Simulation study of the thermal insulation performance of a vacuum‐insulated tube inside an electrically heated device
Bibliographic record
Abstract
Abstract To realize stable heating of a heated cigarette while effectively controlling the outer‐wall temperature of an electric heating‐type appliance, a vacuum‐insulated tube (VIT) is currently widely used for thermal insulation against the heating element. The effects of the critical parameters of the VIT, gases at different pressures, wall thickness, and material on its thermal insulation performance, were simulated using Fluent to provide guidance on the structure and performance optimization of the EHD. When the absolute pressure of gas in the VIT was <0.5 Pa, increasing the degree of vacuum did not improve the thermal insulation performance. The thermal insulation performance of VITs made from different materials (stainless steel, copper, aluminium, brass, and titanium alloy) was closely related to the emissivity and thermal conductivity of the material. Stainless steel yielded better overall thermal insulation owing to its low thermal conductivity and emissivity. Highly emissive materials, such as brass and titanium alloy, contributed to higher heat transfers through the radiation inside the vacuum chamber and external radiation from the VIT, decreasing the thermal insulation performance of the VIT. Stainless steel VITs yielded an improved thermal insulation performance as the wall thickness decreased, but at the cost of decreased mechanical stability. For a wall thickness of 0.05–0.30 mm, stainless steel yielded the best structural stability. VITs with different wall thicknesses were made from stainless steel, copper, and aluminium, and the thermal insulation performance and structural stability of the EHD were optimized.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".