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 multiyear 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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".