Combustion Computational Fluid Dynamics Simulations of a Range of Experimental Lean Hydrogen/Natural Gas Blend Flames
Bibliographic record
Abstract
Abstract Hydrogen micromix combustion is a promising technology to reduce the environmental impact of both aero and land-based gas turbines by delivering carbon-free, and potentially ultra-low-NOx, combustion with a greatly reduced risk of autoignition or flashback. A preliminary investigation of such novel injector geometries, that are capable of stabilizing premixed, partially premixed, and diffusion flames, using fuel mixtures ranging from pure methane to pure hydrogen, was performed in an Atmospheric Combustion Rig at the National Research Council Canada (NRC). High quality experimental data was collected using Particle Image Velocimetry (PIV) and both OH and acetone planar laser-induced fluorescence (PLIF). Combustion Computational Fluid Dynamics (CCFD) Methods and Tools were developed and validated against these experimental results, for multiple flame shapes, and combustion modes, resulting from fuel lean mixtures of H2/CH4 ranging from 70%/30% to 90%/10% blends, by volume. Within the current study, Large Eddy Simulations (LES) of a single injector, as well as an array of 5 injectors were investigated. A Flamelet Generated Manifold (FGM), Kinetic Rate, tabulated chemistry approach, using 1D Freely Propagating Flamelets, is used as the combustion model. LES-FGM simulations of the entire experimental injector and test section assembly were performed. Meshing and numerical methodologies were developed, resulting in good agreement for the flame shapes and positions; as well as for the measured thermoacoustic pressure fluctuation amplitude changes, over the range of simulated operating conditions. Further work is underway in applying and assessing the validity of this LES-FGM methodology for flames, with injection systems using micromix technology, at more typical engine conditions, where higher pressures and temperatures are found.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".