Phase-averaged, 3D OH-LIF reconstruction for multi-nozzle, micromixed hydrogen combustion
Bibliographic record
Abstract
Micromix fuel injection strategies for hydrogen combustion produce multiple, distributed, compact, and often stratified flames. Single injectors can present highly-tridimensional and non-axisymmetric flame structures along the reactive fuel injection wakes. Their integration into multi-nozzle combustion systems, as commonly found in industrial applications, generates increasingly complex interactions between flames produced through this micromix injection and between neighboring nozzles. Two-dimensional, planar laser-based diagnostics can therefore only provide limited insight into the combustion process of these burners. Five premix/micromix injectors, positioned in a cross pattern, burning pure hydrogen are studied in this work. Three-dimensional (3D) OH volumes are interpolated from 25 OH planar laser-induced fluorescence (PLIF) slices over three inline injectors, resulting in a measurement volume spanning ∼2D×6D×3.75D (x×y×z). The laser diagnostic is registered with the acoustics signal to obtain phase-averaged datasets and capture the complex flame dynamics through a complete period. Comparison with single PLIF measurements demonstrates that, while a single slice provides valuable insight, out-of-plane motion and flame-flame interaction between distributed micromix injections and neighboring nozzles require increasingly complex diagnostics. The reconstruction captures flame merging between micromixed, jet-in-crossflow flames within a single nozzle and between injectors. It highlights the importance of injector clocking to mitigate the formation of hot spots in these systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".