DNS study of dual-plume interference in a wall-bounded turbulent flow
Bibliographic record
Abstract
Turbulent mixing of dual plumes emitting simultaneously from line sources in a turbulent boundary layer has been studied using direct numerical simulation (DNS). A comparative study of three test cases has been conducted to investigate the effects of the source separation on turbulent mixing of two plumes. The dispersion and interference of dual plumes are investigated in both physical and spectral spaces, which include the analyses of turbulence statistics of the concentration field, cross-correlation between the two fluctuating plumes, pre-multiplied spectra of the velocity and concentration fields, and pre-multiplied co-spectrum of the dual plume fluctuations. As the downstream distance from the line sources increases, the plume development transitions from a turbulent convective stage to a turbulent diffusive stage. It is observed that plume released from a ground-level source reaches the turbulent diffusive stage faster than that from an elevated source. It is also observed that a smaller separation between two line sources results in a larger negative correlation coefficient between the two plumes and faster mixing of two fluctuating plumes. The pre-multiplied co-spectrum shows that even for a test case with the largest source separation, two fluctuating plumes quickly reach a complete mixing state downstream of the lines source such that they fluctuate as one single plume.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".