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Record W7009766067

Experimental and Computational Study of Flame Inhibition Mechanisms of Halogenated Compounds in C1-C3 Alkanes Flames

2013· dissertation· en· W7009766067 on OpenAlexaboutno aff

Bibliographic record

VenueOakTrust (Texas A&M University Libraries) · 2013
Typedissertation
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsFlammable liquidCombustionIgnition systemWork (physics)Mechanism (biology)Chemical reaction kineticsHydrocarbonFire protectionBenchmark (surveying)Laminar flow
DOInot available

Abstract

fetched live from OpenAlex

After the restriction of different halogenated fire suppressants by the Montreal Protocol, there is an urgent need to identify environmentally friendlier alternatives. In particular, several efforts have been conducted to find substitutes of Halon 1301 (CF_(3)Br) which was considered the best in its class, not only because of its superior extinguishing performance, but also due to its relatively low toxicity. Different options have been proposed over the last decade. However, no single compound has been found to meet all of the exigent criteria. Further progress in this research requires fundamental combustion knowledge that can help us understand the unique performance of Halon 1301, to prevent this search from becoming a tedious trial-and-error process. \n\nTo this end, the present work aids in the search of fire suppressants alternatives by improving the flame inhibition mechanism understanding, starting with CF_(3)Br, which serves as a benchmark for new fire suppressants. Then, a case study of two of the most currently used fire suppressants, C_(2)HF_(5) (HFC-125) and C_(2)HF_(7) (HFC-227), is presented and compared with CF_(3)Br performance. For these analyses, a systematic analytical methodology was used to examine the effect of fire suppressants on ignition and laminar flame propagation of C_(1)-C_(3) alkanes premixed mixtures, as good representatives of flammable gas fires (Class B fires). This methodology integrates model formulations and experimental designs in order to examine both chemical kinetics and thermal effects on fire suppressants at different stoichiometric conditions. Modeling predictions were based on a detailed chemical kinetics mechanism which was assembled from a new, well-studied H_(2), C_(0)–C_(5) hydrocarbon mechanism from NUI Galway and recent CF_(3)Br and HFC fire suppressant chemistry from NIST. Experimental study involved the use of a shock tube (for ignition analysis) and a freely expanding flame speed bomb (for laminar flame speed analysis). Most of the experimental data provided in this work are the first measurements of their kind for the compounds and mixtures explored in this thesis. These measurements are extremely valuable since they can be used as a metric for model validation which represents one of the objectives of this work. \n\nCurrent analyses indicate that the combustion properties of halogenated compounds cannot be generalized and depends on different factors. On one hand, the presented results showed that all the tested fire suppressants can decrease the laminar flame speed of the examined C_(1)-C_(3)alkanes premixed flames; however, in some cases they can act as ignition promoters. In order to understand these behaviors, sensitivity analyses were conducted showing that halogenated species, resulting from the fire suppressants decomposition, can participate in both promoting and inhibiting reactions that compete to give a net effect. Identification of the key reaction responsible for such effects was conducted. Then, improvements on the fire suppressant chemistry can be done by modifying the corresponding Arrhenius parameters of such important reactions. This work not only provides fundamental knowledge of halogenated flame inhibition mechanisms, but also serves as the basis for more accurate chemical kinetics mechanisms that can be used for better predictions over a wide range of conditions.

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 categoriesMeta-epidemiology (narrow)
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 score0.458
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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