Large Eddy Simulation of multi-injector flame blow-off sensitivities to inlet biases
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it