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Record W4408574551 · doi:10.1063/5.0256204

Flow and release characteristics of a potential Halon substituent CF3I influenced by filling density and pressure

2025· article· en· W4408574551 on OpenAlexaboutno aff
Qi Yang, Jingjing Liu, Yun Lu, Xiaomeng Zhou, Haijun Zhang

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsnot available
FundersScience Fund for Distinguished Young Scholars of TianjinTianjin Science and Technology ProgramData Center of Management Science, National Natural Science Foundation of China - Peking University
KeywordsPhysicsFlow (mathematics)MechanicsSubstituentStereochemistry

Abstract

fetched live from OpenAlex

Halon fire-extinguishant has been banned by the Montreal Protocol due to the ozone-depleting effect, and CF3I is proposed as a promising alternative to Halon on aircraft cargo compartments. The filling density and filling pressure of CF3I in the fire-extinguishing vessel are critical for the design of onboard fire-extinguishing system. Therefore, this paper combines experimental test and quantitative computational fluid dynamics numerical simulation to explore flow and release characteristics for CF3I under five sets of filling densities and five sets of filling pressures. The increase in filling density and the decrease in filling pressure are unveiled to promote the evaporation of CF3I. In addition, a release parameter of R is herein proposed to quantitatively assess the release efficiency of CF3I under varying conditions, and the recommended ranges of filling density (514–1028 kg/m3) and filling pressure (2.5–3.5 MPa) are accordingly proposed for CF3I. As revealed by the monitored phase transition mass transfer rate, Halon 1301 can be evaporated at the pipeline inlet from the beginning of the agent discharging process, while CF3I only evaporates when approaching the nozzle at the later stage of the agent release 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.459

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 head0.006
GPT teacher head0.235
Teacher spread0.229 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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