Experimental investigation of the influence of air conduction on heat transfer across fibrous materials
Bibliographic record
Abstract
Accurate prediction of the effective thermal conductivity of loose fill fibrous building thermal insulation remains a challenging issue to date. Most researchers considered the complex problem of heat transfer across a fibrous material to be the sum of heat transfer due to air conduction, solid conduction and radiation for which general empirical relationships were developed from reliable thermal conductivity tests. In this study, an experimental investigation of this hypothesis was undertaken by removing the air from a porous material, hence, theoretically leaving heat transfer due to solid conduction and radiation only. This hypothesis was tested by conducting steady state thermal conductivity measurements under vacuum conditions for coconut fiber and sugarcane fiber specimens. Test results indicated an average difference between the thermal conductivity at atmospheric conditions and under vacuum conditions of 0.0279 and 0.0231 W/m.K for coconut fiber and sugarcane fiber, respectively, over the density range tested. The experimental results were in agreement with the widely expressed opinion that air behaves as an independent individual contributor to the overall effective thermal conductivity of a loose fill fibrous material. Key words: Fibrous insulation, thermal conductivity, loose fill insulation, effective thermal conductivity, air conduction.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".