Bibliographic record
Abstract
Particle-laden flow has been acknowledged as an important phenomenon integral to many technological and natural systems. Analysis of a high speed particle-laden flow typical of those in the high velocity oxy-fuel (HVOF) process is presented in this thesis. Since the particulate flow in this process includes a large variety of particle suspension, the Lagrangian and Eulerian approaches have been applied in parallel to simulate the two-phase flow associated with the process. Effects of attaching a cylindrical shroud to the end of the supersonic HVOF nozzle on gas and particle flows have been analyzed. It was found that the shroud significantly reduces the oxygen content in the field by protecting the supersonic jet from ambient air entrainment. The validation experiments were performed for the majority of process parameters such as shock formation, particle conditions, and coating oxygen content. The thesis concludes with application of the numerical models in designing a nozzle that provides improved performance in the areas of deposition efficiency, particle in-flight oxidation, and flexibility to allow coating of ceramic powders. And finally, the new nozzle was built, and successfully tested with several coating materials including metallic, carbide, and ceramic powders.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".