A Novel Methodology to Predict the Long-Term Performance of Vacuum Insulation Panels (VIPs) Using Climate Data
Bibliographic record
Abstract
Vacuum insulation panels (VIPs) have been a common insulating technology used in refrigeration and can help limit energy use in buildings by providing up to 10 times more insulation than typical insulation materials, all while using less wall space. This is specifically useful in places like Canada, where climates are cooler. Knowledge gaps around aging have currently prevented VIPs from being used in building envelope constructions. One of the remaining gaps of knowledge is that there is no methodology that has been created and linked to climate data to predict the actual performance of VIPs. This paper starts with discussions on various factors which influence the thermal conductivity of a VIP, relates it to the climate data of Victoria, British Columbia, Canada from 1997-2021, and proposes a methodology that can predict the long-term performance of VIPs in different climates. The proposed methodology was created in a piecewise approach, starting from constant conditions of 23 ֯C and 75%RH, moving to dynamic conditions based on climate data, and then adding the presence of a getter and desiccant. The resultant methodology produced a simplistic approach that has the potential to predict the performance of VIPs in various climate conditions. The proposed methodology shows that the thermal conductivity of VIPs remained relatively constant until either the getter or desiccant reached capacity. From there, the thermal conductivity began to increase over time. This methodology was then applied across four other (total of five) Canadian cities (Victoria, BC; Edmonton, AB; Yellowknife, NT; Ottawa, ON; Quebec City, QC), which all showed similar aging trends except for Victoria, British Columbia when reviewing ageing due to moisture content and Yellowknife, NT due to air pressure. The outputs from this methodology were also compared to the results obtained from accelerated ageing tests conducted in the laboratory, to estimate VIP parameters such as air and water vapour transmission rates, desiccant quantity, and sorption characteristics of the core material. The refined methodology can be converted into a standard method that has the potential to accurately predict VIP ageing in different climatic 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".