The Equivalent Depth of Wave-Induced Scour Around Offshore Pipelines

Mousavi, Mir Emad; Bakhtiary, Abbas Yeganeh; Enshaei, Nastaran

doi:10.1115/1.3058681

Cited by 13 publications

(4 citation statements)

References 3 publications

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“…Mousavi et al [27] proposed the following equation in live bed condition, based on their experiments for the wave-induced scour depth around pipelines:…”

Section: Scour Around Submarine Pipelinesmentioning

confidence: 99%

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Prediction of wave-induced scour depth under submarine pipelines using machine learning approach

Etemad‐Shahidi¹,

Yasa²,

Kazeminezhad³

2011

Applied Ocean Research

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The scour around submarine pipelines may influence their stability; therefore scour prediction is a very important issue in submarine pipelines design. Several investigations have been conducted to develop a relationship between wave-induced scour depth under pipelines and the governing parameters. However, existing formulas do not always yield accurate result due to the complexity of the scour phenomenon. Recently, machine learning approaches such as Artificial Neural Networks (ANNs) have been used to increase the accuracy of the scour depth prediction. Nevertheless, they are not as transparent and easy to use as conventional formulas. In this study, the waveinduced scour was studied in both clear water and live bed conditions using M5' model tree as a novel soft computing method. M5' model is more transparent and can provide understandable formulas. To develop the models several dimensionless parameter such as gap to diameter ratio, Keulegan-Carpenter number and Shields' number were used. The results show that the M5' models increase the accuracy of the scour prediction and that Shields' number is very important in the clear water condition. Overall, results illustrate that the developed formulas could serve as 2 a valuable tool for the prediction of wave-induced scour depth under both live bed and clear water conditions.

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“…Mousavi et al [27] proposed the following equation in live bed condition, based on their experiments for the wave-induced scour depth around pipelines:…”

Section: Scour Around Submarine Pipelinesmentioning

confidence: 99%

“…They found that ANN-based model is more accurate than the previous empirical equations. Hereafter, Lucassen [6], Sumer and Fredsøe [7], Pu et al [8] and Mousavi et al [27] are referred to as Lu, SF, Pe and Me, respectively.…”

Section: Scour Around Submarine Pipelinesmentioning

confidence: 99%

Prediction of wave-induced scour depth under submarine pipelines using machine learning approach

Etemad‐Shahidi¹,

Yasa²,

Kazeminezhad³

2011

Applied Ocean Research

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“…Flow around pipelines in a steady current and under waves has been investigated extensively in the past few decades. Some of these studies are; Chao and Hennesy (1972), Kjeldsen et al (1973), Bijker and Leeuweistein (1984), İbrahim andNalluri (1986), Mao (1986), Sumer and Fredsøe (1990), Sumer and Fredsøe, (1996), Klomp et al (1996), Cevik and Yuksel (1999), Mousaviet et al (2009), Yasa (2011) and Kızıloz et al (2013). Numerical methods and other computing tools have been developed to simulate scour around submarine pipelines by Liang and Cheng (2005a), Liang and Cheng (2005b).…”

Section: Introductionmentioning

confidence: 99%

Assesment of Estimation Models for Scour Around Pipelines Under Irregular Waves

Kizilöz¹,

Çevik

Yüksel

et al. 2014

Int. Conf. Coastal. Eng.

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This study focuses on scour around submarine pipelines exposed to normal-incidence irregular waves over a live bed in shoaling region. The Artificial Neural Network (ANN) method was applied. The models of this study were compared to those of Kızıloz et al. (2013) who used multiple regression analysis method. Two wave characteristic pairs Hs Tm and Hrms Tp identify the sea state of irregular wave train as the representative wave parameter pairs of parametric model for scour process. Because these pairs result the same scour depth as regular waves do. The best ANN models for regular and irregular waves were obtained with Hs0Tm0dD or Hs0Lm0dD input parameters considering deep water wave parameters and HsTmdD or HsLmTmD input parameters considering local wave parameters. The mean wave period, Tm, and the significant wave height, Hs, representing the irregular waves are more compatible with the wave period and wave height of regular waves, respectively.

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“…In the past decades, several experimental studies on wave-induced scour beneath pipelines have been carried out (e.g. Mao 1986;Sumer and Fredsøe 1990;Mousavi et al 2009;Xu et al 2010). Sumer and Fredsøe (1990) pointed out that the equilibrium scour depth for live bed conditions is governed by Keulegan-Carpenter number, KC, defined as:…”

Section: Introductionmentioning

confidence: 99%

Two-Phase Simulation of Wave-Induced Tunnel Scour beneath Marine Pipelines

et al. 2012

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An Eulerian two-phase flow model was presented and employed to investigate waveinduced tunnel scour beneath a marine pipelines. The model is based on the Euler-Euler coupled governing equations for the fluid and sediment phases, i.e. time-averaged continuity and momentum equations were solved for both phases with a modified k-ε turbulence closure for the fluid phase. Fluid-particle, particle-particle and fluid-structure interactions were implemented in the simulation. The model accounts for the interphase momentum exchange by considering the drag, lift and added mass forces. The flow model was validated against an oscillatory flow around an isolated cylinder and a cylinder close to a rigid wall. The two-phase model was also validated against an

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The Equivalent Depth of Wave-Induced Scour Around Offshore Pipelines

Cited by 13 publications

References 3 publications

Prediction of wave-induced scour depth under submarine pipelines using machine learning approach

Prediction of wave-induced scour depth under submarine pipelines using machine learning approach

Assesment of Estimation Models for Scour Around Pipelines Under Irregular Waves

Two-Phase Simulation of Wave-Induced Tunnel Scour beneath Marine Pipelines

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