Phase-averaged, 3D OH-LIF reconstruction for multi-nozzle, micromixed hydrogen combustion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it