Thermal Conductivity and Specific Heat Capacity of Insulation materials at Different Mean Temperatures
Bibliographic record
Abstract
Abstract Thermal conductivity and heat capacity are among the most essential properties of a building insulation in calculating thermal performance which are subjected to change when exposed to temperatures variation in service. Ignoring the temperature dependency of these material properties can result in under and over estimations of buildings energy uses and the corresponding equipment sizing. To obtain more realistic conductivity values of insulation materials, in this paper, thermal conductivity tests are conducted at various mean temperatures. For the study six commonly used insulations including Cellulose fiber, Expanded Polystyrene, Extruded Polystyrene, Open Cell Spray Polyurethane, Polyisocyanurate, and Mineral Wool are considered, and their thermal conductivity are measured at seven mean temperatures ranging from 5°C to 60°C. Furthermore, their specific heat capacity are measured at nine mean temperatures ranging between 16°C and 36°C. The results showed that except Polyisocyanurate board, the thermal conductivities and specific heat capacities of all insulation materials increased linearly with rising temperature, presenting a linear regression model with correlation coefficients (R 2 ) values between 0.96 and 0.98. The curve fitting of the Polyisocyanurate thermal conductivity measurements resulted a nonlinear regression model with R 2 of 0.97. The thermal conductivity of six insulations as a function of temperature have been established.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".