Autoignition of Hydrogen / Natural Gas / Nitrogen Fuel Mixtures at Reheat Combustor Operating Conditions
Bibliographic record
Abstract
Previous autoignition studies at conditions relevant to reheat combustor operation have indicated that the presence of relatively small amounts of natural gas (NG) in H2/N2 fuel significantly changes the autoignition behavior. The present study further elucidates the influence of NG on autoignition, kernel propagation, and subsequent flame stabilization at conditions that are relevant for the practical operation of gas turbine reheat combustors (p = 15 bar, Tinlet > 1000 K, hot flue gas, appropriate residence times). The experimental investigation was carried out in a generic, optically accessible reheat combustor. Autoignition events in the mixing zone were recorded by a high-speed camera at frame rates of up to 30,000 fps. This paper describes the autoignition behavior as the H2 volume fraction is increased (decreasing NG) in a H2/NG/N2 fuel mixture for two different jet penetration depths. Additionally, the subsequent flame stabilization phenomena and the structure of the stabilized flame are discussed. The results reveal that autoignition kernels occurred even for the lowest H2 fuel fraction, but they did not initiate a stable flame in the mixing zone. Increasing the H2 volume fraction decreased the distance between the initial position of the autoignition kernels and the fuel injector, finally leading to flame stabilization. The occurrence of autoignition kernels at lower H2 volume fractions (H2/(H2+NG) < 85%) was not found to be significantly influenced by the fluid dynamic and mixing field differences related to the different jet penetration depths. In contrast, autoignition leading to flame stabilization was found to depend on jet penetration; flame stabilization occurred at lower H2 fractions for the higher jet penetration depth (H2/(H2+NG) ≈ 89 compared to H2/(H2+NG) ≈ 95 vol. %).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".