Numerical Study on the Effect of Low‐Frequency Power Supply on Desulfurization in the Electroslag Remelting Process
Bibliographic record
Abstract
In the electroslag remelting (ESR) process, low‐frequency power supply can significantly reduce power consumption and achieve three‐phase balance of power supply. Therefore, a transient coupling model of fluid flow, heat transfer, and component transport in the ESR process, which is coupled to the electromagnetic field calculated using Maxwell 3D software, is established to study the influence of low‐frequency power supply on desulfurization. When a 50 Hz power supply is used, a skin effect is observed in the metal, and the direction of the Lorentz force at the slag/metal interface changes. However, this effect becomes less pronounced with decreasing current frequency. Sulfur is mainly transferred at the electrode tip, and the desulfurization rate is approximately 50%. Electrochemical reactions mainly occur at the electrode tip/slag interface and the metal pool/slag interface. The removal rate of sulfur using direct current (DC) power supply is less than that using an alternating current power supply. The DC reverse polarity power supply leads to higher desulfurization rate than DC straight polarity, which is 74% and 31%, respectively. The sulfur removal rate increases from 81.37% to 84.59% as the frequency decreases from 50 to 2 Hz because of the longer electrochemical reaction time at this lower frequency.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".