A modified continuum surface force (M-CSF) model for two-phase flow problems in smoothed particle hydrodynamics
Bibliographic record
Abstract
This work proposes a modified interfacial tension model based on the continuum surface force (M-CSF) in the context of smoothed particle hydrodynamics. This correction aims to enhance computational stability, improve force evaluation precision, and increase symmetry around the interface. Several two-phase flow benchmarks are solved using both the conventional and proposed CSF models, and the results are compared with each other and with the available literature. The results indicate that the modified model can efficiently increase the force evaluation accuracy in the pressure field at the interface. For instance, the relative error in pressure calculation using the proposed and conventional CSF models is 0.05% and 3.5%, respectively, when compared to the analytical solution, with both models having the same particle resolutions for the droplet deformation problem. In predicting the critical surface tension for hydrodynamic instabilities, such as the Rayleigh-Taylor instability, the M-CSF methodology exhibits much better alignment with existing theories, showing less than 5% deviation, while conventionally used CSF models can deviate up to 15% for the same problem. These findings confirm the superiority of the proposed methodology in evaluating interfacial forces, even in complex hydrodynamic instabilities.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".