Characterization of Time-Averaged Turbulence Statistics for Shear Thinning Fluid in Horizontal Concentric Annulus Using RANS Based CFD Simulation
Bibliographic record
Abstract
A RANS based shear stress transportation (SST) model was employed in this study to validate experimental results from a recent literature, which investigated the fully developed turbulent flow for a non-Newtonian shear thinning fluid, containing drag reduction polymer additives in a horizontal concentric annulus (inner to outer radio θ = 0.4). The polymer concentration varied from 0.07% V/V to 0.12% V/V and three mass flow rates from 3.92 kg/s to 5.95 kg/s were analyzed. The viscous property of the fluid was modeled by the power-law model. Simulation performed with the commercial code of ANSYS-CFX indicated that the SST model with default model constants overestimated the turbulence statistics of shear thinning flow in the near wall region where y+<60. As an effort to improve simulation accuracy, one of the model constants α1 was tuned in this study for the first time. Simulation results obtained from the modified model showed better agreement with experimental data compared to those from the default one. The present study represents a successful benchmark task for simulating turbulent shear thinning flow in concentric annuli with modified turbulence model constants.
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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 | 0.000 | 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 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".