Dissipation and heat-transfer management in high-voltage heating elements
Bibliographic record
Abstract
The global transition toward renewable energies has brought special interest in electrified industrial heating systems, including thermal energy storage systems. While extensive research has been undertaken to design electrical heaters—focusing on macro-scale analyses, no research has addressed in a computationally efficient manner the micro-scale design of electrical heating elements to maximize power, and to avoid dielectric breakdown and heating-wire melting. This research addresses the micro-scale Joule heating and heat-transfer problem, furnishing a design rationale to inform macro-scale design of electrical heating elements under any voltage and external heat-transfer resistance. The model is used to interpret a recent literature report of an electrical heating element in air. From measured surface temperatures and the power dissipation, we ascertain the natural convection heat transfer coefficient and the internal temperature distribution, unveiling how close the heating wire comes to melting. The natural convection heat transfer coefficient for ambient air under non-Oberbeck-Boussinesq conditions is found to be significantly enhanced. By drawing on dimensional and geometric simlitude, the model may be translated to other thermal-fluid systems to optimize in-the-field performance.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".