Development of a Low-Temperature, Oxy-Fuel (LTOF) Thermal Spray Gun
Bibliographic record
Abstract
Abstract This paper presents the development of a new thermal spray gun for the so-called warm spraying process in which powder particles are not melted but heated to temperatures much higher than those typically found in a cold spray process. The increased heating leads to a reduction in the particle impact velocity required to deposit the coating and hence reduces operating cost. The new gun utilizes methane-oxygen combustion for particle heating and features a swirl-type combustion chamber to create a turbulent mixture of the fuel and oxidizer for efficient combustion. Powder can be fed axially or radially into the gun. To control particle temperature independently, combustion gases are diluted by adding nitrogen gas through axial or radial ports provided in the gun. A converging-diverging nozzle with a downstream cylindrical barrel accelerates the burnt gases to supersonic velocities. The design of the nozzle and barrel was optimized using numerical simulations. Mass flow rates of methane, oxygen, and nitrogen were calculated using energy balance, stoichiometric combustion, and nozzle flow rate equations. The gun is designed to operate up to 200 kW and is water-cooled. Experiments were conducted to test the performance of the new gun in which tungsten carbide coatings were deposited on aluminum substrates. Coatings were analyzed using standard methods and showed promising results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".