Diesel Engine: Applications of Aluminum Alloys
Bibliographic record
Abstract
The Diesel engine, introduced by Rudolph Diesel in 1892, achieves a higher combustion ratio and fuel efficiency, has lower CO2 emissions per mile than the gasoline engine and is considered to be one of the most viable environmentally friendly technologies for vehicles. “Clean Diesel” using lower sulfur content fuel has become available since 2006. Currently, the Diesel engine and cylinder head are mostly cast iron to withstand the high compression pressures and temperatures of Diesel operation. Further weight reduction (40%–55%) via aluminum substitution in the Diesel engine would result in substantial fuel economy and increased environmental benefits. Current aluminum alloys cannot meet the requirements of the Diesel engine and a new research topic has emerged in aluminum materials technology to address these requirements. The main issue with aluminum alloys is the low resistance to thermal fatigue that results from the constrained expansion and contraction of the material in the interval regions leading to compressive creep deformation at 300°C during engine heat-up and to tensile deformation around 150°C during engine cooldown. This article discusses the performance requirements and the design principles for aluminum alloys for Diesel engine applications. Efforts on the modification of A356 and A319 alloys via Cu, Mg, Ni, Cr, V, Zr, Ti, and Mn addition are reviewed. Recent studies on Mn/Mo addition are presented and the related principles are introduced in designing high volume fraction, thermally stable, and uniform nanoscale dispersoids using solutes with opposite partitioning coefficients in aluminum.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".