Simultaneously estimating functional forms for thermal conductivity and vapor resistance factor of wall insulation in situ by inverse problem
Bibliographic record
Abstract
• A dual inverse method estimates effective hygrothermal properties in a wall assembly. • Datasheet and in situ conductivity and vapor resistance show significant differences. • Literature overestimates vapor resistance of EPS and conductivity of insulation. • These differences induce a bias of 9% in the annual heat and moisture flow estimates. In the design or retrofit stages, numerical simulations are a powerful tool to evaluate the hygrothermal performance of building envelopes. However, simulation results are only reliable if the building physics modeling is calibrated with accurate input data. This study reports an inverse analysis to simultaneously estimate functional forms of the thermal conductivity and the vapor resistance factor of prefabricated wall insulation layers. Inverse fully coupled hygrothermal modeling considered temperature and relative humidity measurements obtained in situ from an occupied detached house located in Quebec City, Canada. The inverse approach enabled the numerical model to better fit the in situ hygrothermal behavior of the investigated wall under actual environmental conditions. The estimated effective properties had considerable differences compared to literature data and their effect was evidenced by computing the annual heat and moisture flux. Therefore, in situ model calibration reduced inaccuracies arising from neglecting the impact of temperature and moisture content on material properties and eliminated potential differences between operating conditions and those reported by building standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".