Lattice Boltzmann Equations-Based Model of the Convection Melt Flow Driven by the Combined Effects of Buoyancy, Surface Tension and Magnetic Body Forces with Heat Generation
Bibliographic record
Abstract
The main topic of this paper is the development of a mathematical model, based on the Lattice Boltzmann equations (LBE), which is proposed for the simulation of the complex convective flow, held in an electrically conducting melt, driven by the combined action of buoyancy, surface-tension, and electromagnetic forces. The lattice Boltzmann method (LBM) is relatively novel and contrasts with the usual well-known methods to physical modeling in the domain of computational fluid dynamics (CFD). Indeed, the LBM describes the fluid (i.e. lattice fluid) at a microscopic level (molecular) and proposes models for the collision between molecules. The full continuum-level physics (i.e. the macroscopic hydrodynamic fields) is implicitly contained in the LB model. Indeed those macroscopic quantities are defined as moments of the so-called particle distribution functions. In the present work, a two-dimensions (2D) LBE-based model is developed to the simulation of convection melt flow driven by the combination of natural buoyancy, surface tension, and electromagnetic forces. The model is applied to numerical modeling of the problems of buoyancy, surface-tension, and electromagnetic driven convection melt flow in an enclosure. The melt system used has a low Prandtl number, which is appropriate to crystal growth melts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".