Temperature dependency of the long-term thermal conductivity of spray polyurethane foam
Bibliographic record
Abstract
The thermal properties of closed-cell foam insulation display a more complex behaviour than other construction materials due to the properties of the blowing agent captured in their cellular structure. Over time, blowing agent diffuses out from and air into the cellular structure resulting in an increase in thermal conductivity, a process that is temperature dependent. Some blowing agents also condense at temperatures within the in-service range of the insulation, resulting in non-linear temperature dependent relationships. Moreover, diffusion of moisture into the cellular structure increases thermal conductivity. Standards exist to quantify the effect of gas diffusion on thermal conductivity, however only at standard laboratory conditions. In this paper a new test procedure is described that includes calculation methods to determine Temperature Dependent Long-Term Thermal Conductivity (LTTC (T) ) functions for closed-cell foam insulation using as a test material, a Medium-Density Spray Polyurethane Foam (MDSPF). Tests results are provided to show the validity of the method and to investigate the effects of both conditioning and mean test temperature on change in thermal conductivity. In addition, testing was conducted to produce a moisture dependent thermal conductivity function. The resulting functions were used in hygrothermal simulations to assess the effect of foam aging, in-service temperature and moisture content on the performance of a typical wall assembly incorporating MDSPF located in four Canadian climate zones. Results show that after 1 year, mean thermal conductivity increased 15%–16% and after 5 years 23%–24%, depending on climate zone. Furthermore, the use of the LTTC (T) function to calculate the wall assembly U-value improved accuracy between 3% and 5%.
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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 | 0.000 | 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) | 0.001 | 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".