Three‐dimensional free surface flow simulation of segregating dense suspensions
Bibliographic record
Abstract
Abstract This paper presents a 3D numerical solution algorithm for the simulation of free surface flows of dense suspensions including particle migration phenomena. Segregation of the solid phase in processes such as powder injection molding and molding of semi‐solid materials affects the rheology of the mixture and therefore the filling pattern. Segregation affects also the final properties and characteristics of such molded parts as a non‐uniform particles distribution leads to non‐uniform shrinkage, warpage and non‐uniform mechanical properties. In this paper, particle migration is modeled using the diffusion flux model of Phillips et al . ( Phys. Fluids A 1992; 4 :30–40) and is extended to address 3D mold filling problems. The solution algorithm is validated against flow problems for which experimental and numerical data are available: circular Couette flow, piston‐driven flow and sudden contraction–expansion flow. The particle migration model is then used to simulate mold filling problems in which the piston movement in the sleeve is known to induce particle migration before the material enters the cavity. An arbitrary Lagrangian–Eulerian (ALE) formulation is developed and combined to a level‐set front capturing method to simulate the piston movement and the evolution of the free surface in molding simulations. The ALE formulation is first compared with an Eulerian solution for the case of the piston‐driven flow problem. The approach is then applied to injection molding problems to study the evolution of particle distributions during molding and in the final molded parts. Copyright © 2008 Crown in the right of Canada. Published by 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".