Direct numerical simulation of a wall source dispersion in a turbulent channel flow
Bibliographic record
Abstract
A comprehensive direct numerical simulation (DNS) is conducted to analyze the turbulent mixing process of a surface source dispersion emitting from the bottom wall in a turbulent channel flow. A comparative analysis of ten test cases has been conducted to examine the turbulent mixing process and its relationship with the source strength. The study involves an examination of the dispersion process in both physical and spectral spaces, which includes an evaluation of statistical moments of the concentration field, pre-multiplied spatial spectra of the velocity and concentration fields, as well as an analysis of the coherent turbulent structures in the flow. Upon normalization by friction concentration, the DNS analysis reveals that the first- and second-order statistical moments of the concentration field remain unaffected by variations in the source strength. Conversely, the relative intensity of concentration fluctuations and the thickness of the concentration boundary layer exhibit significant susceptibility to variations in the source strength. It has been observed that the dispersion process within the channel is significantly influenced by the small-scale eddies in cases where the source strength is not particularly intense. When confronted with high source strength scenarios, analysis of the simulation results establishes that both small- and large-scale eddies are crucial players in the dispersion phenomena, as evinced by a comparison of the pre-multiplied spectra of concentration and velocity fields. Furthermore, hairpin vortices have been recognized as the primary source of turbulence in the dispersal of substances from regions of high shear to the center of the channel.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".