Investigation on Erosion Due to Fine Particles in Multiphase Flow
Bibliographic record
Abstract
Pipeline production system often experiences complex multiphase flow and entrained fine-particles. The erosion due to solid fine particles presents one of the greatest threats to oil and gas flow assurance and consequently impacting material selection and wall thickness design. Limited literature is available on erosional effect caused by submicron particles such as fine sand, abrasive solid materials or gas bubbles. Previous studies on particle erosion are limited to particle size greater than 100 microns in single phase fluid flow. This is based on the assumption that potential for erosion by particle size smaller than 100 microns (specifically lesser than 62.5 microns) is insignificant. Additionally, very few studies have addressed the combined effect of erosion caused by micro-sized particles and multiphase flow. Most predictive erosion models are limited to single phase flow for model simplification purposes. Hence, the effects of multiphase flow and its interaction with sand particles, specifically fine solids, are neglected. Therefore an in-depth understanding of multiphase flow regimes and its interaction with micro-sized particles is an important enabler for more accurate erosion prediction. For more accurate flow modeling and erosion characterization, computer fluid dynamics (CFD) tool is required. In this study, Multiphase CFD (MCFD) is implemented for predicting micro-fine erosion, considering two phase flow pattern features. Concurrently, trajectories of fine particles' bombardment on the pipe inner wall surface are captured using Lagrangian Particle Tracking Model. Analyses are carried out for water and gas flow at isothermal conditions, covering various particle size lesser than 62.5 microns in order to determine material removal rate. The results will be benchmarked against Tulsa multiphase erosion model prediction. Based on the results, it is concluded that the erosional effect caused by micro-sized particles is strongly dependent on the flow patterns in the pipe, determined by superficial velocities of each phase. Additionally, erosional impact or material removal rate is predicted, which though small, is expected to significantly impact material design. The presence of these micro-sized particles acts as an enabler, which produces homogeneous "pits" on the surface of metal, significantly increasing the contact surface area for chemical and mechanical interactions to take place. The results from the proposed modeling using MCFD are expected to benefit erosion impact assessment in multiphase hydrocarbon production and piping systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".