Examining the effect of red blood cell collisions using a fully euleriansimulation approach
Bibliographic record
Abstract
Computational fluid dynamics simulations are employed using the volume of fluid multiphase approach to simulate the fluid structure interaction dynamics of red blood cells in a Couette shear flow. A fully Eulerian structural approach is employed using a finite volume solver to simulate the structural dynamics in order to eliminate the need to Re-mesh the fluid portion of the simulation. Transport equations are defined for the components of the Left Cauchy deformation tensor, from which a solid stress is computed and directly incorporated into the momentum equations. Several improvements are made to the standard fully Eulerian approach for fluid structure interaction. A hyper-elastic strain energy function based on the Yeoh Model fit using optical tweezers that enforces area incompressibility is developed to model the higher-order strain stiffening behavior of the red blood cell. Furthermore, a boundary shape function is developed to compute the distance to the membrane and alleviate issues of numerical diffusion associated with the diffusion of the membrane. The method is implemented in ANSYS Fluent, validated, and compared against the simple analytical solutions for a droplet in a Couette shear flow, as well as compared against images of deformed red blood cells in a shear flow. Multiple Simulations are executed to examine the effect of red blood cell packing on the strain induced on nearby cells during collision vents. It is found that the traction applied on the red blood cell membrane by the fluid is locally increased by up to a factor of three, and depends on red blood cell packing. There is a noticeable local increase in strain observed due to red blood cell collisions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".