Experimental and Analytical Characterization of Alternative Aviation Fuel Sprays Under Realistic Operating Conditions
Bibliographic record
Abstract
The National Jet Fuel Combustion Program (NJFCP) is an initiative being led by the Office of Environment & Energy at the FAA, to streamline the ASTM jet fuels certification process for alternative aviation fuels. To accomplish this, the program has identified specific applied research tasks in several areas. The National Research Council of Canada (NRC) is contributing to the NJFCP in the areas of sprays and atomization and high altitude engine performance. This paper describes work pertaining to atomization tests using a reference injection system. The work involves characterization of the injection nozzle, comparison of sprays and atomization quality of various conventional and alternative fuels, and uses the experimental data to validate spray correlations. The paper also briefly explores the application viability of a new diagnostic system that has the potential to reduce test time in characterizing sprays. Measurements were made from ambient up to 10 bar pressures in NRC's High Pressure Spray Facility using optical diagnostics including laser diffraction, phase Doppler anemometry (PDA), LIF/Mie imaging and laser sheet imaging to assess differences in the atomization characteristics of the test fuels. A total of nine test fluids including six NJFCP fuels and three calibration fluids were used. The experimental data were then used to validate semi-empirical models, developed through years of experience by engine original equipment manufacturers, and modified under the NJFCP, for predicting droplet size and distribution. The work offers effective tools for developing advanced fuel injectors, and generating data that can be used to significantly enhance multidimensional combustor simulation capabilities.
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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 | 0.001 | 0.001 |
| 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".