Properties of Ignition Kernel Generated by a Sunken Fire Igniter
Bibliographic record
Abstract
Environmental regulations imposed stringent requirements, including the adoption of biofuels and the reduction of fuel consumption. As these measures are integrated, reliable engine operation, particularly for in-flight re-ignition and high-altitude ground ignition, must be ensured. Flame development and propagation in annular combustion chambers strongly depend on kernel characteristics generated by the igniter used and their interaction with a surrounding environment that is typically challenging to ignite. Therefore, a more detailed understanding of ignition kernel properties under adverse conditions is essential. The primary objective of the present study is to characterize ignition kernel properties using a real helicopter igniter, focusing on the characteristic times at the onset of the ignition phase during spark generation, a stage for which fine-scale data are currently lacking. Such information is crucial for accurately initializing numerical simulations dedicated to predicting ignition success or failure in the engine. To this end, several diagnostic techniques have been employed, including microcalorimetry, schlieren visualization, and filtered plasma chemiluminescence. The results indicate a decrease in energy transfer efficiency and an increase in final ignition kernel volume as initial pressure decreases. An existing simple model has been evaluated for its ability to predict the experimentally obtained results.
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 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".