CFD and Experimental Studies of Yield Power-Law Fluids in Turbulent Pipe Flow
Bibliographic record
Abstract
The transport of Non-Newtonian fluids through pipelines and mud circulation in wellbores often occur in turbulent flow regimes. In this study, experiments and computational fluid dynamics (CFD) models are used to examine the influence of yield power law (YPL) fluid rheological properties on pressure loss in the flow loop in turbulent flow. Three Non-Newtonian fluids at different concentrations of Xanthan gum solutions (0.05%, 0.10% and 0.15%, by weight) are studied at flow rates ranging between 400 and 800 L/min. A fully instrumented flow loop system was used, consisting of three main sections of different inclinations: 5 m long horizontal, 5 m vertical, and 3 m inclined 45° test section. Additionally, CFD codes of ANSYS CFX 17.2 are examined and compared to experimental results. These models are based on the Reynolds Averaged Navier-Stokes (RANS) equations. The comparison is done with the results of these investigations, based on vertical and horizontal pipe frictional pressure drops. The results show that the gap between experimental and CFD models has been increased in comparison with increase concentration Xanthan gum solution at the same density of fluids. Specifically, pressure loss rises with rises in the consistency index, k and flow behaviour index, However, rises in yield stress τ0 showed less impacts on frictional pressure losses. Given these simulation outcomes, it is clear that pressure drop in the Non-Newtonian fluid in one phase flow can be more accurately predicted by used the Reynolds-Stress Models (RSM) more than Eddy-viscosity models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".