Subfilter Scale Modelling for Large Eddy Simulation of Lean Hydrogen-Enriched Turbulent Premixed Combustion
Bibliographic record
Abstract
Hydrogen (H2) enrichment of hydrocarbon fuels in lean premixed systems is desirable since it can lead to a progressive reduction in greenhouse-gas emissions, while paving the way towards pure hydrogen combustion. In recent decades, large-eddy simulation (LES) has emerged as a promising tool to computationally describe and represent turbulent combustion processes.\nHowever, a considerable complication of LES for turbulent premixed combustion is that chemical reactions occur in a thin reacting layer at small scales which cannot be entirely resolved on computational grids and need to be modelled.\nIn this thesis, subfilter-scale (SFS) modelling for LES of lean H2-enriched methane-air turbulent premixed combustion was investigated. Two- and three-dimensional fully-compressible LES solvers for a thermally perfect reactive mixture of gases were developed and systematically\nvalidated. Two modelling strategies for the chemistry-turbulence interaction were pursued: the artificially thickened flame model with a power-law SFS wrinkling approach and the presumed conditional moment (PCM) coupled with the flame prolongation of intrinsic low-dimensional manifold (FPI) chemistry tabulation technique. Freely propagating and Bunsen-type flames\ncorresponding to stoichiometric and lean premixed mixtures were considered. Validation of the LES solvers was carried out by comparing predicted solutions with experimental data and other published numerical results.\nHead-to-head comparisons of different SFS approaches, including a transported flame surface density (FSD) model, allowed to identify weaknesses and strengths of the various models. Based on the predictive capabilities of the models examined, the PCM-FPI model was selected for the study of hydrogen-enrichment of methane. A new progress of reaction variable was proposed\nto account for NO. The importance of transporting species with different diffusion coefficients was demonstrated, in particular for H2. The proposed approach was applied to a Bunsen-type configuration, reproducing key features observed in the experiments: the enriched flame was shorter, which is attributed to a faster consumption of the blended fuel; and the enriched flame displayed a broader two-dimensional curvature probability density function. Furthermore, reduced levels of carbon dioxide (CO2), increased levels of nitrogen monoxide (NO), and a slight increase in the carbon monoxide (CO) levels in areas of fully burned gas were predicted for the\nenriched flame.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".