Thermal conductivity modelling of alumina/Al functionally graded composites
Bibliographic record
Abstract
Abstract This paper describes a new manufacturing process for producing functionally graded ceramic‐metal composites with anisotropic properties for thermal management in automotive engine blocks. These composites are elaborated by producing a porosity gradient within an alumina matrix, subsequently infiltrated by a molten aluminum alloy. The interconnected macro porosity inside the ceramic is controlled by the coalescence of PMMA (PolyMethylMethAcrylate) spherical particles during the elaboration step of an organic frame template in which bridges are created at contact points between the particles. The diameter of the interconnection contact plays a key role because it greatly controls the microstructure of the final composites and consequently the thermal conductivity. A numerical model was developed for simulating the different steps of the elaboration process of the composites. Numerical assessments of the effective thermal conductivity are achieved in order to examine interconnection contact effects. To end up, the developed model is validated by comparing the numerical predictions with experimental measurements.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".