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Record W4255031183 · doi:10.22215/etd/2014-10069

Particulate Deposition Prediction of Diluted Two-Phase Impinging Jet

2014· dissertation· en· W4255031183 on OpenAlexaff
Eid Alatawi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsCarleton University
FundersKing Abdulaziz City for Science and Technology
KeywordsReynolds-averaged Navier–Stokes equationsMechanicsLarge eddy simulationParticle depositionJet (fluid)Reynolds stressDeposition (geology)TurbulenceComputational fluid dynamicsLagrangian particle trackingParticle (ecology)Shear stressFlow (mathematics)Reynolds numberMaterials sciencePhysicsGeology

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.259
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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