In-Situ Experimental Validation of THERM Finite Element Analysis for a High R-Value Wall Using Vacuum Insulation Panels
Bibliographic record
Abstract
Team Ontario is one of twenty collegiate teams selected to design and build a solar powered, net positive home for the U.S. Department of Energy Solar Decathlon 2013. One aspect of Team Ontario’s competition design entry is a high R-value wall using vacuum insulation panels. This paper details the method used for theoretical evaluation of the high R-value wall, stating all simplifying assumptions made. Theoretical simulations were performed in THERM, a two dimensional finite element heat transfer modelling program. Following a weighted average method used by industry experts, the whole-wall thermal resistance value was calculated. To verify the modelling results, an in-situ experimental validation was conducted. An 8′ × 8′ wall test specimen was built to the specifications of Team Ontario’s wall design. Experimental heat flux and temperature readings were collected from the test specimen in Carleton University’s Vacuum Insulation Test Facility located in Ottawa, Ontario, Canada, with the test specimen exposed to exterior weather elements. The experimental and theoretical results are compared and conclusions drawn to determine the effective thermal resistance of the vacuum insulation panels installed in the wall assembly. Finally the theoretical model is refined based on the previous study and a more accurate whole-wall thermal resistance of Team Ontario’s wall design is determined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".