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Record W2038763853 · doi:10.1520/jai13562

Fires in P-3 Aircraft Oxygen Systems

2006· article· en· W2038763853 on OpenAlexaboutno aff
JM Stoltzfus

Bibliographic record

VenueJournal of ASTM International · 2006
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsOxygenMaterials scienceEnvironmental scienceAerospace engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Fires in three P-3 air craft oxygen systems have occurred: one in the Royal Australian Air Force in 1984 and two in the U.S. Navy in 1998 and 2003. All three fires started in the aluminum manifold and check valve (MCV) assembly and produced similar damages to the aircraft in which they occurred. This paper discusses a failure analysis conducted by the NASA Johnson Space Center White Sands Test Facility Oxygen Hazards and Testing Team on the 2003 U.S. Navy VP-62 fire. It was surmized that the fire started due to heat generated by an oxygen leak past a silicone check valve seal or possibly because of particle impact near the seat of one of the MCV assembly check valves. An additional analysis of fires in several check valve poppet seals from other aircraft is discussed. These burned poppet seals came from P-3 oxygen systems that had been serviced at the Naval Air Station in Jacksonville following standard fill procedures. It was concluded that these seal fires occurred due to the heat from compression heating, particle impact, or the heat generated by an oxygen leak past the silicone check valve seal. The fact that catastrophic fires did not occur in the case of each check valve seal fire was attributed to the protective nature of the aluminum oxide layer on the check valve poppets. To prevent future fires of this nature, the U.S. and Canadian fleets of P-3 aircraft have been retrofitted with MCV assemblies with an upgraded design and more burn-resistant materials.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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 designSimulation or modeling
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
Published2006
Admission routes1
Has abstractyes

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