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Record W2523441648 · doi:10.1115/gt2016-56655

A Numerical Study on the Reactivity of Biogas/Reformed-Gas/Air and Methane/Reformed-Gas/Air Mixtures at Gas Turbine Relevant Conditions

2016· article· en· W2523441648 on OpenAlexaff
Pablo Diaz Gomez Maqueo, Philippe Versailles, Gilles Bourque, Jeffrey M. Bergthorson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsSiemens (Canada)McGill University
Fundersnot available
KeywordsSyngasCHEMKINLaminar flame speedMethaneBiogasCombustorAdiabatic flame temperatureLaminar flowChemistryReactivity (psychology)Adiabatic processMaterials scienceCombustionThermodynamicsPremixed flameWaste managementHydrogenOrganic chemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

This study investigates the increase in methane and biogas flame reactivity enabled by the addition of syngas produced through fuel reforming. To isolate thermodynamic and chemical effects on the reactivity of the mixture, the burner simulations are performed with a constant adiabatic flame temperature of 1800 K. Compositions and temperatures are calculated with the chemical equilibrium solver of CANTERA® and the reactivity of the mixture is quantified using the adiabatic, freely-propagating premixed flame, and perfectly-stirred reactors of the CHEMKIN-Pro® software package. The results show that the produced syngas has a content of up to 30 % H2 with a temperature up to 950 K. When added to the fuel, it increases the laminar flame speed while maintaining a burning temperature of 1800 K. Even when cooled to 300 K, the laminar flame speed increases up to 30 % from the baseline of pure biogas. Hence, a system can be developed that controls and improves biogas flame stability under low reactivity conditions by varying the fraction of added syngas to the mixture. This motivates future experimental work on reforming technologies coupled with gas turbine exhausts to validate this numerical work.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.626

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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 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

Citations5
Published2016
Admission routes1
Has abstractyes

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