Computational Fluid Dynamics Modeling of Subsea Pipeline Leaks in Arctic Conditions
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Résumé
Abstract The purpose of this study is to investigate subsea pipeline leaks and their impact on the surroundings. A numerical approach using a computational fluid dynamics (CFD) package is used. The subsea condition is extremely harsh due to the remoteness and inaccessibility. Marine pipeline can be damaged directly by contact with drifting sea ice. Trenched pipeline is at risk as well, as it may be damaged by corrosion, or the pipeline could be plastically deformed by the resulting seabed shake down event. Furthermore, due to the remoteness and harsh climate of the under the ocean, it is difficult to conduct normal maintenance procedures. Leakage of pipelines in arctic subsea environment can have severe consequences. Leak detection and location identification in a timely manner is crucial because of the economic impact of a hydrocarbon spill to its stakeholders can be huge. Pipeline leakage could have an adverse impact on life, the environment, the economy and corporate reputation. It is imperative to take additional precautions while operating in the subsea regions, so rapid leak detection and location identification is crucially important. In this paper, a numerical modeling of a subsea pipeline leakage is performed using a 3-D turbulent flow model in computational fluid dynamics (CFD). Four different types of fluids are tested in this study, with specified operating conditions. It is difficult to conduct small-scale experiments on subsea pipeline with leakage, mainly because; the pipeline may need to release hydrocarbons to the environment. Further, since the industrial full-scale pipeline is large in diameter, fluid thermodynamics cannot be captured accurately in a small-scale, laboratory environment. Thus, a numerical simulation can provide a better understanding of pipeline internal flow and the consequences of pipeline leaks in different scales, reducing the cost and number of experiments. Commercially available ANSYS (FLUENT) computational fluid dynamics software is used to serve this purpose. ANSYS workbench provides integrated design, meshing technology, and large degree of freedom for pre- and post-processing for the fluid flow simulation in pipeline. The CFD simulation results in this study showed that the flow rate of the fluid escaping from the leak increases with pipeline operating pressure. The static pressure and pressure gradient along the axial length of the pipeline have observed a sharp signature variation near the leak orifice. This signature has been captured using pressure gradient curves. The temperature profiles near leak orifice indicate that the temperature is observed to increase slightly in the case of incompressible fluids; however, temperature drops rapidly for the compressible fluids. Transient simulation is performed to obtain the acoustic signature of the pipe near leak orifice. The power spectral density (PSD) signal is strong near the leak orifice and it dissipates as the distance and orientation from the leak orifice increase. The high-pressure fluid flow generates more noise than the low-pressure fluid flow. In order to model the turbulence large eddy simulation (LES) used and Ffowcs-Williams and Hawking (FW-H) model in FLUENT was activated to generate acoustic data. Time step of the simulation was selected At = 0.0005 s and the number of simulation 20000 to get higher frequency noise signal.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle