The Role of Nucleation Behavior in Phase-Field Simulations of the Austenite to Ferrite Transformation
Bibliographic record
Abstract
Three-dimensional (3-D) phase-field simulations of the austenite (γ) to ferrite (α) transformation during continuous cooling at different cooling rates were performed for an Fe-0.10C-0.49Mn (wt pct) steel, with the aim of studying the interaction between the assumed nucleation temperature range and the effective interfacial mobility when fitting transformation kinetics curves. Ferrite nuclei are assumed to form continuously over a temperature range of δT. An effective interfacial mobility is assumed with an activation energy of 140 kJ/mol and a pre-exponential factor, μ 0. The pre-exponential factor and the nucleation temperature range are used as the only two adjustable parameters to match an experimental reference transformation curve for a particular cooling rate. The initial austenitic microstructure and the nuclei-density input data are based on experimental observations. A number of combinations of values (μ 0, δT) are found to represent the experimental reference curve equally well when related to the accuracy of experimental measurements. The comparison between the simulated and the experimental ferrite grain-size distribution is used as an additional criterion to establish the best estimate of nucleation temperature range and interface mobility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".