Heat Transfer Mechanisms in Arcs of Various Gases at Atmospheric Pressure
Bibliographic record
Abstract
Abstract This work expands findings about the dominant heat transfer mechanisms in argon and helium arcs at atmospheric pressure for monoatomic (Ar, He, 50% Ar–50% He), diatomic (air, $${\hbox{N}}_{2}$$ N 2 , $${\hbox{O}}_{2}$$ O 2 , $${\hbox{F}}_{2}$$ F 2 , $${\hbox{Cl}}_{2}$$ Cl 2 ), and triatomic ( $${\hbox{CO}}_{2}$$ CO 2 ) gases. The objective is to understand the dominant mechanisms in atmospheric plasmas through validated numerical modeling for GTAW welding process. Arcs of all gases have lengths of 10 mm and 200 A current. Five heat transfer mechanisms are considered: Joule heating, convection, radiation, conduction, and Thomson effect. Results indicate that the general structure of the arcs and dominant mechanisms are qualitatively similar for all gases; sizes change depending on the gas. The dominant energy input near the cathode is Joule heating, while that near the anode is convection. The dominant energy output always follows the same sequence: Thomson effect next to the cathode followed by convection, radiation in the arc column, and conduction near the anode. Joule heating is the most significant in Ar, while in He, it has the lowest significance. This is due to differences in electric conductivity of He (higher up to 21,000 K and lower beyond 21,000 K than other gases) and high viscosity of He, which creates a small Joule heating versus a large convection region. He transfers the most heat towards the anode by convection while $${\hbox{N}}_{2}$$ N 2 has the lowest; due to the high enthalpy and viscosity of He compared to $${\hbox{N}}_{2}$$ N 2 . Finally, Ar has the most significant radiative emission and He the smallest due to their net emission coefficient.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".