Modelling Blood Flow in an Aorta with Realistic Boundary Conditions
Bibliographic record
Abstract
Computational Fluid Dynamics (CFD) methods together with realistic geometries of arteries, obtained from an MRI data, are commonly used for modelling blood flow in an aorta in scientific research, but also more and more often in clinical applications They enable to assess significant haemodynamic parameters such as velocity profiles, pressure distributions, wall shear stress and oscillatory shear index in physiological and pathological cases. The accuracy of applied boundary conditions and validation of obtained results remain an open question. A typical approach for defining inlet boundary condition is to take the data from available measurements like a phase-contrast magnetic resonance or Doppler ultrasound as a time profile of flow rate To reconstruct a spatial velocity distribution usually a Womersley solution of Navier-Stokes equations is being used Especially when modelling blood flow in an ascending part of an aorta, there is a complicated 4-dimensional velocity profile induced by the movements of the aortic valve. In the presented work, we proposed a methodology of obtaining an accurate inlet velocity condition for an aorta blood flow modelling using local flow measurements done with time-resolved threedimensional flow-sensitive cardiovascular magnetic resonance (4D-flow)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".