A Lagrangian Point Approximation-Based Immersed Boundary–Lattice Boltzmann Method for FSI Problems Involving Deformable Body
Bibliographic record
Abstract
The proposed immersed boundary-lattice Boltzmann method (IB-LBM) with smoothed point interpolation method (S-PIM) has been verified to be an effective tool for simulating complex fluid–structure interaction (FSI) problems in previous works. LBM is employed as fluid solver with a simple solution process, S-PIM is used for largely deformable solids on the basis of gradient smoothing technique, and their combinations for FSI problems are achieved under the framework of immersed boundary method (IBM). IBM allows the coupling method to use a fixed fluid Euler mesh to avoid frequent mesh updates due to the movement or deformation of solids, whereas the introduction of fictitious fluid causes the internal mass effect and yields numerical errors. An extended Lagrangian point approximation approach has been proposed and introduced in IB-LBM with S-PIM to tackle this issue, and numerical experiments for FSI problems associated with rigid movement and large deformation of solids are investigated. It is verified that the accuracy and convergency properties of the present method are significantly improved compared with the original one in which the mass effect was not considered.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".