Design and simulation study on the heating performance of mesh heating elements
Bibliographic record
Abstract
Abstract To achieve uniform heating of heat‐not‐burn cigarettes, the ANSYS Workbench was used to verify the calculation rule of the resistance value of the mesh heating element. To provide a theoretical basis for improving the design of mesh heating elements, the effect of rounded corners on the resistance value and that of key parameters such as the number of mesh heat emitters in series and parallel, mesh density, and heating power on the heating uniformity were investigated. The deviation of all calculation results is less than 2.4%, indicating that the total resistance value of the mesh heating element conforms to the calculation rule of the series and parallel connections of single‐module resistors. The heating power increases by 3.2%, which has a relatively small impact on the overall heating element when a rounded corner with a radius equal to the line width is added to the corner of the heating element. The area of the heating body increases when increasing the number of series connections and reducing the number of parallel connections, which is conducive to uniform heating. While maintaining the ratio of the number of series connections to the number of parallel connections constant, reducing the module size will not change the area of the mesh heating element, but it will improve the temperature uniformity. Under different power conditions, the heating uniformity of the same heating element does not change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".