A Novel Swirled Trichannel Injector for Hydrogen Combustion
Bibliographic record
Abstract
Abstract Decarbonization is driving the development of various technologies to reduce the environmental impact across multiple fields. In aerospace and other sectors, hydrogen is envisioned as an alternative fuel for carbon-free combustion. As a result, many different combustion technologies are being developed to adapt processes to hydrogen operations. In the context of combustion in an aircraft turbine, new hydrogen combustion technologies must comply with stability and low emission requirements, as the higher reactivity and flame temperature raise concerns about flashback and nitrogen oxide emissions. This work focuses on one specific combustion technology derived from the conventional Rich-Quench-Lean technology in wide use in current aircraft engines. It is based on the injection of a rich premix followed by air injection in order to achieve globally lean conditions and finish combustion. To accommodate this new injection technology, an injector was developed to leverage the unique properties of rich premixed hydrogen flames, particularly their high resistance to strain. To do so, the injector was designed around a concentric arrangement of three channels, with hydrogen being injected in the intermediate channel in order to benefit from two shear layers surrounding the flame. This setup aims to reduce flame temperature and NOx emissions while ensuring a stable flame through the injection of a rich mixture. To increase control over the flame structure, each channel of the injector was fitted with a swirler. The individual and combined effects of these swirlers were investigated to yield an inventory of the flame types possible on the injector. To do so, OH* chemiluminescence imaging was employed. In addition, using a gas analyzer, we monitor NOx emissions in various conditions to evaluate the effects of the different parameters of this new injector.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".