A new technique for numerical simulation of dendritic solidification using a meshfree interface finite element method
Bibliographic record
Abstract
Summary A new technique for sharp‐interface modeling of dendritic solidification is proposed using a meshfree interface finite element method such that the liquid–solid interface is represented implicitly and allowed to arbitrarily intersect the finite elements. At the interface‐embedded elements, meshfree interface points without connectivity are imposed directly at the zero level set while meshfree interpolants are constructed using radial basis functions. This ensures both the partition of unity and the Kronecker delta properties are satisfied allowing for precise and easy imposition of Dirichlet boundary conditions at the interface. The constructed meshfree interpolants are also used for solving a variational level set equation based on the Ginzburg–Landau energy functional minimization such that reinitialization is completely eliminated and fast marching algorithms for interfacial velocity extension are not necessary resulting in an efficient algorithm with excellent volume conservation. The meshfree interface finite element method is used for modeling dendritic solidification in a pure melt where it is found suitable in handling the complex interfacial dynamics often encountered in dendritic growth. Mathematical formulation and implementation followed by numerical results and analysis will be presented and discussed. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".