Investigation of coupled vapor and heat transport in hygroscopic material during adsorption and desorption
Bibliographic record
Abstract
Vapor sorption in hygroscopic porous materials is accompanied by latent heat release/storage, which can influence indoor thermal comfort and building heating and cooling energy consumption. There is a need to better understand the coupled vapor and heat transport during adsorption and desorption. In this study, longitudinal spruce samples are exposed to adsorption and desorption experiments. Neutron radiography provides accurate measurement of moisture content variations spatially and temporally. Wireless thermocouples provide accurate measurements of temperature at different locations. Large changes in moisture content and temperature are observed during both adsorption and desorption experiments. Both moisture content and temperature variations seen in experiments are well simulated with hygrothermal modeling. The latent heat associated with vapor sorption is found to be the source of the large variations in temperature. It is found that vapor permeability influences both vapor and thermal transport while thermal conductivity influences only thermal transport. The vapor transfer coefficient has a small influence on vapor transport while the convective heat transfer coefficient has an influence on heat transport. The validated hygrothermal model is further used to simulate the coupled vapor and heat transport occurring in moisture buffering tests. It is found that moisture buffering values are different by up to 14% depending on the presence or absence of thermal insulation around the samples. For more hygroscopic materials, the difference can be even much larger. It is recommended not only to seal and but also to insulate samples for moisture buffering tests.
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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 | 0.000 |
| 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".