Effect of fuel composition on the response of an acoustically forced flat flame
Bibliographic record
Abstract
Interest in alternative fuels for power generation is growing, yet these fuels bring new challenges to gas turbine design and operation.Among these challenges are combustor operability issues, highlighted by problems with combustion instabilities.For this thesis, a fundamental study of the effects of fuel composition on combustion dynamics was undertaken.An acoustically forced flat flame burner was constructed, allowing measurement of the flame transfer function (FTF) relating acoustic perturbations to heat release rate fluctuations in the flame.Tests were done using methane, along with simulated syngas and biogas fuel mixtures over a variety of operating conditions.Large variations in methane concentration had a significant impact on the FTF, while variations in the hydrogen to carbon monoxide ratio did not impact the FTF in fuel mixtures of equal parts methane and syngas.The Strouhal number was found to be an important parameter in predicting phase response independent of the fuel type.Flame liftoff distance and fuel composition were the key parameters determining the peak FTF magnitude.A hypothesis on the role of the non-adiabatic nature of the flat flame and thermal-diffusive effects on the trends in peak FTF magnitude is presented and discussed.I would like to thank my two supervisors, Professor Matthew Johnson and Dr. Wajid Chishty for their guidance throughout my Master's degree.Their technical advice as well as general life advice is truly appreciated.There are many others who have helped along the way.Mike Player at NRC has provided invaluable assistance.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".