Dynamic characteristic analysis of riser considering drilling pipe contact collision

Mao, Liangjie; Luo, Jun; Zeng, Song; Li, Jun; Chen, Ran

doi:10.1016/j.oceaneng.2023.115470

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…The main components of a deep-sea mining system include seabed mining vehicles [18][19][20], transportation hoses, relay stations, lifting hard pipes [21], and surface mining vessels [22][23][24]. The complete system structure is shown in Figure 1.…”

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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Nonlinear coupled motions of a pipe-in-pipe system experiencing vortex-induced vibrations

Hou,

Long,

Meng

et al. 2024

Nonlinear Dyn

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This paper delves into the dynamics of the pipe-in-pipe system, crucial in offshore oil and gas exploration, featuring an inner drill string nestled within an outer riser. A reduced-order model with five degrees of freedom is proposed to capture the coupled dynamics of this system. Here, the inner drill string is depicted as a torsional-lateral coupled rotor constrained by the outer riser. In contrast, the outer riser is modeled as a suspended stator subjected to vortex-induced excitations and contact forces. The model incorporates nonlinearities, including dry frictions, collisions, loss of contact, and nonlinear damping through the van der Pol wake oscillator. This comprehensive model allows an indepth exploration of the dynamics of the coupled pipein-pipe system. Parametric analysis is conducted based on the non-dimensional numerical model, revealing a novel and stable whirling phenomenon termed "follower whirling" at low rotary speeds of the drill string. During this stable follower whirling, the riser's vortex-induced vibration traces an "8"-shaped trajectory, while the

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Analysis of lateral dynamic response characteristics of drilling riser during installation of deep-water Christmas tree

Qiaolei,

Yuwei,

et al. 2023

Journal of Engineering Research

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Dynamic characteristic analysis of riser considering drilling pipe contact collision

Cited by 5 publications

References 26 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

Nonlinear coupled motions of a pipe-in-pipe system experiencing vortex-induced vibrations

Analysis of lateral dynamic response characteristics of drilling riser during installation of deep-water Christmas tree

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