ORCID Profile
0000-0003-0827-2302
Current Organisation
Fondazione Bruno Kessler
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Publisher: Elsevier BV
Date: 04-2015
Publisher: Elsevier BV
Date: 02-2011
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2012
Publisher: Elsevier BV
Date: 10-2010
Publisher: Elsevier BV
Date: 2011
Publisher: ASMEDC
Date: 2008
Abstract: Offshore pipelines are exposed to hydrodynamic forces due to the current actions. The forces (drag and lift) have an unsteady nature and oscillate around their average values because of the occurrence of vortex shedding behind the pipeline. Vortex shedding leads to the Vortex-Induced-Vibration (VIV) which is an important source of fatigue damage of pipeline in free spanning sections. Time-averaged drag force can be calculated using drag equation which has a hydrodynamic coefficient. However it does not take the VIV effects into account. To do so and to consider the unsteady nature of the force, the flow pattern and vortex shedding phenomenon around a pipeline should be simulated accurately. The main objective of this study is to simulate the flow field around an offshore pipeline placed on the plane bed. This study investigates the gap ratio effects on the flow pattern and drag force exerted on the pipeline as well. The flow field was simulated by the two dimensional Reynolds-Averaged Navier-Stokes (RANS) equations with a k-ε turbulence closure model. Results indicate that the k-ε turbulence closure can simulate the vortex shedding behind a pipeline and the oscillation of drag force successfully. By considering different values of gap to diameter ratio, it is found that the drag coefficient is increased gradually as the gap ratio rises up and the increase rate reduces as gap ratio increases.
Publisher: IWA Publishing
Date: 09-11-2010
Abstract: Scour around pile groups is rather complicated and not yet fully understood due to the fact that it arises from the triple interaction of fluid–structure–seabed. In this study, two data mining approaches, i.e. Support Vector Machines (SVM) and Artificial Neural Networks (ANN), were applied to estimate the wave-induced scour depth around pile groups. To consider various arrangements of pile groups in the development of the models, datasets collected in the field and laboratory studies were used and arrangement parameters were considered in the models. Several non-dimensional controlling parameters, including the Keulegan–Carpenter number, pile Reynolds number, Shield's parameter, sediment number, gap to diameter ratio and number of piles were used as the inputs. Performances of the developed SVM and ANN models were compared with those of existing empirical methods. Results indicate that the data mining approaches used outperform empirical methods in terms of accuracy. They also indicate that SVM will provide a better estimation of scour depth than ANN (back-propagation/multi-layer perceptron). Sensitivity analysis was also carried out to investigate the relative importance of non-dimensional parameters. It was found that the Keulegan–Carpenter number and gap to diameter ratio have the greatest effect on the equilibrium scour depth around pile groups.
Publisher: Elsevier BV
Date: 10-2005
Publisher: Elsevier BV
Date: 02-2008
Publisher: Elsevier BV
Date: 04-2008
Publisher: Elsevier BV
Date: 04-2011
Publisher: IWA Publishing
Date: 09-2009
Abstract: Scour around submarine pipelines remains a largely complex and not yet fully understood problem. In this study, wave-induced scour around submarine pipelines was investigated. Since various physical processes occur during the development of a scour hole, the effects of each process were considered by employing several nondimensional parameters. To find the effective parameters on equilibrium scour depth, the correlation between independent parameters (e.g. Keulegan–Carpenter number) and dependent parameter (nondimensional scour depth) were determined using different experimental data. Then, an Artificial Neural Network (ANNs) approach was used to develop a more accurate model for prediction of wave-induced scour depth around submarine pipelines. ANN models with different input parameters including gap to diameter ratio, Keulegan–Carpenter number, pipe Reynolds number, Shields number, sediment Reynolds number and boundary layer Reynolds number were trained and evaluated to find the best predictor model. To develop the ANN models, both holdout and tenfold cross-validation methods were used. In addition, an existing empirical method was examined. Results show that the empirical method has a significant error in the prediction of scour depth for the cases with an initial gap between pipe and seabed. It is also indicated that the ANN models outperform the empirical method in terms of prediction capability.
Location: Iran (Islamic Republic of)
No related grants have been discovered for Mohammad Hossein Kazeminezhad.