Radiation and Combustion Effects of Hydrogen Enrichment on Biomethane Flames
Bibliographic record
Abstract
Hydrogen has been presented as a promising energy vector in decarbonized economies. Its singular properties can affect important aspects of industrial flames, such as the temperature, emissions, and radiative/convective energy transfer balance, thus requiring in-depth studies to optimize combustion processes using this fuel isolate or in combination with other renewable alternatives. This work aims to conduct a detailed numerical analysis of temperatures and gas emissions in the combustion of biomethane enriched with different proportions of hydrogen, with the intent to contribute to the understanding of the impacts of this natural gas surrogate on practical combustion applications. RANS k-ω and k-ϵ turbulence models were combined with the GRI Mech 3.0, San Diego, and USC mechanisms using the ANSYS-Fluent 2024-R2 softwareto evaluate its performance regarding flame prediction. The Moss–Brookes model was adopted to predict soot formation for the methane flames by solving transport equations for normalized radical nuclei concentration and the soot mass fraction. The Discrete Ordinates (DOs) method with gray band model was applied to solve the Radiation Transfer Equation (RTE). The results of the experiments and numerical simulations highlight the importance of carefully selecting turbulence and chemical kinetics models for an accurate representation of real-scale industrial burners. Relative mean errors of 1.5% and 6.0% were registered for temperature and pollutants predictions, respectively, with the USD kinetics scheme and k-omega turbulence model presenting the most accurate results. The operational impacts of hydrogen enrichment of biomethane flames were accessed for a practical combustion system. With 15% of hydrogen blending, the obtained results indicate a 73% penalty in CO emissions, an increase of 6% in NO emissions, and a 34 K flame temperature increase. Also, a reduction in flame radiation due to hydrogen enrichment was observed for hydrogen concentrations above 20%, a behavior that can affect practical combustion systems such as those in glass and other ceramics industries.
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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".