Friction pressure method for simulating solute drag and particle pinning in a multiphase-field model
Bibliographic record
Abstract
In many polycrystalline materials, second phase particles and solute atoms impose a drag pressure on the motion of grain boundaries.The drag effect occurs on a scale comparable to the particle diameter and interface thickness.However, to simulate grain growth with numerical efficiency one requires a model that captures the drag pressure on the interfaces but does not resolve the fine particles or solute segregation spike.In this paper, a multiphasefield model is proposed to simulate the evolution of microstructure under constant and velocity dependent drag pressures.The accuracy of the model is confirmed in comparison with analytical expressions for a shrinking circular grain.Application of the model is presented for grain growth in two dimensions under particle pinning.Measuring curvature of grain-boundary segments reveals that in the completely pinned structure, the average driving pressure is not equal to but lower than the pinning pressure.Considering this effect, the predicted limiting grain size is about three times larger than that assumed in conventional mean-field theories.Based on this observation, a correction factor is introduced for these mean-field models.The proposed phase-field formulation is also applied to simulate grain growth in the presence of solute drag.The grain growth kinetics follows a phenomenological relationship that can be described with a power law with a time exponent in the range 0.35-0.50.The deviation of the time exponent from 0.5, associated with ideal grain growth, and its correlation with the solute drag parameters is discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".