Fire tests to study the effect of pressure relief valve blowdown on the survivability of propane tanks in fires
Bibliographic record
Abstract
Abstract There is a significant variation in the blowdown behavior of the pressure relief valves (PRV) commonly supplied on certain commercial propane tanks. Determining how different pressure relief valve characteristics affect the survivability of a propane tank in a fire and the associated hazards, was the goal of these tests. To determine the effect of PRV blowdown, six 1890 L (500 gallon) propane tanks were exposed to an array of torch fires and thermally ruptured. A computer controlled valve was used to simulate the desired characteristics of a commercial PRV. In all cases the simulated PRV was set to open at 1.9 MPag, and blowdown was varied from 5% to 45%. The impact of the blowdown was determined by observing the nature of the failure, the time to failure, and the energy stored in the liquid and vapor phases at failure. Blast, radiation, and projectile hazards were also observed and recorded in order to determine the impact blowdown has on these pressure vessel failure hazards. An important finding of this testing is that the blowdown setting does impact the tank's survivability. It was consistently observed that large blowdown resulted in delayed failure. As a result, the PRV was open for a longer time, with a resulting reduction in hazards because of the lower fill at failure. This is believed to happen because of the lower average stress state in the tank with increased blowdown.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".