Equations of state for single-component and multi-component multiphase lattice Boltzmann method
Bibliographic record
Abstract
The lattice Boltzmann method is an alternative method for conventional computational fluid dynamics. It has been used for simulating single-phase and multiphase flows and transport phenomena successfully and efficiently. In the current work, single-component and multi-component multiphase systems are studied. A methodology that differentiates between types of fluids is developed. Moreover, an approach for a multi-component multiphase system is developed in which a single distribution function is used regardless of the number of components. The value of the cohesion parameter (Gf) in the multi-component multiphase model becomes unimportant, like the cohesion parameter (Gp) in the single-component multiphase model, because their effects cancel when calculating the cohesion force. The fluids and mixtures are treated as real, so that mixing rules are used for the mixtures. Several types of fluids and mixtures are considered to investigate the capability of the proposed approach in dealing with miscible mixtures in both azeotrope and non-azeotrope situations. The layered Poiseuille flow and falling droplet on a liquid film are presented to evaluate the model developed. We conclude that this methodology can distinguish between different types of fluids when modeling single-component and multi-component multiphase systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".