Large Eddy Simulation of multi-injector flame blow-off sensitivities to inlet biases
Bibliographic record
Abstract
Reactant biases of mass flow rate or stochiometry can result from design trade-offs in industrial implementations of multi-injector, lean-premixed flames. Rules for maximising the lean-extinction limit require additional insight from experiments and/or computations as global scalings may not necessarily apply. Models, however, need extensive validation as the timescale separation between chemistry and turbulence decreases towards the lean limit, and a larger range of thermochemical states may be present. This leads to difficulties in parametrising them accurately. In this work, large eddy simulation (LES) is used to model blow-off in a linear array of lean, swirling, methane-air flames at atmospheric conditions. The LES methodology is assessed with regard to reproducing partial blow-off due to reactant equivalence ratio ( ϕ ) and flow rate ( m ̇ ) biases. It is found that the blow-off transients at ideal (no bias) and biased conditions are similar with regard to the large-scale effects. Progress variable based flamelet generated manifolds (FGM), as well as transported species, are employed and contrasted. Both methods could reproduce the highly transient nature of blow-off, though the flamelet strategy underpredicts blow-off for some conditions. Using flame-resolved simulations, it is shown that the combustion regime near and during blow-off allows applying flamelet methods. However, the scatter of thermochemical states appears to require more than strain and enthalpy as manifold parameters. • Interacting flames in thin/broken reaction zone regime were investigated using LES. • LES was able to recover flame attachments influenced by reactant biases. • Adaptive Mesh Refinement was used to resolve the flame near blow-off. • Parameterising flames near blow-off remains an open question for modeling.
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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 | 0.000 |
| 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".