The impact of the thermal mass on field energy performance of insulating concrete form (ICF) wall
Bibliographic record
Abstract
This research evaluated the dynamic heat transmission characteristics through an ICF wall assembly in FEWF for a one year cycle of exposure to outdoor natural weathering conditions. The monitored data confirmed that the concrete adds very little to the overall R-value of the wall assembly under steady-state conditions. During the transient conditions, the data showed that the concrete played a significant role in tempering heat loss to the exterior. The thermal mass of the concrete was shown to reduce the peak heat flux through the assembly during cold weather. The present NRC-IRC's hygrothermal model, called hygIRC-C, was used to interpret the readings of the instrumentations and to improve the experiment design by repositioning these instrumentations at critical locations. Subsequently, the present model was benchmarked against the measured data. Results showed that the predictions of the present model are in good agreement with experimental data. This research is one of a series of projects that highlight direct and indirect impacts of thermal performance of the building Envelope technologies in houses. This research is on-going. Recently, the present model was used to conduct numerical simulations in order to investigate the transient thermal response of full-scale ICF wall assemblies subjected to different Canadian climates.
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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".