CFD Modeling of Aerosol Transport and Deposition Using a Drift-Flux Model
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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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".