Micro Particle Image Velocimetry and Numerical Investigation of Micro Couette Blood Flow
Bibliographic record
Abstract
The purpose of the work presented this paper is to design a model to study experimentally and numerically a micro-Couette blood flow to obtain a constant and controlled shear rate that is a suitable environment for analysis of Red Blood Cell (RBC) aggregation. Due to the simplicity of the flow conditions, aggregate size can be related to the constant shear rate applied. This Couette flow is created by the motion of a second fluid that entrains the blood. The experimental work is coupled with 3D numerical simulations performed using a research computational fluid dynamics solver, Nek5000, based on the spectral element method, while the experiments are conducted using a micro-particle image velocimetry system. Two models of microchannels, with different dimensions, 150 × 33μm and 170 × 64μm, are fabricated in the laboratory using standard photolithography methods. The design of the channel is based on several parameters determined by the simulations. A Newtonian model is tested numerically and experimentally. Blood is then tested experimentally to be compared to the simulation results. We find that using a velocity ratio of 4 between the two Newtonian fluids, we create a flow where one third of the channel thickness is filled with the fluid destined to be blood. In the blood experiments, the velocity profile in this layer is approximately linear, resulting in the desired controlled conditions for the study of RBC aggregation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".