Thermal response of basement wall systems with low-emissivity material and furred airspace
Bibliographic record
Abstract
In basement wall systems, airspaces can contribute in obtaining a higher thermal resistance, if a low-emissivity material such as reflective foil is installed within a furred-airspace. In this study, numerical simulations were conducted using the hygrothermal model ‘hygIRC-C’ that was developed at the National Research Council of Canada’s Institute for Research in Construction to investigate the steady-state and transient thermal performance of basement wall systems. This model solves simultaneously the energy equation in the various material layers, surface-to-surface radiation equation in the furred-airspace assembly, Navier–Stokes equation for the airspace, and Darcy and Brinkman equations for the porous material layers. The wall systems used in the simulations incorporate a low-emissivity material (foil with emissivity = 0.04) bonded to a moulded/expanded polystyrene foam that is installed in a furred-airspace assembly. The furring is installed horizontally and covered with a gypsum board. The structural element of the wall (external layer) is a poured-in-place concrete. Walls with and without furred-airspace assembly were considered in this study. Also, consideration was given to investigate the effect of the above-grade and below-grade portions of the wall on the thermal performance when these walls are subjected to two different Canadian climates. Results showed that at steady-state condition, the effective thermal resistance ( R-value) of the wall with a furred-airspace assembly depends on the soil, outdoor, and indoor temperatures. Additionally, these wall configurations resulted in an energy savings of ~17% compared to walls without furred-airspace assembly when these walls are subjected to two different climate conditions.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".