Buoyancy‐corrected <i>k</i>–ε models and large eddy simulation applied to a large axisymmetric helium plume
Bibliographic record
Abstract
Abstract The present numerical study is focused on testing two different modeling approaches to simulate a large turbulent buoyant helium plume, in particular the near‐field region. First, buoyancy‐corrected k–ε models are applied in Reynolds‐averaged Navier–Stokes (RANS) calculations, then large eddy simulation (LES) using a standard Smagorinsky model is examined. Good results are produced using the buoyancy‐corrected models, in particular, excellent agreement is achieved for the radial profiles of the streamwise velocity. However, the predictions are very sensitive to the choice of the buoyancy constant, C3ε, in the models. The present LES calculations show that the puffing frequency is accurately predicted. Predictions for the time‐averaged velocities are within experimental uncertainty at all locations. The predicted plume concentrations are in good agreement at the base of the plume, but the centerline values are overpredicted farther downstream. The higher‐order statistics are best predicted with the finest mesh. A sensitivity analysis on grid refinement, values of the Smagorinsky constant and the Schmidt number are included. Copyright © 2008 John Wiley & Sons, Ltd.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".