CFD Modeling of Aerosol Transport and Deposition Using a Drift-Flux Model
Why this work is in the frame
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Bibliographic record
Abstract
Aerosol transport and deposition are important processes in modeling of accident scenarios for a small modular reactor. An aerosol drift-flux model is attractive because it is computationally less expensive than Lagrangian particle tracking. It must be determined, however, how well it performs when implemented in a commercial computational fluid dynamics (CFD) code. This work presents results of modeling aerosol transport and deposition using a full Eulerian three-dimensional drift-flux model implemented in the commercial CFD code STAR-CCM+. The forces due to gravity and thermophoresis are included in the present drift-flux model along with Brownian motion and turbulent diffusion. The forces are added as a source term to a passive scalar transport equation. In addition, a drift velocity representing the forces is used in a built-in electrochemical species transport equation. The results of these two approaches are compared. An appropriate deposition velocity is used to calculate the aerosol concentration deposited on surfaces. The semiempirical relation proposed by Lai and Nazaroff (2000) is used to compute the deposition velocity due to gravitational settling, and the present results are compared with the experimental and numerical data obtained from the work of Chen et al. (2006). It was found that the concentration profile obtained from the present drift-flux model showed reasonable agreement with the literature data. A thermophoresis model showed good agreement when compared with the analytical solution of Nazaroff and Cass (1987). In addition to the particle concentration results, this work presents details of the drift-flux model implementation and the bulk flows. These extra details will enable comparisons by others developing similar models.
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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) | 0.000 | 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 it