Gear Ratios Strategy of PROTON Waja CNG-DI Vehicle for Improved Performance
Bibliographic record
Abstract
A 1597cc gasoline CamPro engine was modified to adapt a Direct Injection (DI) technology that uses Compressed Natural Gas (CNG) as a fuel to form Compressed Natural Gas Direct Injection (CNGDI) engine. The modification includes increasing the compression ratio, redesigning the piston crown and cylinder head and a new engine control systems. These changes resulted in engine performance characteristics which is very different from its gasoline origin. This CNGDI engine is to be used with PROTON Waja vehicle body. Due to this change in the characteristics, the transmission systems utilizing an existing gear ratio combination, appears to be unsuitable, particularly for use in automatic transmission. Therefore, new gearbox with appropriate transmission matching needs to be developed. A computer based algorithm was developed for the purpose of predicting the PROTON Waja’s vehicle dynamic performance when CNGDI engine is used. The parameters being considered are maximum speed, acceleration, and elapsed time and these were optimized depending on the engine characteristics such as power, torque, gear ratios, and vehicle design parameters. The results recommended that the gear ratios of 3.58, 1.95, 1.34, 0.98, 0.8 and 4.33 for first, second, third, fourth, fifth and Final Drive (FD) respectively were the most suitable.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".