Comparative study of multicomponent Lattice Boltzmann models for binary mixture flows
Bibliographic record
Abstract
Two lattice Boltzmann method (LBM) models for binary mixture flows are numerically compared. The first model solves the Navier–Stokes equations within the incompressible limit and considers the mixture as one single fluid. A multi relaxation time (MRT) collision operator tunes the fluid diffusivity independently of the fluid viscosity. The second model emerges from a different theoretical derivation of the kinetic theory, where the governing equations are recovered for each species of the mixture. A source term in the LBM defines the interspecies friction force and couples the species of the mixture. A pure diffusion flow and a 2D plane Poiseuille binary mixture flow verify both models in the incompressible limit where diffusive and viscous transport occurs. The influence of molecular mass ratio, dynamic viscosity ratio, and Schmidt number on species and mixture flow behavior is investigated. The numerical results show good agreement against their respective analytical solutions and capture the deviation between the velocity profiles according to the flow regime. The present numerical study underlines the difference between the models as a function of the flow regimes which was observed from the macroscopic governing equations.
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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".