Numerical Simulation of Heat Transfer during Solidification of Al–Cu Alloy Ingots Cast in a Cylindrical Mold for Different Conditions
Bibliographic record
Abstract
In this paper, two dimensional numerical simulation of heat transfer during solidification of Al- 4.5 wt. % Cu alloy cast in a cylindrical mold was carried out to specify the optimum solidification conditions. The mold has the dimensions of 150 mm height, 38 mm outer radius, and 8 mm thickness. Four cases were studied for the solidification process; first case is the solidification in the mold without applying any thermal effects at four different mold temperatures of 25, 50, 100 and 200 Ԩ respectively. The second case is insulating the cast from the top. The third case is insulating the upper portion of the mold wall. The last case is adding heat to the upper portion of the mold wall for specific time. For the last three cases, the mold temperature is set to 25Ԩ. The results have shown that the increase in mold temperature only increases the solidification time and it does not significantly affect the temperature distribution and the final cast shape. Insulating the top of the mold made the last solidification region to be at the top of the cast, which leads to get ingot free from the secondary cavity. Insulating a portion of the upper wall of the mold made the cast surface to be more homogeneous with smallest secondary cavity. Heat addition to a portion of the upper wall of the mold leads to obtain a cast with approximately flat surface that is free from secondary cavity in addition to the primary cavity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".