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Record W2584521696 · doi:10.3303/cet1648047

Fire Testing of Total Containment Pressure Vessels

2016· article· en· W2584521696 on OpenAlexaff
Frank Otremba, Francisco González, Anand Prabhakaran, Jörg-Peter Borch, Ian Bradley, Luke Bisby

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsContainment (computer programming)Cabin pressurizationEnvironmental scienceHazardous wastePressure vesselRelief valveLeakage (economics)Waste managementFirefightingForensic engineeringEngineeringMarine engineeringNuclear engineeringMechanical engineeringComputer scienceChemistry

Abstract

fetched live from OpenAlex

Full engulfment fire tests have been conducted on total containment pressure vessels filled to 50% and 98 % capacity with water. The tests included an unprotected tank and tanks with two different levels of thermal protection. Total containment in this context means there was no pressure relief device. The tests were conducted with 1/3rd linear scale rail tank cars similar to the DOT 111 tank cars used in North America. The 2.4 m3 model tanks were subjected to 100 % engulfing fires fuelled by liquid propane. The fireheat flux was approximately 80 % by radiation and 20 % by convection with a total heat flux to a cool surface of approximately 100 kW/m2. The tanks were instrumented with wall and lading thermocouples and pressure transducers. The fire conditions were measured using directional flame thermometers (DFT). In these tests the tank pressure increased rapidly suggesting strong liquid temperature stratification. Even at high fill levels of 98 % the tank wall temperature in the vapour space increased rapidly to dangerous levels. The results from these tests will be used to validate computer models of the tank heating process.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.165
GPT teacher head0.476
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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