Evaluating the Thermal Resistance of a Vacuum Insulation Panel Wall Assembly Containing Thermal Bridges using Industry Standard Calculation Methods and Numerical Simulation Techniques
Bibliographic record
Abstract
This thesis investigates the accuracy of industry standard calculation methods, and two and three-dimensional numerical simulation techniques, to predict the thermal resistance of a wall assembly containing vacuum insulation panels (VIPs) and thermal bridges.The calculation methods and numerical simulations were used to predict the thermal resistance of a wall assembly that was tested in a guarded hot box.The calculation methods and two-dimensional simulation scenarios which did not include VIP edge thermal bridges resulted in a minimum overestimation of 38%.Accounting for the thermal bridges using the average joint width between panels reduced the minimum overestimation to 13% (modified zone calculation method) and 20% (two-dimensional simulations).The three-dimensional simulations overestimated the thermal resistance by 14%.Overall, the most reliable predictions of thermal resistance were determined through 3D simulations and the modified zone method in combination with the thermal bridge effect due to the average joint width between VIPs.iiiAcknowledgements First and foremost, I would like to thank my family for supporting me.To my wife, Heidi, you are my most steadfast supporter, never wavering in your belief that this is something I could and should do.Your diligence and dedication to your own degree inspired me to new levels of dedication required to complete this thesis.Thank you to my children, Charlotte and Logan
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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) | 0.000 | 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 | 0.001 |
| 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".