Validation of leading point concept in RANS simulations of highly turbulent lean syngas-air flames with well-pronounced diffusional-thermal effects
Bibliographic record
Abstract
While significant increase in turbulent burning rate in lean premixed flames of hydrogen or hydrogen-containing fuel blends is well documented in various experiments and can be explained by highlighting local diffusional-thermal effects, capabilities of the vast majority of available models of turbulent combustion for predicting this increase have not yet been documented in numerical simulations. To fill this knowledge gap, a well-validated Turbulent Flame Closure (TFC) model of the influence of turbulence on premixed combustion, which, however, does not address the diffusional-thermal effects, is combined with the leading point concept, which highlights strongly perturbed leading flame kernels whose local structure and burning rate are significantly affected by the diffusional-thermal effects. More specifically, within the framework of the leading point concept, local consumption velocity is computed in extremely strained laminar flames by adopting detailed combustion chemistry and, subsequently, the computed velocity is used as an input parameter of the TFC model. The combined model is tested in RANS simulations of highly turbulent, lean syngas-air flames that were experimentally investigated at Georgia Tech. The tests are performed for four different values of the inlet rms turbulent velocities, different turbulence length scales, normal and elevated (up to 10 atm) pressures, various H2/CO ratios ranging from 30/70 to 90/10, and various equivalence ratios ranging from 0.40 to 0.80. All in all, the performed 33 tests indicate that the studied combination of the leading point concept and the TFC model can predict well-pronounced diffusional-thermal effects in lean highly turbulent syngas-air flames, with these results being obtained using the same value of a single constant of the combined model in all cases. In particular, the model well predicts a significant increase in the bulk turbulent consumption velocity when increasing the H2/CO ratio but retaining the same value of the laminar flame speed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".