Ignition Delay Time Experiments for Natural Gas/Hydrogen Blends at Elevated Pressures
Bibliographic record
Abstract
Applications of natural gases that contain high levels of hydrogen have become a primary interest in the gas turbine market. While the ignition delay times of hydrogen and of the individual hydrocarbons in natural gases can be considered well known, there have been few previous experimental studies into the effects of different levels of hydrogen on the ignition delay times of natural gases at gas turbine conditions. To examine the effects of hydrogen content at gas turbine conditions, shock-tube experiments were performed on nine mixtures of an L9 matrix. The L9 matrix was developed by varying four factors: natural gas higher-order hydrocarbon content of 0, 18.75, or 37.5%; hydrogen content of the total fuel mixture of 30, 60, or 80%; equivalence ratios of 0.3, 0.5, or 1; and pressures of 1, 10, or 30 atm. Temperatures ranged from 1092 K to 1722 K, and all mixtures were diluted in 90% Ar. Correlations for each mixture were developed from the ignition delay times and, using these correlations, a factor sensitivity analysis was performed. It was found that hydrogen played the most significant role in the ignition delay times of a mixture. Pressure was almost as important as hydrogen content, especially as temperature increased. Equivalence ratio was slightly more important than hydrocarbon content of the natural gas, but both were less important than pressure or hydrogen content. Comparison with a modern chemical kinetic model demonstrated that the model captures well the relative impacts of H2 content, temperature, and pressure, but some improvements are still needed in terms of absolute ignition delay times.
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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) | 0.001 | 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 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".