Numerical study of the impact of hydrogen addition, swirl intensity and equivalence ratio on methane-air combustion
Bibliographic record
Abstract
Abstract Hydrogen-blended fuel is a promising resource for future generations of gas turbine engines, due to its capability of reducing carbon-based emissions. This paper presents a numerical study to assess hydrogen-enriched combustion in a laboratory-scale burner operating at a high turbulence level and under lean and stoichiometric burning conditions. Moreover, a wide range of H2 (up to 90 %) is used for enriching CH4-air combustion in combination with two different swirl levels. The results show that a high swirl intensity results in shorter flames, due to the increased turbulent intensity, which reduces the flame surface area and uniformness the reacting zone. Besides, increasing swirl intensity further increase flame temperature for a given H2-blended fuel. Overall, the results suggest that high swirl intensity in combination to lean mixtures is favorable when using H2-blended fuel with high H2 concentrations. The simulation results also demonstrate that considering radiation heat loss is influential, as it yields a reduction of the outlet temperature by not less than 100 K, bringing down NO x emissions by half.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".