A novel methodology for calculating thermal conductivity of natural hollow fibers with validation in nonwoven fabric structures
Bibliographic record
Abstract
Natural fibers, especially hollow ones, are increasingly used in nonwoven insulation structures for their superior thermal insulation performance, renewability, and biodegradability, as hollow fibers offer lower thermal conductivity than solid fibers. However, accurately measuring the thermal conductivity of single hollow fibers, particularly given their extremely small diameter (10–50 μm) and thin wall thickness (around 1 μm), remains challenging, limiting the understanding of their role in composite materials. To address this, a novel approach combines experimentally measured bulk thermal conductivity with theoretical models for effective and radiative thermal conductivity in a numerical iterative process. Additionally, a theoretical framework was established to analyze composite thermal conductivity and was validated through experimental bench tests. After successfully predicting the thermal conductivity of a single hollow fiber, results indicated minimal anisotropy in the examined fibers that can be attributed to their thin wall thickness. Furthermore, the study demonstrated that fiber arrangement had little impact on thermal conductivity in highly porous structures, while decreasing fiber diameter significantly reduced radiative thermal conductivity due to increased scattering. These findings provide a comprehensive framework for evaluating the thermal behavior of hollow fibers and optimizing natural-fiber-based insulation materials, contributing to the development of more efficient and sustainable thermal insulation solutions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".