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Record W2988054047 · doi:10.1115/gt2019-90538

Comparison of Hydrogen Micromix Flame Transfer Functions Determined Using RANS and LES

2019· article· en· W2988054047 on OpenAlexaff
Jonathan McClure, David Abbott, Parash Agarwal, Xiaoxiao Sun, Giulia Babazzi, Vishal Sethi, Pierre Gauthier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsSiemens (Canada)
Fundersnot available
KeywordsReynolds-averaged Navier–Stokes equationsCombustionComputational fluid dynamicsMechanicsTransfer functionHydrogenAutoignition temperatureDiffusion flameNuclear engineeringCombustorMaterials scienceThermodynamicsEnvironmental scienceChemistryPhysicsEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Hydrogen has been proposed as an alternative fuel to meet long term emissions and sustainability targets, however due to the characteristics of hydrogen significant modifications to the combustion system are required. The micromix concept utilises a large number of miniaturised diffusion flames to improve mixing, removing the potential for local stoichiometric pockets, flash-back and autoignition. No publicly available studies have yet investigated the thermoacoustic stability of these combustion systems, however due to similarities with lean-premixed combustors which have suffered significant thermoacoustic issues, this risk should not be neglected. Two approaches have been investigated for estimating flame response to acoustic excitations of a single hydrogen micromix injector element. The first uses analytical expressions for the flame transfer function with constants obtained from RANS CFD while the second determines the flame transfer function directly using unsteady LES CFD. Results show the typical form of the flame transfer function but suggest micromix combustors may be more susceptible to higher frequency instabilities than conventional combustion systems. Additionally, the flame transfer function estimated using RANS CFD is broadly similar to that of the LES approach, therefore this may be suitable for use as a preliminary design tool due to its relatively low computational expense.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.294

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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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