An experimental comparison of the effects of jet fuel (F-34) and diesel (F-54) on the wear of fuel system components and fuel consumption of an internal combustion diesel engine
Bibliographic record
Abstract
The logistics of maintaining a fuel supply is a significant consideration for the military. Members of the North American Treaty Organization (NATO) prefer to use a single fuel to avoid problems with fuel transport and storage. Thus, we chose the kerosene-based F-34 jet fuel as the “single fuel” for testing on a land-based vehicle. We examined the feasibility of using F-34 jet fuel in a diesel engine and compared the performance of the jet fuel with a conventional F-54 diesel fuel. We compared the power and torque characteristics of an M52 diesel engine, as well as fuel consumption as a function of engine speed and torque using F-34 jet fuel and F-54 diesel fuel. Moreover, wear of the fuel pumps in Magirus Unimog and MAN trucks was measured during long-term tests operating with F-34 and F-54 fuels, respectively. We found that there was no obvious difference in engine power and torque using F-34 or F-54 fuel.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".