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Record W4296466486 · doi:10.18280/mmep.090432

Effects of Hydrogen and Nitrogen Concentration on Laminar Burning Velocities and NO, CO Formation of Propane-Air Mixtures

2022· article· en· W4296466486 on OpenAlexvenueno aff
Muna Hameed Alturaihi, Mahmoud A. Mashkour, Sanaa Turki Mousa AL-Musawi

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCombustionPropaneAdiabatic flame temperatureNOxLaminar flowHydrogenNitrogenChemistryDiffusion flameHydrocarbonFuel gasFlame speedThermodynamicsOrganic chemistryCombustorPhysics

Abstract

fetched live from OpenAlex

With the development of the uses of hydrocarbon compounds in many industrial fields, especially in the field of energy liberation through oxidation of gases, as previous research specialized in improving the combustion processes of gases by adding particles of other gases that contribute to improving the combustion of these compounds in terms of temperatures and speed of flame combustion. The previous study aimed to improve the flame speed by adding different gases to several compounds. In this research paper, propane gas C3H8 is used as the main gas to improve its combustion properties. N2 and H2 gas were added in different proportions, through which a clear effect on the combustion properties can be seen in terms of combustion speed. Flames and exhaust as CO and NOx. As the auxiliary gases were added in this improvement at rates of 20%, 30% and 40%, and then these tests were analyzed, where it was concluded that H2 gas clearly contributed to improving the combustion speed of the flame, as the flame combustion velocity reached 58.3 cm/s by 40%.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.382

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 head0.011
GPT teacher head0.184
Teacher spread0.173 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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