Performance and emissions of a diesel engine using Al–Fe–Mg–Si pistons contain up to 15.7% volume fractions of Fe-rich intermetallic compounds
Bibliographic record
Abstract
Engine performance and emission characteristics were investigated using a single cylinder four-stroke diesel engine with different concentrated intermetallic-based Al–Fe–Mg–Si pistons. Three different alloy combinations (types A, B, and C) of Al–Fe–Mg–Si pistons were developed through the incremental alloying addition of Fe, Mg, Mn, Cu, and Ni. Piston types A, B, and C had Fe-rich intermetallic compounds (IMC), where types B and C had a higher density IMC distribution than type A. The influence of Fe, Mg, Mn, Cu, and Ni alloyed IMC pistons on engine performance and emissions was investigated at various loading conditions. Combustion characteristics such as cylinder pressure and net heat release rate for all piston types were investigated and compared. A similar duration of ignition was seen for all piston types. Frictional loss was reduced by ∼25% in types B and C in comparison to type A. Similarly, mechanical and thermal efficiency were enhanced considerably in types B and C compared to type A. Emission characteristics were also investigated for all piston types. Results showed that NOx was reduced by ∼17.3% with the use of types B and C.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".