A variable turbulent Schmidt number formulation by numerical simulation of atmospheric plume dispersion
Bibliographic record
Abstract
Turbulent Schmidt number as an important parameter in computational fluid dynamic (CFD) simulations is strongly dependent on height, whereas it is mostly considered to be constant in the literature. This paper presents a new variable turbulent Schmidt number formulation which can calculate the relative concentrations (RCs) in neutral atmospheric conditions more accurately. To achieve this aim, RCs from continuous releases are calculated in different distances by the analytical Gaussian plume mode. CFD simulations are carried out for single stack dispersion on a flat terrain surface and an inverse procedure is then applied so that different turbulent Schmidt numbers are used as inputs to determine the RCs to select the “best-fit” turbulent Schmidt number value. This process is continued for different heights to fit a curve to obtain the new formulation for turbulent Schmidt number varying with height. The values are compared with experimental results. The comparison indicates that the new formulation for turbulent Schmidt number is more accurate and reliable than previous research works.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".