Numerical Study of the Arc Fluctuations in DC Plasma Torch
Bibliographic record
Abstract
Abstract Plasma spray technology is widely employed by industry to apply coatings on different components to protect them from corrosion, wear and high temperature environments. The gases introduced into the DC plasma torch are heated by the arc and a plasma jet exits the torch. Powders are injected into the plasma jet where they are then accelerated, heated, and melted before impacting the substrate, which is placed at some distance from the outlet of plasma spray torch. Plasma arc exhibits strong voltage fluctuations which correspond to the movement of the anode arc root attachment. Understanding the arc movement within the torch and how it affects the flow and temperature fields of the plasma jet exiting the torch is of great importance. Understanding the flow, temperature and electromagnetic fields within the DC plasma torch is extremely challenging and there is a limited number of investigations in the literature. In order to provide unique sets of surface characteristics, e.g., thermal barriers, wear and corrosion resistance, a high quality coating with appropriate combination of powder and base materials must be produced. To produce a high quality coating, powder particles should be uniformly heated and accelerated, and then deposited onto the substrate. In this paper, an unsteady 3-dimensional model of the arc movement within the plasma torch is reported. The proposed model is employed to solve electric potential and magnetic vector potential equations in addition to continuity, momentum and energy equations. The k-ε turbulence model was used to model the turbulence of the flow field inside a non-transferred DC argon plasma torch. The geometry of the torch was that of SG-100 torch (Praxair). TO study the effect of the arc length on the voltage, first a steady-state model was considered for a range of arc lengths and arc-root radii. The results of this model provided the relation between arc length and arc voltage for a set of arc root radii and given argon flow rate. Then, given voltage fluctuation profile, the unsteady, arc root attachment movement was simulated from the estimation which found from steady models. Results show that the effects of velocity and temperature fluctuations at the outlet of the torch (where the particles are injected) are not negligible and such fluctuations exceed 15% of their average values. These will in turn affect the particle heating history and will negatively impact the microstructure of the coating.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".