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Record W4293790105 · doi:10.2172/1871202

Experimental Results of 2-m Heptane, Bakken Crude Oil, and Dilbit Crude Oil Pool Fire Tests Performed for the National Research Council of Canada

2021· report· en· W4293790105 on OpenAlexaboutno aff
Anay Luketa, Alvaro Cruz-Cabrera, Walter Gill, Shane Adee, Joseph Hogge

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsCalorimeter (particle physics)HeptaneCone calorimeterHeating oilHeat fluxEnvironmental scienceCrude oilWaste managementFuel oilFlux (metallurgy)ThermocoupleCombustionChemistryPetroleum engineeringPulp and paper industryMaterials scienceHeat transferThermodynamicsComposite materialEngineeringElectrical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

This report provides results from a series of 2-m pool fire experiments performed in the Thermal Test Complex at Sandia National Laboratories testing heptane, Bakken crude oil, and dilbit crude oil. The effect of the presence and placement of a calorimeter, fuel supply temperature, and maintaining a constant fuel level were assessed. Measurements include burn rate, surface emissive power, flame height, heat flux to an engulfed calorimeter, heat flux to external instruments, thermocouple temperatures within the fuel and fire plume, and heat release rate. The results indicate that the presence and placement of the calorimeter has the most effect on the measured quantities for the Bakken crude oil and indicated no effect for the Dilbit crude oil. The fuel feed temperature had a slight effect for the heptane fuel, but not for the crude oils. Allowing the fuel to burn down did not have a significant effect on any of the fuels. The Bakken crude oil resulted in the highest average total heat flux to the calorimeter by a factor of about 1.5 and 1.3 higher compared to heptane and the dilbit crude oil, respectively.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.112
GPT teacher head0.336
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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