Hygrothermal response to air movements in wall junctions: Comparison between two numerical approaches and experiments
Bibliographic record
Abstract
Airflow in wall-to-wall junctions is known to have a major hygrothermal impact on building performance. However, current and validated modeling options to simulate such phenomena are limited. This paper develops and compares two numerical models to study the heat and moisture transfer due to air infiltrations through a prefabricated wall-to-wall junction. The first model explicitly accounts for the airflow with a pipe flow approach. The second model is a modification to a typical approach to simulate ventilated cavities in building envelope simulation tools and mimics the effect of the airflow through source terms. Both approaches were introduced in a heat and moisture transfer 2D finite element model. Additionally, laboratory measurements were conducted in a climatic chamber to validate the simulation results. Six scenarios were tested experimentally under steady-state conditions. These datasets were used to calibrate different parameters of the models, such as material properties, the junction air gap thickness, and the magnitude of the heat and moisture source terms. Both sets of numerical results provided reasonable agreement with the measurements. The first approach outputs more accurate temperature and relative humidity values than the second one. However, considering uncertainties, no method predicted a perfect fit with the relative humidity profiles. Close to the junction, the first method estimates better the relative humidity than the second one. This work provides guidelines to better model and account for wall junctions in building envelope simulators.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".