Investigation of photovoltaic roof fire-induced smoke spread under windeffect
Bibliographic record
Abstract
Photovoltaic (PV) panels have been installed seven times more over the last decade. However, the number of fires caused by PV panels has also increased. Based on the forecast, PV power generation capacity is expected to exceed 1 TW in 2022, and 30% of PV installations are rooftops. A statistical prediction indicates that 27,166 fires will occur in 2021 and 28,900 fires will occur in 2022, with 8,670 of those fires occurring on solar roofs. In buildings, however, there are very few studies on the spread of PV fireinduced smoke, especially when it is accompanied by wind, which is essential for life safety. This study fills this research gap by proposing a new similarity law and a method for substituting pure helium gas for real fire smoke in order to study PV roof fireinduced smoke movement in the non-fireproof wind tunnel. As a result, the heat release rate (HRR) of PV fires, wind speed, and roof angle of the wind tunnel are tested. It is possible to substitute the HRR and smoke temperature for a certain volumetric flow rate and helium concentration. As a next step, the critical values are determined by a parametric study of HRR, wind speed, and roof angle. Based on the findings, three conclusions can be drawn: 1) a greater HRR results in greater smoke infiltration; 2) a lower wind speed causes stronger separation flow to blow smoke into the building via the leeward skylight; 3) flat roofs and 15 roofs are the most dangerous because of smoke infiltration; conversely, 60 roofs are the safest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".