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Record W3187461404 · doi:10.1177/07349041211034456

Nitrogen as an environmentally friendly suppression agent for aircraft cargo fire safety

2021· article· en· W3187461404 on OpenAlexaboutno aff
Michail Diakostefanis, Suresh Sampath, Akhil Dinesh, Rainer Beuermann, A. K. Malkogianni

Bibliographic record

VenueJournal of Fire Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
FundersHorizon 2020
KeywordsEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

Fire suppression systems in cargo compartments are a certification requirement for commercial aircraft safety. Halon production was banned and usage ends in 2040 according to Montreal Protocol for environmental reasons. This necessitates an alternative environmentally friendly agent. Quantitative analysis of nitrogen as agent established suitability of the suppression system. The Minimum Performance Standards specifies the qualification procedure of an agent through four scenarios – bulk load; containerised load; surface burning; and aerosol can explosion. Empirical sources from Airbus, independent computational fluid dynamics studies and small-scale cup-burner tests indicate suitability of nitrogen specific to aircraft cargo fire suppression. The nitrogen delivery system and the experimental apparatus are presented. Extensive commissioning tests verified instrumentation reliability. All the four scenarios were conducted at Cranfield University, in a replica of a wide-body aircraft cargo compartment. In a reduced oxygen environment (11%) obtained with nitrogen discharge, the aerosol can explosion tests were performed without any evidence of explosion or pressure increase beyond the expected baseline value. The surface burning scenario was completed successfully and passed the Minimum Performance Standard criteria. The maximum average temperature was found to be 220°C (limit – 293°C). All the scenarios passed the Minimum Performance Standard criteria for indicating successful prevention of Class B fire re-ignition. Similarly, the containerised and bulk-load scenarios obtained results that passed the Minimum Performance Standard criteria for successfully maintaining continued fire suppression for a specified period of time. The maximum average temperature in containerised-load fire scenario was found to be 210°C (limit – 343°C) and in bulk-load scenario was 255°C (limit – 377°C). Additional qualification criteria and system design are presented in this article according to the Minimum Performance Standard format. This work can be extended to introduce standard testing for safety critical systems, such as engine bay and lithium-ion fires.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.013
GPT teacher head0.270
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations9
Published2021
Admission routes1
Has abstractyes

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