Micro-Mixing Combustion for Highly Recuperated Gas Turbines: Effects of Inlet Temperature and Fuel Composition on Combustion Stability and NOx Emissions
Bibliographic record
Abstract
Abstract The micromix combustion concept offers an elegant compromise between premixed and nonpremixed combustion. By mixing the fuel and air at the smallest scale possible, one can achieve NOx emissions comparable to premixed combustion while removing the risks of auto-ignition and flashback. Current literature reports multiple micromix designs that achieve low NOx emissions (<10 ppm) with hydrogen or hydrogen-rich fuels at combustor inlet temperatures (CIT) representative of low to medium pressure ratio gas turbines (<650 K). This paper seeks to bridge the gap between current literature and the design requirements of highly recuperated ceramic gas turbines, which should allow low NOx operation with various fuels at combustor inlet temperatures upwards of 900 K. To this end, micromix injection nozzles were designed and tested at small scale to investigate the effects of fuel composition and inlet temperature on combustion stability and NOx emissions. The nozzles were additively manufactured in Inconel 625 having hundreds of holes as small as 0.25 mm. An axial swirler is used to induce recirculation of the products behind the nozzle, which helps stabilize combustion with hydrocarbon fuels due to their longer reaction times and slower flame speeds. Experimental results show that NOx emissions can be decreased down to premixed levels if the jet Damköhler number is kept under a critical value, which requires increasingly smaller holes or higher jet velocities as the inlet temperature increases. Combustion instabilities are observed at low inlet temperatures with hydrocarbons, which are also correlated to the jet Damköhler number.
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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 | 0.001 |
| 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 | 0.000 |
| 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".