An immersed boundary lattice Boltzmann approach to simulate deformable liquid capsules and its application to microscopic blood flows
Bibliographic record
Abstract
In this paper, we develop an immersed boundary lattice Boltzmann approach to simulate deformable capsules in flows. The lattice Boltzmann method is utilized to solve the incompressible flow field over a regular Eulerian grid, while the immersed boundary method is employed to incorporate the fluid-membrane interaction with a Lagrangian representation of the capsule membrane. This algorithm was validated for the Laplace relationship, the dispersion relationship for interfacial waves and the drag coefficient for cylinders; excellent agreement with theoretical results was observed. Furthermore, simulations of single and multiple red blood cells in shear and channel flows were performed. Several characteristic hemodynamic and hemorheological features were successfully reproduced, including the tank-treading motions, cell migration from the vessel wall, slipper-shaped cell deformation, cell-free layers, blunt velocity profiles and the Fahraeus effect. These simulations therefore demonstrate the potential usefulness of this computational model for microscopic biofluidic systems. However, extension of this algorithm to three-dimensional situations is necessary for more realistic simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".