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Enregistrement W2547756455 · doi:10.5339/qfarc.2016.eepp2378

CFD Simulations of Abnormal Flow in Horizontal Pipes for Offshore Hydrocarbon Transport

2016· article· en· W2547756455 sur OpenAlexaff
Ibraheem A. Adeoti, Mohammad Dadashzadeh, Mohmmad A. Rahman

Notice bibliographique

RevueQatar Foundation Annual Research Conference Proceedings Volume 2016 Issue 1 · 2016
Typearticle
Langueen
DomaineEngineering
ThématiqueOffshore Engineering and Technologies
Établissements canadiensMemorial University of Newfoundland
Organismes subventionnairesnon disponible
Mots-clésSlug flowMechanicsMultiphase flowVolume of fluid methodComputational fluid dynamicsFlow (mathematics)Pipeline transportTwo-phase flowGeologyGeotechnical engineeringEngineeringPhysicsMechanical engineering

Résumé

récupéré en direct d'OpenAlex

Introduction: Industrial pipelines for multiphase transportation can result in unstable flows which often cause major operational problems. Due to liquid wave growth and phase interactions (hydrodynamic slugs), liquid arriving in larger, intermittent chunks may cause flow instabilities in pipelines. At an increased air volumetric flow rate, the surface wave amplitudes become larger to the pipe/conduit and wave forms frothy slug where it touches the wall of the pipe. When the slugs travel at a velocity higher than average liquid velocity, it can cause severe vibration that could reduce the integrity of or damage equipment. In order to tackle the problems associated with slug flows, there is a clear need to better understand the multiphase flow leading to various flow phenomenon in the pipelines. The multiphase flows are characterized by flow patterns or regimes that define a particular distribution of phase volume fraction in pipeline. While there are several numerical models characterized the development and evolution of slugs and slug flows, studies which describe the stress analysis of these slug flows and their effects are scarce. This study compares two CFD codes (ANSYS CFX and FLUENT) in slug development in jumper and the stress analysis of slug movement in jumper. As well, the effects of flow parameters such as fluid superficial velocity, fluid density ratio, and viscosity on slug were investigated. The model considered in the present study is based on a quasi-3D formulation where the governing equations are based on volume averaging and ensemble averaging of Navier-Stokes equation. In present study, proposed benchmark relies on focusing on two CFD tools, FLUNT and CFX, to simulate surface instabilities and slugs on stratified flow in a horizontal channel considering slip, surface tension, and frictional momentum transfer between the phases (liquid and gas). FLUENT Set-up The setup mimics the modified version of the experimental study previously investigated by Vallee and Hohne (2007), the flow channel with rectangular cross-section was modelled using Computational Fluid Dynamics (CFD) package, FLUENT code. The dimension of the model are 4000 × 300 × 30 mm 3 (length × height × width). The simulation was performed by a grid consists of 4 × 462 hexahedral elements and 4 × 46152 nodes applying a quasi-3D model that consider the wall effect of channel in a 2D model. The volume-of-fluid (VOF) model is used for modeling the fluid domain with air and water. This model is well suited for separated flows with no mixing at the interface. The fluid interface shape is represented by geometric reconstruction scheme. For the two-phase flow, 1.0–1.5 m/s superficial velocity of water and 5.0–11.5 m/s of air were chosen for the CFD calculations. The model inlet was divided into two parts: in the lower half of the inlet cross–section, water was injected and in the upper half air. An initial water level of 50 mm was assumed for the entire model length. As well, initial inlet velocity 1 m/s was considered for water and air, and the velocity of air was increased gradually to simulate different scenarios until final velocity 11.5 m/s considered in this work. The reference pressure considered during the simulation was 1 bar and surface tension of 0.072 N/m. A hydrostatic pressure was also assumed for the liquid phase. For surface instability generation with subsequent slugs, the interfacial momentum exchange and turbulent parameters had to be modeled accurately (Razavi and Namin). In this regard, turbulent model of K-ε model was chosen as the viscosity that is able to model surface instabilities and turbulence of slug flow. Solution for calculating 15s of simulation time on 6-processors lasted for 48 hours. Selected discretization schemes were PRESTO for pressure, Geo-Reconstruction for volume fraction, and First Order Upwind for other cases. Variable time step between 10–6 and 10–3 was appropriate steps for the simulation. CFX Set-up Building the geometry in ICEM, the mesh was then imported to the ANSYS CFX-Pre in order to define the simulation parameters. Air and water were defined as the two gaseous and liquid phase and using the expressions, the height of water is set to 0.05 (half of the area section) through the entire domain. According to (Frank, 2005), Shear Stress Transport (SST) turbulence model was selected for the simulation and the term “Production and Dissipation” was added to the equations. Surface tension coefficient was set to the value of 0.072 (N/m), interface length scale to 1 (mm), and drag coefficient to 0.44 ( − ) (Frank, 2005). The mixture model was chosen for the interphase transfer. The inflow type was chosen as ‘inlet” and the fractional intensity was set to the value of 0.05 with the eddy length scale equal to the liquid height at the upstream (Razavi & Namin, 2011). The mass flow rate of air and water were set to the values of 0.074 (kg/s) and 7.83 (kg/s), respectively. Several simulations were conducted in order to improve the simulation results and due to the blockage of the outlet in the previous runs, the outflow boundary type was set to “opening” instead of “outlet” with a pressure controlled and medium intensity (5%) turbulence in the boundary details. The liquid and gaseous phases were defined based on their volume fraction in downstream at outflow. The wall boundary type was set to “wall” and for both phases “no slip wall” and “smooth wall” options were assigned to the mass and momentum and the wall roughness, respectively (Hohne, 2009). The analysis type was set to transient with the total simulation time of 8 (s) and time steps of 0.001 (s), according to the similar study conducted by (Razavi & Namin, 2011). In the solver control, a second order backward Eulerian approach was chosen with high resolution turbulence. Due to the instability and fatal errors in the previous simulations, the minimum and maximum number of loops were set to 1 and 200 (due to divergence problem), respectively with the convergence criteria of 1 × 10 –4 . Figure 1: Abnormal flow simulations (L = 4 m, D = 0.3m, Ug = 9 m/s and Ul = 1 m/s). References: S.Y. Razavi and M. M. Namin. Numerical Model of Slug Development on Horizontal Two-phase Flow, Proc. of Int. Conf. on Recent Trends in Transportation, Environmental and Civil Engineering 2011. A. Ashrafian, J-C. Barbier and S.T. Johansen. Quasi-3D Modeling of Two-Phase Slug Flow in Pipes. 9th International conference on CFD in the minerals and process industries CSIRO, Melbourne, Australia, 2012. T. Frank. 2005. Numerical Simulation of Slug Flow regime for an air-water two-phase flow in horizontal pipes, The 11th International Topical Meeting on Nuclear Thermal Hydrualics (NURETH-11), Avignon, France, 2006. R.E.M. Morales et al. 2013. A comprehensive Analysis on Gas-Liquid Slug Flows in Horizontal Pipes. Offshore Technology Conference, Brazil. OTC 24437. D. Duraivelan, Y. Dai and M. Agrawal 2013. CFD Modeling of Bubbly, Slug, and Annular Flow Regimes in Vertical Pipelines. Offshore Technology Conference. OTC 24245.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,695
Score d'incertitude au seuil0,752

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,027
Tête enseignante GPT0,293
Écart entre enseignants0,266 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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