Study of the impact of operation distance of outdoor portable generators under different weather conditions
Bibliographic record
Abstract
The US Centres for Disease Control and Prevention has reported that up to half of non-fatal carbon monoxide (CO) poisoning incidents during the hurricane seasons in 2004 and 2005 involved generators operated outdoors but within 7 ft of the home. Current guidance for safe operating distances of generators is often neither specific nor consistent. A study was conducted to examine the impact of generator distance on indoor CO exposure. The study was based on computer simulations of CO transport outdoors and subsequently into a generic two-storey house. This paper presents the simulation results when using an indoor air quality model coupled with a computational fluid dynamics model to predict CO concentrations near and within the home. Fire Dynamics Simulator was validated against the measured contaminant dispersion data in a wind tunnel. A parametric study was then conducted for the two-storey house to consider the effects on indoor CO levels of generator location, distance, exhaust temperature and speed and weather conditions. It was found that in most cases, to reduce CO levels for the conditions modelled, it was more effective to point the generator exhaust away from the house and position the generator at a distance of >4.6 m.
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 | 0.001 |
| 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 | 0.000 | 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".