Comparison of Hydrogen Micromix Flame Transfer Functions Determined Using RANS and LES
Bibliographic record
Abstract
Abstract Hydrogen has been proposed as an alternative fuel to meet long term emissions and sustainability targets, however due to the characteristics of hydrogen significant modifications to the combustion system are required. The micromix concept utilises a large number of miniaturised diffusion flames to improve mixing, removing the potential for local stoichiometric pockets, flash-back and autoignition. No publicly available studies have yet investigated the thermoacoustic stability of these combustion systems, however due to similarities with lean-premixed combustors which have suffered significant thermoacoustic issues, this risk should not be neglected. Two approaches have been investigated for estimating flame response to acoustic excitations of a single hydrogen micromix injector element. The first uses analytical expressions for the flame transfer function with constants obtained from RANS CFD while the second determines the flame transfer function directly using unsteady LES CFD. Results show the typical form of the flame transfer function but suggest micromix combustors may be more susceptible to higher frequency instabilities than conventional combustion systems. Additionally, the flame transfer function estimated using RANS CFD is broadly similar to that of the LES approach, therefore this may be suitable for use as a preliminary design tool due to its relatively low computational expense.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".