An Interface Capturing Procedure for Simulating Incompressible Two-Phase Flows on Adaptive Unstructured Grids
Bibliographic record
Abstract
To design many industrial systems, accurate and efficient simulations of complex two-phase flows are required. In this context, handling complex geometries becomes necessary. The use of unstructured grids fulfills this requirement, and with Adaptive Mesh Refinement (AMR) computational resources can be allocated according to need. This work presents an Accurate Conservative Level-Set/Ghost-Fluid algorithm for unstructured grids, implemented in the YALES2 incompressible finite-volume flow solver. In the ACLS framework, the interface is defined as the iso-contour of a hyperbolic tangent function, which is advected by the fluid, and reshaped using a reinitialization equation. A new form of this equation, that better preserves the interface shape, has been recently proposed by Chiodi et al, and we extend it to unstructured grids in this study. To compute interface normals and curvature, the signed-distance function is reconstructed in a narrow band around the interface using a geometric-projection marker method. Isotropic AMR is automatically triggered based on interface displacement. Interface transport and two-phase flow tests are firstly simulated to validate the procedure. We then perform LES of a water jet in quiescent air from a low-pressure compound nozzle.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".