A comparison of hygrothermal simulation results derived from four simulation tools
Bibliographic record
Abstract
The objective of this work was to compare the hygrothermal responses and the moisture performance of four wood-frame walls as predicted by four hygrothermal (HAM) simulation tools, namely: DELPHIN, WUFI, hygIRC and COMSOL. The four wall systems differ only in their cladding type; these were fibreboard, vinyl, stucco and brick. Three Canadian cities having different climates were selected for simulations: Ottawa, Ontario; Vancouver, British Columbia and Calgary, Alberta. In each city, simulations were run for 2 years. Temperature and relative humidity of the outer layer of OSB sheathing were compared amongst the four simulation tools. The mould growth index on the outer layer of the OSB sheathing was used to compare the moisture performance predicted by the respective hygrothermal simulation tools. Temperature profiles of the outer layer of the OSB sheathing were all in good agreement for the four HAM tools in the three locations. For relative humidity, the highest discrepancies amongst the four tools were found with stucco cladding where differences as high as 20% could be found from time to time. Mould growth indices predicted by the four HAM tools were similar in some cases but different in other cases. The discrepancies amongst the different HAM tools were likely related to: the material property processing, how the quantity of wind-driven rain absorbed at the cladding surface is computed and some implementation details. Despite these discrepancies, The tools generally yielded consistent results and could be used for comparing the impacts of different designs on the risk of premature deterioration, as well as for evaluating the relative effects of climate change on a given wall assembly design.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".