphase flow numerical algorithms commonly used in deep-sea lifting pumps along with their modified models, and summarizes some experimental methodologies in this field. Lastly, it outlines the forthcoming challenges in deep-sea lifting pump research and proposes potential directions to promote the commercial development of deep-sea mining, thereby offering theoretical and engineering support for the development of deep-sea mining slurry pumps.

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Effect of Particle Factors on the Reflux and Blockage of a Deep‐Sea Six‐Stage Pump Based on CFD‐DEM

Cited by 3 publications

References 30 publications

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Hydrodynamic Parameter Identification of Deep‐Sea Mining Vehicle during Deployment and Retrieval Using a Nonlinear Filter

Research Progress of Advanced Design Method, Numerical Simulation, and Experimental Technology of Pumps in Deep-Sea Resource Exploitation

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