Development of Aluminium Alloys for High Temperature Applications in Diesel Engines
Bibliographic record
Abstract
Rapid penetration of diesel engines is expected in North America because of their better fuel efficiency and lower greenhouse gas (GHG) emissions. Diesel engine components, particularly cylinder heads, are made of cast irons and replacing them with aluminium alloys could result in a significant weight reduction and consequently better fuel efficiency. Aluminum alloys for diesel engine applications need to withstand higher operating temperatures and pressures as compared to conventional 3xxx based alloys that lose strength above 150oC. This paper presents selected results pertaining to alloy development with improved high temperature performance based on the modified 356 composition. Such alloys with engineered chemical composition and properly designed heat treatment could have improved properties at temperatures up to 250oC. The advanced thermal analysis techniques including dilatometer analysis were used to determine the effect of alloying additions on thermal characteristics including aging kinetics and its impact on casting service performance. Selected structure analysis results including XRD and TEM/EDX as well as elevated temperature tensile testing are presented.
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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 | 0.000 | 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".