A sharp interface immersed boundary approach for simplified and highly stable lattice Boltzmann method
Bibliographic record
Abstract
The Lattice Boltzmann Method is a mesoscopic method and has been used for some time as a computational fluid dynamics solver. The recently developed Simplified and Highly Stable Lattice Boltzmann Method (SHSLBM) simplifies the boundary condition implementation to a great extent as it can be implemented using the macroscopic variables instead of distribution functions in traditional Lattice Boltzmann Method. Moreover, it can simulate incompressible flows without evolution of the distribution function, making it less computationally expensive. However, it is still a challenge to implement it for flow around geometry not aligned with the mesh. Immersed boundary Method is used for the flow around geometries without body fitted mesh therefore making it as an ideal choice to extend SHSLBM to simulate flow around geometries not aligned with the mesh. In the present work, for the first time a combination of a sharp interface Immersed Boundary approach is implemented for the boundary treatment of the non-aligned geometry for Simplified and Highly Stable Lattice Boltzmann Method. The implementation is done using an extrapolation of the macroscopic flow variables to the ghost (solid) nodes using a Least-Square technique, for both Neumann and Dirichlet boundary type. The present work is verified for a Poiseuille flow test case and a code-to-code verification is done for flow around square cylinder in a channel, with Immersed Boundary, and a comparison is made with flow around square cylinder without Immersed Boundary.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".