Characterization of a Novel Additive Manufacturing Micromix Nozzle Burning Methane to Hydrogen
Bibliographic record
Abstract
Abstract As the energy landscape transitions to low/zero-carbon fuels, gas turbine manufacturers are targeting fuel flexible operation with natural gas, syngas, and hydrogen-enriched mixtures. Having a single geometry that can support the different fuel blends requested by clients can accelerate the transition to cleaner energy generation and mitigate the environmental impact of gas turbines. Toward this goal, micromix combustion technology has received significant interest, and when coupled with additive manufacturing, novel injector geometries with unique configurations may be capable of stabilizing premixed, partially-premixed, and diffusion flames using fuel mixtures ranging from pure methane to pure hydrogen. In this work, a preliminary investigation of this micromix concept is performed in the Atmospheric Combustion Rig at the National Research Council (NRC) Canada. Flame stability maps are obtained for fuel lean mixtures of H2/CH4 ranging from 0/100, 70/30, 90/10, to 100/0%, by volume. Multiple flame shapes are observed depending on the fuel mixture and combustion mode selected. Particle image velocimetry (PIV), OH, and acetone planar laser-induced fluorescence (PLIF), and acoustic measurements provide additional insights into the combustion process of these novel burners to better understand the stability mechanisms. The quality of the fuel–air mixing from the premixed and micromix injectors is assessed using acetone as a tracer for the fuel, while simultaneous OH-PLIF measurements provide an indication of the postflame regions in the flow. Acoustic measurements complete the current dataset and provide combustion dynamics maps measuring the normalized pressure amplitudes and identifying the dominant frequencies. The preliminary characterization of this additive manufacturing (AM) micromix nozzle shows promising fuel flexibility with wide stability margins and low combustion dynamics for this single nozzle burner.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".