Measurement of thermophysical properties of fire‐resistive and reactive materials. Urgent problems and solutions
Bibliographic record
Abstract
Summary The ASTM Standard Test Method E2584 ‘Standard Practice for Thermal Conductivity of Materials Using a Thermal Capacitance (Slug) Calorimeter’ was developed by National Institute of Standards and Technology to measure thermal conductivity of fire‐resistive and reactive materials during monotonic heating and cooling. The heating regime adopted in ASTM E2584 is very reasonable because change of materials' composition and structure during a fire can depend on kinetic factors and thermal story of the materials. The main problem in experimental measurements of thermophysical properties is the impossibility of using standard steady‐state methods during time‐dependent processes in materials accompanied by latent heat effect. Using standard transient methods, such as hot wire or laser flash methods, is also incorrect, because the transient measurement heat process can be started only after steady‐state temperature field is established in the sample, that is, at the time when the involved physical or chemical processes could be finished. The objectives of this paper are to review and to analyze scientific problems to be taken into account in the revised version of ASTM E2584 Standard. Examples of experimental results are presented for measurement of thermophysical properties during chemical and physical processes in solid materials, powders, metals, and ceramic materials; building materials during fire; and so on. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".