Effect of Micromagnetorotation on an MHD Blood Flow through an Idealised Stenosis
Bibliographic record
Abstract
This study examines the effect of micromagnetorotation (MMR) on MHD micropolar blood flow through an idealised stenosis.A numerical solver based on OpenFOAM was developed to analyse such flows, both with and without considering the effect of MMR behaviour.The solver was validated by comparing the numerical results with the analytical solutions of MHD micropolar Poiseuille blood flow with and without the effect of MMR.Here, emphasis is given to the impact of MMR on key characteristics of the flow, including the streamlines as well as the velocity and microrotation profiles both inside and outside the stenotic region.It was observed that the maximum velocity within the stenotic region decreases, while the vortex formed downstream of the stenosis is dampened.Considering the velocity and microrotation distributions within the stenotic region and downstream, it was found that including the MMR term can lead to a velocity reduction of up to 35% and a microrotation decrease of up to 99% when a magnetic field of 8T is applied.It is important to note that the impact of the Lorentz force alone (i.e., without acknowledging the MMR term) on the MHD micropolar blood flow through stenosis is minimal.In conclusion, the influence of MMR on MHD blood flow through stenosis is substantial.The findings of this study are expected to be valuable for bioengineering applications involving high-intensity applied magnetic fields on blood flows, such as magnetic hyperthermia and magnetic drug delivery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".