Feasibility of Plastic Components in the Cooling System of a Modern IC Engine: A Technical Analysis.
Bibliographic record
Abstract
The push for improved fuel efficiency and reduced carbon dioxide ($CO_2$) emissions in the automotive sector has necessitated a paradigm shift in material selection for internal combustion (IC) engine components. This study evaluates the feasibility of replacing traditional metallic alloys (aluminum and cast iron) with high-performance engineering plastics in the cooling system. Focusing on Polyamide 6.6 with 30% glass fiber reinforcement (PA66-GF30), the research analyzes the mechanical performance, chemical resistance to ethylene glycol, and thermal stability of components such as thermostat housings, water pump impellers, and radiator tanks. The findings suggest that while polymers offer significant advantages in weight reduction (up to 50%) and design complexity, challenges regarding long-term hydrolytic degradation and "softening" under peak thermal loads must be addressed through advanced stabilization and iterative simulation. This study concludes that the transition is not merely a material swap but an architectural evolution requiring non-linear structural analysis, sophisticated vibration damping, and localized cooling strategies to maintain reliability over the vehicle's design life.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".