A 3-D coupled hydromechanical granular model for simulating the constitutive behavior of metallic alloys during solidification
Bibliographic record
Abstract
A three-dimensional (3-D) coupled hydromechanical granular model has been developed and validated to directly predict, for the first time, hot tear formation and stress–strain behavior in metallic alloys during solidification. This granular model consists of four separate 3-D modules: (i) the solidification module is used to generate the solid–liquid geometry at a given solid fraction; (ii) the fluid flow module (FFM) is used to calculate the solidification shrinkage and deformation-induced pressure drop within the intergranular liquid; (iii) the semi-solid deformation module (SDM) simulates the rheological behavior of the granular structure; and (iv) the failure module (FM) simulates crack initiation and propagation. Since solid deformation, intergranular flow and crack initiation are deeply linked together, the FFM, SDM and FM are coupled processes. This has been achieved through the development of a new three-phase interactive technique that couples the interaction between intergranular liquid, solid grains and growing voids. The results show that the pressure drop, and consequently hot tear formation, depends also on the compressibility of the mushy zone skeleton, in addition to the well-known contributors (lack of liquid feeding and semi-solid deformation).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".