Numerical Modeling of Particle Laden Flow in HVOF Torch with Gas Shroud
Bibliographic record
Abstract
Abstract A particle laden flow in an HVOF torch is analyzed using Computational Fluid Dynamics (CFD). The torch is similar to the DJ Metco torch with a converging-diverging (de Laval) nozzle, where particles are injected through the center together with nitrogen as a carrier gas. The Eulerian formulation is used for the gas flow whereas the particle motion is described by using the Lagrangian formulation. The flow turbulence is modeled via k-e model with standard wall functions. For modeling the combustion process in the torch, a multi-reaction Eddy-Dissipation Model (EDM) is employed. The computational domain comprised the torch itself and the region outside the torch where our attention is mainly focused. The computations are performed for the torch with and without the gas shroud attachment. The results showed that the presence of the shroud affected to some degree the flow and temperature fields of the main gas and the particle stream, while at the same time, significantly reducing the entrainment of ambient air into the main stream as shown by the lower oxygen concentrations. The results of the numerical computations are compared with experimental results for the same operating conditions and the agreement is found to be good.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".