A Volume-of-Fluid Based Numerical Simulation of Solidification in Binary Alloys on Fixed Non-Uniform Co-Located Grids
Bibliographic record
Abstract
In this paper, the authors present a platform for the modeling of mold filling and solidification of binary alloys with properties similar to Mg alloys. A volume-of-fluid (VOF) based method is used to capture the interface between solid and liquid in binary alloys solidification process on a fixed non-uniform grid, developed for implementation in a colocated finite volume framework. Contrary to other works, to update the volume fraction (of fluid) in the field, a link between source-based type of energy equation and VOF reconstruction algorithm is described and implemented. A new approximation to the pressure gradient is presented to remove all ‘Spurious Currents’ [1] resulting from pressure jumps in the vicinity of the interface. Based upon the work presented, it is concluded that the present combination of the equations are not only computationally straightforward to implement and upgrade to a 3D problem, but also provides an excellent platform to capture the interface between constituents in a die-casting process including solidification and mold filling process. The current framework will be used in future works to characterize the local mechanical properties of Mg alloys by using information from simulation at the dendritic level.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".