Experimental and Statistical ANOVA Analysis on Combustion Stability of CH4/O2/CO2 in a Partially Premixed Gas Turbine Combustor
Bibliographic record
Abstract
Abstract The application of the oxy-fuel combustion technique could tackle the combustion process's environmental issues. Experiments were conducted on partially premixed air- and oxy-methane combustion flames stabilized over a novel perforated burner in the present work. The burner has a premixing ratio of 7.0. In oxy-fuel combustion, the experiments were performed at oxygen fractions (OF%: volumetric percentage of O2 in the oxidizer mixture) of 29%, 32%, and 36% and over a range of operating conditions necessary for a stable flame. The results of oxy-combustion flames were compared with the corresponding air-combustion flames at the same operating conditions. Two sets of statistical analyses were performed for further confirmation of the experimental results. The first set investigated the operating parameters’ effect, including OF and oxidizer Reynolds number (Re), on the upper flammability limits (UFL). Simultaneously, the second set studied the impact of OF and equivalence ratio on flame length. The experimental results revealed that the flammability limits get wider as the OF increases due to the resulting flame speed rise with O2-enrichment. The statistical analysis is conducted by analysis of variance (ANOVA) technique, which carries innovation and confirms that OF and Re significantly impacted the UFL. The visual flame length of oxy-flames was longer than its correspondents of air-flames due to the reduction of flame speed associated with the negative influence of CO2 dilution in oxy-flames. The statistical analysis showed a significant effect of OF and equivalence ratio on the visible flame appearance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".