Modeling Turbulent Flow of Non-Newtonian Fluids Using Generalized Newtonian Models
Bibliographic record
Abstract
Computational Fluid Dynamic (CFD) is used to model turbulent flow of non-Newtonian polymeric fluids in concentric annulus. The so called Generalized Newtonian Fluid (GNF) approach is used. Four turbulence models are tested. Applicability of each model in predicting turbulent flow of non-Newtonian fluids in annulus is assessed by comparing results of pressure loss and or velocity profiles with experimental data. The first tested model is a modified version of Lam-Bremhorst k–ε turbulence model. The modification was originally developed to model flow of power law fluids in smooth circular pipes. Results of simulation study showed that this model significantly overestimates the pressure losses. Two k–ε closure type turbulence models, one developed to model turbulent flow of Herschel-Buckley and the other for power law fluids, are shown to fail in predicting turbulent flow of polymer solutions. One of the models contains a damping function which is analyzed to show its inadequacy in damping the eddy viscosity. The last tested model is a one layer turbulence model developed for predicting turbulent flow in annular passages. The model has an adjustable parameter, which is shown to control the slope of velocity profiles in the logarithmic region. It is demonstrated that if the model constant is selected carefully, the model accurately predicts pressure loss and velocity profiles.
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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".