Numerical Investigation of Three-Dimensional Laminar Wall Jet of Newtonian and Non-Newtonian Fluids
Bibliographic record
Abstract
Three-dimensional laminar wall jets of a Newtonian fluid and two shear-thinning non-Newtonian fluids were numerically investigated. The complete nonlinear incompressible Navier-Stokes equation was solved using a colocated finite volume based in-house computational fluid dynamics code. For each fluid, the computation was performed at three Reynolds numbers. The results showed that the streamwise velocity profiles for the Newtonian fluid became self-similar but the more shear-thinning fluid never achieved a self-similar condition. Significant differences were observed among the profiles for the various fluids in the inner region. Although the transverse and spanwise components of the velocity decreased substantially with increasing Reynolds number, the values for the non-Newtonian fluids were generally an order of magnitude larger than the corresponding values for the Newtonian fluid. Depending on the specific fluid and Reynolds number, the apparent viscosities were up to 4 orders of magnitude higher than the dynamic viscosity of water. Consequently, the spread of the jet in both the transverse and spanwise directions, decay of the maximum streamwise velocity, and the skin friction coefficient depend strongly on both Reynolds number and nature of the fluid. The results also show that the jet half-width in the transverse direction is significantly higher than in the spanwise direction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".