Newtonian and non-Newtonian two-phase flow in complex pipelines
Bibliographic record
Abstract
Two-phase gas/non-Newtonian fluid flow through pipes commonly occurs in many industrial applications, such as in the chemical industry and oil and gas refineries. Many fluids used in oil and gas industries display non-Newtonian behaviour. Their rheology strongly affects pressure drop, flow structure, interface fluctuation, void fraction, heat transfer, and other flow features. In fact, non-Newtonian systems are not governed by the Newtonian law of viscosity. However, little experimental work has been devoted to studying non-Newtonian flow behaviour. This present work has conducted an experimental investigation to understand the fundamental physics of non-Newtonian two-phase (gas/ liquid) flow through pipes. In the experiments, several concentrations of Xanthan Gum were used as the non-Newtonian liquid, and both compressed air and carbon dioxide were used as gases. The flow rate and pressure of liquid and gas are changed by using a pump placed ahead of the mixing point. Pressure values are recorded by pressure sensors fixed at specific points along the pipe loop, and more than 10,000 experimental data points have been collected. The Experimental data analysis went through four categories. The first part is to calibrate all pressure sensors. The second step was applying rheology tests on our non-Newtonian Fluid to define the fluid behaviour and estimate the fluid properties. The third goal is to develop a new model for estimating pressure drop for the gas/non-Newtonian flow. The last part of the experiments is void fraction measurements. Pressure drop is one of the most challenging concerns related to industrial process design. In fact, multiphase flow process design depends on a better understanding of multiphase flow regimes. Numerous research has been done on gas/Newtonian liquid flow in horizontal and vertical pipes in the past few decades. Still, only a few research studies have been carried out to identify gas/non-Newtonian flow regimes. [1]. Creating a new model to estimate void fraction for gas-non-Newtonian flow is another objective done in the present work. The void fraction is considered one of the most important flow parameters used to describe two-phase flows in tubes. Void fraction is an important process variable for the volume and mass calculation required to transport gas-liquid mixture in pipelines, storage in tanks, metering, and custody transfer. Furthermore, it is important to determine other flow parameters such as two-phase density and viscosity or the average velocities of gas and liquid mixture. In addition, it plays a significant role in estimating and modelling two-phase pressure drop and flow pattern transitions. The created void fraction model is used to determine constants of general form posted by Butterworth[2]. Both developed void fraction models give a good estimation for the void fraction with about 5% percent errors compared with our experimental results and other available literature experimental results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".