Phase-field modeling of austenitic steels used in turbines
Bibliographic record
Abstract
Abstract The performance in hydro-electric turbine casting and repair requires understanding of how process parameters and chemistry selection affect solidification microstructures. The aim of this study is to provide a quantitative phase-field formulation for process-microstructure relationships that seeks to model stainless steels. We have developed a phase-field model to simulate austenitic stainless steel solidification under experimental thermal histories. To this end we look at a pseudo-binary approximations for numerical efficiency. The pseudo-binary formulation is underpinned by the alloying element equivalent value, a metallurgical tool used to analyze the microstructural impact of “minor” alloying elements in stainless steels. For model validation we develop thin wall casting experiments to measure the thermal history and chemistry controlled microstructure. The models incorporate a thermodynamic parameterization and are linked to a thermal-phase transformation model which represents the experimentally measured thermal history. The results display a good agreement with the primary branch spacing and cellular to dendritic transition of the casting experiments. These models and software provide the basis for future expansion to include more complex microstructures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".