Experimental and Numerical Analysis of the Effect of Rheological Models on Measurements of Shear-Thinning Fluid Flow in Smooth Pipes
Bibliographic record
Abstract
The aim of this research is to investigate the effects of rheological models of shear-thinning fluids and their estimated parameters on the predictions of laminar, transitional, and turbulent flow. The investigation was carried out through experimental and computational fluid dynamics (CFD) studies in horizontal pipes (diameters of 19.1 mm and 76.2 mm). Six turbulent models using Reynolds averaged Navier–Stokes equations in CFD_ANSYS Fluent 19.0 were examined in a 3D simulation followed by comparison studies between numerical and experimental results. Regarding results of laminar regions in power-law rheology models, Metzner and Reed presented the best fit for the pressure loss and transitional velocity. For the turbulent region, correlations observed by Wilson and Thomas as well as Dodge and Matzner had good agreement with the experimental results. For Herschel–Bulkley fluids, pressure losses and transitional regions based on a yielded region were examined and compared to the experimental results and the modified Slatter Reynolds number, where the results provided good estimation. For both pipe diameters, the Slatter model was the best fit for pressure losses of Herschel–Bulkley fluids in the turbulent regime. Furthermore, when comparing k-omega and k-epsilon turbulence models to the power-law behaviour, numerical studies delivered the most accurate results with fluids that have a higher behaviour index. However, the error percentage significantly increased at a higher shear rate in the Herschel–Bulkley fluids with a greater yield stress effect. Moreover, the modified Herschel–Bulkley viscosity function by Papanastasiou was implemented in the current CFD study. This function was numerically stabilized, devoid of discontinuity at a low strain rate, and more effective in transitional regions.
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 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".