Bibliographic record
Abstract
Insights into particle transport and deposition process in impinging jet flow, in light of the available experiments, can be gained using computational fluid dynamics (CFD) numerical simulations.The present thesis focuses mainly on the prediction of aerosol particle transport and deposition in impinging jet flow.An extensive literature survey has indicated that the present work represents the first comprehensive investigation of aerosol particle transport and deposition, using Reynolds averaged Navier Stokes/eddy interaction model (RANS/EIM) along with near-wall corrections, and large eddy simulation (LES) numerical approaches applied to particle-laden impinging jet flow.A new in-house tracking code for particle-laden impinging jet flow using modified EIM, as well as modified EIM in conjunction with the near-wall correction technique based on impinging jet flow characteristics was developed to simulate the particulate phase.Two different approaches in the framework of RANS method, RANS SST (shear stress transport) and RANS RSM-BSL (Reynolds stress transport-Baseline) model were used to simulate the fluid phase in three cases of nozzle-to-surface distances of L/D = 2, 4 and 6.Also, to better understand the applicability and accuracy limits of different numerical methods on aerosol particle deposition, one representative case for an impinging jet flow of L/D = 2 was performed using LES.Deposition results without near-wall correction, with turbulent tracking, showed unrealistic behavior at the beginning of the wall jet region and close to the stagnation point.Once the normal-to-wall fluctuating velocity, which plays important role for particle deposition on the impingement wall, was properly modeled via the near-wall iii correction technique, significant improvements were obtained when compared to the previous experiments, for all L/D cases.However, the results showed that RANS RSM-BSL/modified EIM, in conjunction with the near-wall correction, have better performance in predicting the deposition results.Particle deposition results for L/D = 2 showed that LES is in closer agreement with previous experimental data more than RANS RSM-BSL/modified EIM along with near-wall correction.These results provide new insight into the general behavior of the aerosol particle transport and deposition process in impinging jet flow.who offered me valuable guidance, advice and encouragement during my research, and also in my career life.Working with him has been very interesting and beneficial.Also, I want to thank my colleagues for their
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".