The Influence of Marangoni Flows on Crack Growth in Cast Metals
Post-publication record
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Bibliographic record
Abstract
In previous work with copper-based alloys, the authors showed that a necessary part of the hot-tearing process is the creation of a void in association with an inclusion and its subsequent growth to form the crack observed in the as-cast state. The work reported here is an examination of hot tearing in aluminum alloy A201 and various similar Al-based aerospace alloys to determine (1) if a similar process to that seen in copper alloys takes place; (2) the extent to which buoyancy forces influence the movement of solute-enriched liquid and so contribute to the development of voids which are subsequently observed as hot tears; and (3) the influence of the local variations in liquid/void surface tension arising due to local composition and temperature changes on interdendritic fluid flow--that is, the effects of Marangoni convection on void size and movement during solidification. Hot cracking of aluminum alloy A201 has been examined under standardized experimental conditions. In addition, experiments were conducted in which fluid flow in test castings was controlled by magnetic fields. The results from these various investigations are presented in this paper. Similar experiments are planned for reduced gravity aircraft parabolic flights. To assist in the planning and interpretation of the results, numerical modeling simulations have been developed for Al-Cu alloys.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".