Aerosol Deposition Measurements as a Function of Reynolds Number for Turbulent Flow in a Ninety-Degree Pipe Bend
Bibliographic record
Abstract
An experimental and numerical investigation of the effect of the Reynolds number (Re) on the deposition of aerosol particles in a 90° pipe bend for turbulent flow was performed. Deposition fraction data were measured for a range of Stokes numbers (Stk) at different flow Re (10,250, 20,500, and 30,750) higher than those of most previous studies where Re was ⩽10,000. The data show good agreement with previous studies for Stk > 0.4, demonstrating that increased Re does not significantly alter the trend of deposition fraction with Stokes number (Stk) in this range. However, a noticeable increase in deposition was detected for 0.1 ⩽ Stk ⩽ 0.4. At Stk = 0.15, an increase in Re from 10,250 to 30,750 caused a factor of 2.6 increase in deposition fraction from 0.14 to 0.36. Numerical simulations were completed, using the Reynolds Averaged Navier-Stokes (RANS) equations with the Shear Stress Transport turbulence model. Modeling with inertial impaction only (i.e., neglecting turbulent dispersion), the results accurately reproduced the general trends seen in the experimental data; however, they failed to detect the Re effect at low Stk seen experimentally. The inclusion of turbulent particle tracking in the RANS simulation via an eddy interaction model did not improve the results. However, an analytical analysis of the particle tracking equation drawing data from the numerical results, showed that the experimentally observed effect of Re at low Stk can be attributed to damped particle response to velocity fluctuations at the eddy frequency scale.
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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 | 0.000 |
| 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) | 0.001 | 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".