Integration of Lattice Boltzmann-overset method with non-conforming quadtree mesh based on the combination of spatial and Lagrangian-link interpolated streaming technique
Bibliographic record
Abstract
This study integrates the two-dimensional Lattice Boltzmann overset approach with a non-conforming quadtree mesh to address fluid flow problems involving dynamic boundaries. The Lattice Boltzmann overset method employs two grids, one fixed and one movable, which can be computationally intensive due to the dual grid setup. A quadtree mesh is employed to reduce the number of nodes to mitigate this resource-demanding issue. Nonetheless, the use of the quadtree introduces challenges related to varying cell levels and spatial displacements. One of the approaches to address these challenges involves the use of an interpolated particle distribution function streaming technique. This study introduces an interpolation method, which initially applies spatial interpolation as a predictor step. Subsequently, this spatial predictor-interpolated value is utilized for a Lagrangian-link corrector interpolation. Furthermore, the study introduces a node-splitting technique aimed at enhancing the efficiency of the proposed interpolation scheme . The method's order of accuracy is maintained without any degradation as a second order, and the flow around a rotating cylinder validates the method as the results align with previously published data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".