Modeling the Dispersion of a Tracer Gas in a Model Room: Comparison Between the Large Eddy Simulation Method and a Euler Approach
Bibliographic record
Abstract
Abstract In this paper, indoor tracer-gas concentration and temperature profiles obtained by a large-eddy simulation (LES) and an inviscid model based on Euler equations are presented. The numerical results from these two approaches are compared with experimental results, available in the literature, for both mixing jet and displacement ventilation strategies. The numerical results reveal that the ventilation strategy changes the dispersion of the tracer gas in the model room to a significant degree. Comparison between LES, Euler, and experimental data shows that the LES model can simulate the tracer-gas concentration and temperature distribution relatively well. Comparison between LES models, Euler, and experimental data show that the LES models simulate the concentration of the tracer gas and the temperature distribution reasonably well. The results of this model coincide well with the experimental results. The same finding was observed by the Euler model. Performance of this approach for evaluation of indoor air quality has been verified for two ventilation strategies. Differences from 3 to 4.7 % were observed between the Euler model and experimental results. The efficiency of ventilation obtained from the Euler model is almost identical to the experimental values with a calculation time less than that obtained by the LES model. The Euler model has its limits only when it comes to high velocities (near air outlets). Generally, a room's air flows are low so in this case, the Euler model can predict the concentration of pollutants effectively.
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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 | 0.005 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".