scholarly journals Propulsion simulation on fully appended ship model

Author(s):  
Andreea Mandru ◽  
Liliana Rusu ◽  
Florin Pacuraru

This study presents the numerical investigation for the flow around the propeller of the ONR Tumblehome combatant in open water and for the flow around the same ship in the case of self-propulsion with actuator disk method. Computational Fluid Dynamics based on RANS-VOF solver have been used in order to analyse the flow. The free surface treatment is multi-phase flow approach, incompressible and nonmiscible flow phases are modelled through the use of conservation equations for each volume fraction of phase. Accuracy involves close attention to the physical modelling, particularly the effects of turbulence, as well as the numerical discretization.

Author(s):  
Mohamed Odan ◽  
Faraj Ben Rajeb ◽  
Mohammad Azizur Rahman ◽  
Amer Aborig ◽  
Syed Imtiaz ◽  
...  

Abstract This paper investigates issues around four-phase (Oil/CO2/water/sand) flows occurring within subsea pipelines. Multi-phase flows are the norm, as production fluid from reservoirs typically include sand with water. However, these multi-phase flow mixtures, whether three- or four-phase, are at risk of forming slug flows. The inclusion of sand in this mixture is concerning, as it not only leads to increased levels of pipeline erosion but it also has the potential, to accumulate sand at the bottom of the pipe, blocking the pipe or at the very least hindering the flow. This latter impact can prove problematic, as a minimum fluid velocity must be maintained to ensure the safe and regulated flow of particles along a pipeline. The presence of low amounts of sand particles in oil/gas/water flow mixtures can serve to reduce the pressure exerted on bends. The sand volume fraction must in this case, be relatively low such that the particles’ resistance causes only a moderate loss in pressure. Therefore, the study aims to gauge the impact of oil/gas/water/sand mixtures on various pipeline structures as well as to further investigate the phenomenon of flow-induced vibration to determine the optimal flow variables which can be applied predicting the structural responses of subsea pipelines.


Author(s):  
Mohamed Odan ◽  
Faraj Ben Rajeb ◽  
Mohammad Azizur Rahman ◽  
Amer Aborig ◽  
Syed Imtiaz ◽  
...  

Abstract This paper investigates issues around four-phase (Oil/CO2/water/sand) flows occurring within pipelines. Multiphase flows are the norm, as production fluid from reservoirs typically include sand with water. However, these multi-phase flow mixtures, whether three- or four-phase, are at risk of forming slug flows. The inclusion of sand in this mixture is concerning, as it not only leads to increased levels of pipeline erosion but it also has the potential, to accumulate sand at the bottom of the pipe, blocking the pipe or at the very least hindering the flow. This latter impact can prove problematic, as a minimum fluid velocity must be maintained to ensure the safe and regulated flow of particles along a pipeline. The presence of low amounts of sand particles in oil/gas/water flow mixtures can serve to reduce the pressure exerted on bends. The sand volume fraction must in this case, be relatively low such that the particles’ resistance causes only a moderate loss in pressure. Therefore, the study aims to gauge the impact of oil/gas/water/sand mixtures on various pipeline structures as well as to further investigate the phenomenon of flow-induced vibration to determine the optimal flow variables which can be applied predicting the structural responses of pipelines.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Wook Kim ◽  
Seong-Hoon Kang ◽  
Se-Jong Kim ◽  
Seungchul Lee

AbstractAdvanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.


2020 ◽  
Vol 76 ◽  
pp. 103187
Author(s):  
C.R. Clarkson ◽  
B. Yuan ◽  
Z. Zhang ◽  
F. Tabasinejad ◽  
H. Behmanesh ◽  
...  

2016 ◽  
Vol 13 (02) ◽  
pp. 381-415
Author(s):  
Debora Amadori ◽  
Paolo Baiti ◽  
Andrea Corli ◽  
Edda Dal Santo

In this paper we study the flow of an inviscid fluid composed by three different phases. The model is a simple hyperbolic system of three conservation laws, in Lagrangian coordinates, where the phase interfaces are stationary. Our main result concerns the global existence of weak entropic solutions to the initial-value problem for large initial data.


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