The Effect of Pressure Relief Valve Blowdown and Fire Conditions on the Thermo-Hydraulics Within a Pressure Vessel
Bibliographic record
Abstract
If a pressure vessel containing a pressure liquefied gas (PLG) is exposed to a severe fire, there is a chance the tank will rupture and possibility even explode. It has been shown that the energy stored in the liquid phase is important in the outcome of such an accident. In order to better understand tank failure and energy storage, it is desired to understand the complex thermo-hydraulic response of the lading prior to a thermally induced rupture. In the summers of 2000 and 2001, a series of controlled fire tests were conducted on horizontal 1890 litre (500 US gallon) propane tanks. The test tanks were instrumented with pressure transducers, lading and wall thermocouples, and an instrumented flow nozzle in place of a pressure relief valve (PRV). A computer controlled PRV was used to control pressure, while high momentum, liquid propane utility torches were used to heat the tank. PRV blowdown and fire conditions were varied in this test series while all other input parameters were held constant. It was found that the lading response and energy storage varied according to the fire conditions and PRV operation. The location and quantity of the burners affected the thermal stratification within the liquid, and the swelling and frothing at the liquid/vapour interface. The blowdown of the PRV affected average tank pressure, average liquid temperature and time to destratify. PRV operation caused enhanced convective cooling on the vapour space wall. The degree of blowdown dictated the pressure drop, and thus the liquid flashing and subsequent swelling were affected. This paper will discuss these thermohydraulic responses and their role in the tank failure.Copyright © 2002 by ASME
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".