NOx Emissions Predictions for a Hydrogen Micromix Combustion System
Bibliographic record
Abstract
Abstract Being free from carbon content, hydrogen has been considered as a promising candidate to reduce pollutant emissions in Gas Turbine Combustion Systems. Due to hydrogen’s significantly different burning characteristics, its implementation requires adjustments to the design philosophies of traditional combustion chambers. The micromix concept offers an alternative diffusive combustion injection system, improving the mixing characteristics without the risk associated with pre-mixing, thereby reducing the likelihood of hotspots forming. The importance of turbulence-chemistry interaction modelling, particularly for highly diffusive flames such as hydrogen, has been widely addressed. A turbulence-chemistry interaction study on such a micromix injector was performed investigating the coupling between the Flamelet Generated Manifold (FGM) combustion model and different hydrogen reaction mechanisms. This methodology correctly reproduces the typical micromix micro-flame behaviour and the analysed mechanisms are shown to be in good agreement in terms of flow characteristics prediction. A comparative study between two reduced order emissions prediction models was then carried out: a CFD post-processing technique for NOx emissions calculations and a hybrid CFD-CRN approach were explored. Due to the coupling between accurate turbulence-chemistry interaction modelling and the ability to handle detailed chemistry, the hybrid CFD-CRN approach gives valuable results with a modest computational cost and it could be used as an optimising tool during the injector geometry design process.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".