Comparison of multiphase SPH and LBM approaches for the simulation of\n intermittent flows
Bibliographic record
Abstract
Smoothed Particle Hydrodynamics (SPH) and Lattice Boltzmann Method (LBM) are\nincreasingly popular and attractive methods that propose efficient multiphase\nformulations, each one with its own strengths and weaknesses. In this context,\nwhen it comes to study a given multi-fluid problem, it is helpful to rely on a\nquantitative comparison to decide which approach should be used and in which\ncontext. In particular, the simulation of intermittent two-phase flows in pipes\nsuch as slug flows is a complex problem involving moving and intersecting\ninterfaces for which both SPH and LBM could be considered. It is a problem of\ninterest in petroleum applications since the formation of slug flows that can\noccur in submarine pipelines connecting the wells to the production facility\ncan cause undesired behaviors with hazardous consequences. In this work, we\ncompare SPH and LBM multiphase formulations where surface tension effects are\nmodeled respectively using the continuum surface force and the color gradient\napproaches on a collection of standard test cases, and on the simulation of\nintermittent flows in 2D. This paper aims to highlight the contributions and\nlimitations of SPH and LBM when applied to these problems. First, we compare\nour implementations on static bubble problems with different density and\nviscosity ratios. Then, we focus on gravity driven simulations of slug flows in\npipes for several Reynolds numbers. Finally, we conclude with simulations of\nslug flows with inlet/outlet boundary conditions. According to the results\npresented in this study, we confirm that the SPH approach is more robust and\nversatile whereas the LBM formulation is more accurate and faster.\n
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".