SIMULATION OF BLOOD FLOW WITH LATTICE BOLTZMANN METHOD
Bibliographic record
Abstract
Understanding blood flow is essential in diagnosing and treating health problems related to blood flow, such as formation of stenosis and blood vessel blockages. Blood contains Red (RBC) and white (WBC) cells beside other constitute floats in plasma. The blood flows in deformable vessels, which is not easy to model and simulate. In general, the flow is unsteady and three dimensional with non-Newtonian behavior. In the literature either blood assumed homogenous fluid with Non-Newtonian flow in small vessels or Newtonian in large vessels. Also, a few authors were considered blood non-homogenous with RBC floating in the plasma. However, the blood viscosity is function of hematocrit. Many computational techniques have been used to simulate blood flow, such as finite element and finite volume methods. Since, early 90s lattice Boltzmann method (LBM) emerged as an alternative method for simulation of fluid flow and heat and mass transfer. The method has many advantages compared with conventional methods. In this paper simulations of blood flow were reviewed. Detail of using LBM in simulation of blood flow is laid out with examples. Also, results of simulations will be presented and discussed.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".