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How “mixing” affects propagation and structure of intensely turbulent, lean, hydrogen-air premixed flames

2024· article· en· W4405729746 on OpenAlexafffund
Yuvraj Yuvraj, Hong G. Im, Swetaprovo Chaudhuri

Bibliographic record

VenueCombustion and Flame · 2024
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaKing Abdullah University of Science and TechnologyUniversity of Toronto
KeywordsTurbulenceMixing (physics)Premixed flameHydrogenFlame structureMechanicsCombustionChemistryMaterials sciencePhysicsPhysical chemistryOrganic chemistryCombustor

Abstract

fetched live from OpenAlex

Understanding how intrinsically fast hydrogen-air premixed flames can be rendered much faster in turbulence is essential for the systematic development of hydrogen-based gas turbines and spark ignition engines . Here, we present fundamental insights into the variation of flame displacement speeds by investigating how the disrupted flame structure affects speed and vice-versa. Three DNS cases of lean hydrogen-air mixtures with effective Lewis numbers ( L e ) ranging from about 0.5 to 1, over Karlovitz number ( K a ) range of 100 to 1000 are analyzed. Suitable comparisons are made with the closest canonical laminar flame configurations at identical mixture conditions and their appropriateness and limitations in expounding turbulent flame properties are elucidated. Since near zero-curvature surface locations are most probable and representative of the average flame geometry in such large K a flames, statistical variation of the flame displacement speed and concomitant change in flame structure at those locations constitute the focus of this study. To that end, relevant flame properties are averaged in the direction normal to the zero-curvature isotherm locations to obtain the corresponding conditionally averaged flame structures. In the leanest case with smallest L e , the temperature increases beyond that of the standard laminar flame downstream of the zero-curvature regions, leading to enhanced local thermal gradient and flame speeds in the conditionally averaged structure. These result from increased heat-release rate contribution by differential diffusion ( L e ≪ 1 ) in positive curvatures downstream of the zero-curvature locations. Furthermore, locally, the flame structure is broadened for all cases due to a reversal in the direction of the flame speed gradient. This reversal is caused by cylindrical flame–flame interactions upstream of the zero-curvature regions, resulting in localized scalar mixing within the flame structure. The combined effect of these two non-local phenomena defines the conditionally averaged flame structure and the associated variation of the local flame speed of a premixed flame in turbulence. Novelty and Significance Statement The paper presents fundamental discoveries pertaining to the structure and propagation of intensely turbulent, lean premixed hydrogen-air flames, emerging from the analysis of averaged flame structures conditioned to zero-curvature surface locations. These locations are most probable alongside corresponding to the mean of curvature distribution. Analysis of such structures reveals how non-local effects determine the average flame displacement speed, for the first time. The paper shows that non-local effects within the flame structure address long-standing questions on how premixed flames are broadened in turbulence and why local flame displacement speeds of intensly turbulent, ultra-lean hydrogen-air flames are ubiquitously higher than their standard laminar counterpart.

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 score0.926
Threshold uncertainty score0.704

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 head0.191
Teacher spread0.185 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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