Simplified Infinite Fin Method to Model the Heat Transfer Associated with Slab Edges and Balconies
Bibliographic record
Abstract
Abstract As building codes become more stringent in terms of thermal performance of building envelopes, and higher insulated wall assemblies are becoming more common, the heat flow due to major thermal bridges can contribute to a significant portion of the total heat transfer through a building façade. Characterizing different thermal bridging elements is essential not only to capture the thermal resistance of wall assemblies and understand the thermal efficiency of buildings, but also in terms of understanding the impact of each thermal bridging element and mitigation strategies that can be used. Numerical simulations are used widely to characterize different thermal bridging elements. However, not all designers have access, technical skills or time to complete numerical simulations to calculate the heat transfer loss through thermal bridges. In this study we propose an analytical method to integrate the effect of adding a slab edge/balcony/eyebrow into a clear-field wall assembly. The additional heat transfer due to the slab edge is calculated by considering the slab edge to be an infinite fin. The additional heat transfer is integrated into the clear-field as a quasi-convective heat transfer coefficient. The overall thermal resistance of the wall assembly is calculated by employing the parallel path method. Comparing the results obtained from this method with the numerical simulations which were benchmarked against guarded hot box results, an overall deviation of 1 to 8 percent was observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".