Characteristics of Deposits in Gas Turbine Combustion Chambers Using Synthetic and Conventional Jet Fuels
Bibliographic record
Abstract
The synthetic fuel industry is poised to experience large scale growth and profoundly affect current aviation fuel infrastructure. New candidate technologies, such as Camelina oil-derived synthetic fuel have been demonstrated to not only provide satisfactory quasi drop-in characteristics for conventional fuels, but in life cycle analysis studies have also been shown to potentially offer positive improvements relative to conventional feedstocks with respect to economic, environmental, and land use considerations. As part of a multi-year study at the Royal Military College of Canada to evaluate combustion related parameters of fuel additives and alternative fuels for gas turbine applications, a Camelina-derived synthetic fuel blend was assessed to determine potential combustion related benefits as compared to conventional and other synthetic blends. The Combustion Chamber Sector Rig (CCSR) which houses a Rolls Royce T-56-A-15 combustion section, was utilized for the evaluation of emissions and deposits. Following combustion testing, several combustion system components, including the combustion chamber, fuel nozzle and igniter plug were analyzed for relative levels of deposit build-up. As with other Fischer Tropsch derived synthetic fuels, there were positive benefits found with Camelina blends in terms of emissions performance and deposit production tendencies.
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 | 0.001 | 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 | 0.001 |
| 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".