Effects of High Boiling Point Fuel Additives on Deposits in a Direct Injection Gasoline Engine
Bibliographic record
Abstract
The effects of high boiling point fuel additives on deposits were investigated in a commercial turbocharged direct injection gasoline engine. It is known that high boiling point substances have a negative effect on deposits. The distillation end points of blended fuels containing these additives may be approximately 15°C higher than the base fuel (end point: 175°C). Three additives with boiling points between 190 and 196°C were examined: 4-tert-Butyltoluene (TBT), N-Methyl Aniline (NMA), and 2-Methyl-1,5-pentanediamine (MPD). Aromatics and anilines, which may be added to gasoline to increase its octane number, might have a negative effect on deposits. TBT has a benzene ring. NMA has a benzene ring and an amino group. MPD, which has no benzene ring and two amino groups, was selected for comparison with the former two additives. The base gasoline was a Toyota in-house premium grade test gasoline with properties in the range defined by the Japanese Industrial Standards (JIS) (RON: approximately 100) with no detergent content. Test gasolines were prepared by blending the base gasoline with 10% of each additive by volume. The concentration of the additives was set to 10% to accelerate deposit formation. The engine operating conditions for examining deposit formation were an engine speed of 1,600 rpm and medium load. Deposit formation was examined over a period of 30 hours, after which the fuel consumption was approximately 200 L. It was found that amino group additives caused large increases in deposits. Compared to the base gasoline, the piston top deposits were about twice as thick with the TBT blend and about four times as thick with the NMA blend. The MPD blend caused compression leakage after fuel consumption of 10 L because the piston rings stuck to the grooves. Chemical analysis of the deposit formation mechanism suggests that deposits were formed by high boiling point polar substances that penetrated into the quenching zone near the combustion chamber surfaces, and then oxidized, polymerized, or carbonized, and adhered to the surfaces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".