Micro-Mixing Combustion: Experimental Assessment of the Impact of Fuel Preheating on Combustion Stability
Bibliographic record
Abstract
Abstract Micro-mixing combustion is a promising technology in the field of gas turbine combustion because of its safeness against autoignition and flashback while having NOx emissions near premixed levels. This technology is especially well suited for the combustion of hydrogen because of its high reactivity. Because of the potential safety and low NOx emissions of micro-mixing combustion, there is the desire to use this technology with a larger fuel spectrum. However, using micro-mixing technologies, stable combustion is limited to reactive fuels such as hydrogen or high hydrogen fuel blends. Our previous work demonstrated excellent combustion stability with high inlet temperatures up to 1000K with hydrogen, propane, natural gas or Jet fuel A1 as fuels. However, using propane as fuel, stable combustion was limited to inlet temperatures higher than 600K. In this paper, the impact of preheating the fuel prior to its injection on the combustion stability is assessed using propane as fuel because of its stability limitation. For micro-mixing injector configuration discussed in this publication, preheating fuel prior its injection allowed to decrease dynamic instability magnitude under 3 dB for most of the unstable data points (Sound pressure level variation at instability dominant frequency). Those stability gains were obtained while achieving similar levels of NOx emissions. Thermoacoustic instabilities were then linked to observed flame positions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".