Sub-scale helium tests and numerical simulations for studying the effect of location and magnitude of BIPV double skin façade fires on the smoke spread
Bibliographic record
Abstract
Nowadays climate change all over the world, imposed risk to human health, security, and economies. Thus, renewable clean energy sources have been recently promoted as the means of sustainability of world’s energy system and mitigation for the climate change impacts. It is still questionable if the new building technologies such as photovoltaics are well observed in terms of finding the associated risks. According to the wide application of PV systems and lack of investigation on the BIPV (specifically on the façade), there is high demand of studying scenarios through which the fire behavior and smoke propagation are extensively noticed. Hence in the current research, the smoke spread from BIPV DSF (Double Skin Façade) fire is investigated employing the helium tests and a newly proposed helium similarity. The experiments are conducted for the parametric study of the case study building for different location of the fire on the façade and different heat release rates (HRR). Moreover, to link the dimensionless full-scale real fire simulated temperatures with the downscaled dimensionless helium test measurements, a new equation of similarity is proposed. Subsequently, via proposed theory and Froude modeling, the helium test is designed and carried out. The validated numerical simulation in Fire Dynamics Simulator (FDS) can verify the governing theory and scaling method between small-scale helium test and full-scale real fire smoke test.
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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) | 0.000 | 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 | 0.001 | 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".