Effect of pipeline surface roughness on peak impact forces caused by hydrodynamic submarine mudflow

Guo, Xingsen; Stoesser, Thorsten; Nian, Tingkai; Jia, Yangwen; Liu, Xiaolei

doi:10.1016/j.oceaneng.2021.110184

Cited by 45 publications

(12 citation statements)

References 52 publications

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“…Their primary focus was the in-depth exploration of the rheological characteristics exhibited by hydrate slurries across diverse seabed soil mass fraction conditions. Researchers such as SI [27] and Guo [28], among others, have similarly delved into examining the rheological properties of both seabed mud flow and hydrate-mixed slurries. The outcomes of these investigations reveal a notable non-linear relationship between the logarithmic coordinates of shear rate (γ) and apparent viscosity (µ e ) of the slurry.…”

Section: Parameterization Of Operating Conditionsmentioning

confidence: 99%

The Influence of Shear-Thinning Characteristics on Multiphase Pump Vortex Structure Evolution, Pressure Fluctuation, and Gas-Solid Distribution

Chen,

Yang,

Peng

et al. 2024

Processes

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In the current landscape of natural gas hydrate extraction, the lifting pump assumes a pivotal role as the essential equipment for conveying subsea fluidized hydrate slurry to the wellhead. The inherent shear-thinning characteristics of natural gas hydrate slurry, compounded by the complex multiphase flow conditions of the “gas-liquid-solid” system, present significant challenges to the operational efficiency and stability of the lifting pump. Consequently, this study adopts a hybrid approach, combining experimental and numerical simulations, to comparatively investigate the impact of non-Newtonian and viscous Newtonian fluids on the hydraulic performance, vortex structure evolution, and induced pressure fluctuations in a multiphase pump. Concurrently, a comparative analysis is conducted on the influence of these two fluid types on the distribution patterns of the “gas-solid” two-phase system. The research findings indicate that the apparent viscosity variations are more pronounced in the diffuser region compared to the impeller region. Under non-Newtonian fluid conditions, two separation vortices emerge at the trailing edge of the diffuser, as opposed to a single separation vortex in the viscous Newtonian fluid, with the latter exhibiting a smaller vortex structure scale. Moreover, the shear-thinning characteristics intensify the interaction between the separated vortex and the mainstream, resulting in an exacerbation of pressure fluctuations. In contrast to the viscous Newtonian fluid, the rotor–stator interaction and shear-thinning characteristics play a predominant role in pressure fluctuations, with shear-thinning attributes giving rise to low-frequency pressure fluctuations. Additionally, shear-thinning characteristics significantly influence the distribution behavior of the gas-solid two-phase flow.

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Section: Parameterization Of Operating Conditionsmentioning

confidence: 99%

The Influence of Shear-Thinning Characteristics on Multiphase Pump Vortex Structure Evolution, Pressure Fluctuation, and Gas-Solid Distribution

Chen,

Yang,

Peng

et al. 2024

Processes

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“…In recent years, the computational fluid dynamics (CFD) simulation approach has been widely used in the studies of submarine slide motion (e.g., Gauer et al, 2005;Acosta et al, 2017;Fan et al, 2022a;Fan et al, 2022b) and its interaction with structures (e.g., Zakeri et al, 2009;Zakeri and Hawlader, 2013;Liu et al, 2015;Fan et al, 2018;Fan et al, 2019;Qian and Das, 2019;Sahdi et al, 2019;Fan et al, 2020a;Guo et al, 2021;Guo et al, 2022), due to the numerical solution stability and speed advantages on the multi-phase coupled flow field. To reproduce the influence of submarine slides on submerged floating tunnels in the underwater environment, the ANSYS-CFX numerical software based on the CFD simulation approach is employed in this study.…”

Section: Cfd Simulation Approach and Setupmentioning

confidence: 99%

Feasibility of submerged floating tunnel with polygonal cross-sections — investigation from a viewpoint of submarine slide hazards

Fan

et al. 2023

Front. Mar. Sci.

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Recently, submerged floating tunnels have generated a lot of interest due to their unique cross-water traffic benefits. However, the destructive threat of submarine slide hazards was not fully considered in the design scheme of submerged floating tunnels, in particular to the feasibility of applying various cross-section forms on land to submerged floating tunnels under that hazard influence. This study mainly investigates the load effect of submerged floating tunnels with polygonal cross-sections (comprising three types: square, hexagon, and octagon) under the impact of submarine slides, via a computational fluid dynamics (CFD) approach. Results show that the impact forces produced by submarine slides on submerged floating tunnels are significant (e.g., submarine slides with a velocity of 4 m/s may produce a force level near 1×105 N/m), where the horizontal impact force components should be given priority consideration based on the general working environment of submerged floating tunnels. Compared with typical circle tunnels, polygonal tunnels suffer higher impact forces, and the polygonal types with fewer edges show a greater impact force. Finally, a simplified force evaluation approach for the submerged floating tunnel with polygonal cross-sections is proposed for guiding the relevant engineering design.

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“…The two components of pipeline risks are leaks and ruptures. The risk in the pipeline 40 can be monitored by using supervisory control and data acquisition systems, which collects data about the pipeline operations and transmit the data to engineers or technicians if some problem occurs. The security cameras and sensors, fibre-optic cables, and temperature sensors placed on or near the pipelines can also check for leakages and corrosion to prevent them from becoming dangerous.…”

Section: Related Terminologiesmentioning

confidence: 99%

Efficient qualitative risk assessment of pipelines using relative risk score based on machine learning

Vanitha,

Easwaramoorthy,

Krishna

et al. 2023

Sci Rep

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Pipelines are observed one of the economic modes of transport for transporting oil, gas, and water between various locations. Most of the countries in the world transport petroleum and other flammable products through underground pipelines. The underground and aboveground pipelines are facing various damages due to corrosion, dents, and ruptures due to the environment and operational fluid conditions. The danger of leaks and accidents increases as a result of these damages. Pipelines must be evaluated on a regular basis to make sure they are fit for transmission. By evaluating the effects of damages and the possibility of catastrophic failures using a variety of techniques, pipeline integrity is controlled. Applying the relative risk scoring (RRS) technique, pipeline failures are predicted. One of the probabilistic techniques used to forecast risk based on an impartial assessment is machine learning. With different parameters like corrosion, leakage, materials, atmosphere, surface, earth-movements, above-ground and underground facilities, etc., the RRS method provides an accuracy of 97.5% in identifying the risk and gives a precise classification of risk, whether the pipeline has a high, medium, or low risk without any delay on the prediction compared with Naive Bayes, decision tree, support vector machine, and graph convolutional network.

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Effect of pipeline surface roughness on peak impact forces caused by hydrodynamic submarine mudflow

Cited by 45 publications

References 52 publications

The Influence of Shear-Thinning Characteristics on Multiphase Pump Vortex Structure Evolution, Pressure Fluctuation, and Gas-Solid Distribution

The Influence of Shear-Thinning Characteristics on Multiphase Pump Vortex Structure Evolution, Pressure Fluctuation, and Gas-Solid Distribution

Feasibility of submerged floating tunnel with polygonal cross-sections — investigation from a viewpoint of submarine slide hazards

Efficient qualitative risk assessment of pipelines using relative risk score based on machine learning

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