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Record W2009589396 · doi:10.4043/24114-ms

Advances in Multiphase Flow CFD Erosion Analysis

2013· article· en· W2009589396 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOffshore Technology Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsIntecsea (Canada)
Fundersnot available
KeywordsComputational fluid dynamicsMultiphase flowErosionPetroleum engineeringFlow (mathematics)MechanicsSubseaEnvironmental scienceMarine engineeringGeologyGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract One of the flow assurance challenges in subsea production systems is the occurrence of erosion damage due to the existence of sand particles and high production Gas Oil Ratio (GOR) as such erosion mostly occurs in highly gas dominated operating conditions in the annular flow regime. The erosion rate for an elbow with a constant flow velocity and with all other factors equal is higher in gas systems than liquid systems as more particles will impact on the inner wall of the outer curvature of the elbow. The maximum wear location and the penetration rate for multiphase flows are often an intermediary of gas and liquid systems occurring at 55 degrees from the inlet of the elbow, however this depends heavily on the multiphase flow regime. A challenge facing industry is availability of erosion prediction models; the majority of available models are based on single-phase liquid or gas as the carrying medium. This can result in large discrepancies in erosion rates and potentially increased wall thickness, fabrication and subsequent intervention costs. To predict the flow regime in greater clarity requires the use of computational power and / or instrumentation that can accurately characterize the flow within the pipes. Since experimental work is costly and unlikely to be representative of a large integrated production system, Computational Fluid Dynamics (CFD) is used to perform erosion assessments and can also aide in corrosion prediction and inhibitor selection. Only erosion assessments by CFD methods are discussed in detail within this paper. CFD has been extensively applied for erosion analyses; it is commonly used for identifying potential failure locations, improving understanding of failure mechanisms and only qualitatively used for erosion rates. CFD erosion modelling capability in this paper has been enhanced by simulating flow regime characteristics, in particular the liquid film for annular flow. This benefits the simulation to obtain greater accuracy for sand particle impact angles, area, speed and thus the erosion rate is significantly enhanced. In addition, the local volume fraction of sand has been considered in order to accurately evaluate the impact force. The research to date shows that a promising agreement is obtained between predicted erosion rate and the empirical predictions (Salama, Salama & Venkatesh and DNV RP-501 methods). Further comparisons to empirical model predictions are carried out to address the importance of flow regime on the results as current empirical models lack this consideration. The influence of the flow orientation (upwards and downwards flow), has also been investigated in this work due to current lack of publically available data. The paper presented hereafter illustrates that considerable difference in flow orientation is revealed and the prediction can be improved by considering the flow characteristics. An example is provided highlighting the use of liquid film and droplet velocity to replace the mixture velocity implemented in empirical models for annular flow. All of the findings of this work are aimed at providing assistance to industry not only performing the qualitative but quantitative CFD erosion analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.207
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it