Shadowgraphic Imaging of Fibre-Delivered Pulsed IR Laser-Induced Heat Transfer across Thin Aluminized Polymer Film
Bibliographic record
Abstract
Shadowgraphic imaging was employed to investigate the mid-IR laser induced heat transfer through a double layer thin film. The effect of thin metal coat on the polymer film enhanced the transfer of heat and shock waves due to rapid thermal expansion and the explosive evaporation of the thin fluid layer. Sixty two percent of deposited heat expended for water enthalpy and 38% for other factors. A power of 8.8 kW was launched at the surface of aluminium. The thermal coupling of 45% further reduced the input energy to the film and the non-adiabatic heat diffusion (i.e., ) was transmitted instantaneously within the metal with very small loss. The temperature at the surface of the film was determined ≈301 K, well below the aluminium melting point. The Biot number showed that the metal as single layer and the whole film as double layer satisfies the thermally thin film (i.e., ). Considering the Newtons’s law of cooling, the overall film heat transfer coefficient was found 3 k W·m-2·K-1 equivalent of 3.3 × 10-3 W·m2·K-1 thermal resistance. The analysis of images indicated a reducing percentage of heat transfer as a function of delay time based on the comparison of volume ratios. A calculated power of ≈3 kW was transmitted from the rear side of the film sufficient to thermalize the surrounding water layer and form vapor bubble.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".