A Perfectly Matched Layer Formulation for Lattice Boltzmann Method
Bibliographic record
Abstract
The Lattice Boltzmann Method (LBM) is a well established computational tool for fluid flow simulations. This method has been utilized for low Mach number Computational Aeroacoustics (CAA). Robust and nonreflective boundary conditions, similar to those used in Navier-Stokes solvers, are needed for LBM-based CAA. The focus of the present study was to develop a special form of absorbing boundary condition based on the perfectly matched layer (PML) concept for LBM. The formulations for both two and three dimen-sional problems are presented. The macroscopic behavior of the new formulation is also discussed. The PML formulation is tested using benchmark acoustic problems. The per-fectly matched layer concept appears to be very well suited for LBM, and yieldes very low acoustic reflection factor. Nomenclature −→c α Lattice velocity vector. f Particle distribution function−→ k Wavenumber vector. M Mach number
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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 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".