CFD and experimental approach on three phase gas-liquid-solid Newtonian fluid flow in horizontal pipes
Bibliographic record
Abstract
This study analyses three dimensional fluid flow through horizontal pipelines with three-phase gasliquid-solid Newtonian fluids by Computational Fluid Dynamics (CFD) simulation. Validating the simulation with experimental data, the study aims to develop a versatile acceptable simulation model that can be used further for different applied cases. An experimental setup is developed in our laboratory to determine slug flow (air-water) through a horizontal pipeline. Air as gas, water as liquid and silica as solid particle is used in this work. ANSYS Fluent version 16.2 is employed to perform the simulation. The Eulerian multiphase model with the Reynolds Stress Model (RSM) turbulence closure is adopted to analyse multiphase fluid flow. Parameters are selected from experimental works to validate the simulation. After a good agreement with experimental data, sensitivity analysis is conducted to observe the three phase fluid flow characteristics through horizontal flow. Pressure gradient (pressure drop per unit length) and in situ concentration profile are used as primary parameters. This article provides a clear relationship between the different parameters of three-phase fluid flow through a horizontal pipeline.
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.
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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".