A fluid-structure interaction analysis of the human aortic arch under inertial load
Bibliographic record
Abstract
Presented is an investigation into the use of numerical methods for modelling the effects of inertial load on the human cardiovascular system. An anatomically correct geometry was developed based on three-dimensional computed tomography (CT) data and independent meshes were created for the solid and fluid regimes. These domains were simulated using independent solvers and subsequently coupled using an intermediate data transfer alogrithm. At the inlet of the arch, a pulsatile velocity boundary condition was enforced replicating the cardiac cycle. Time invariant, resistive boundary conditions were used at all outlets and a linear isotropic constitutive model was used for tissue response. Verification was conducted by comparing simulation results at standard earth gravity (9.81 m/s²) with published values for velocity, mass flow rate, deformation, and qualitative flow behaviour. The presented fluid-structure interaction (FSI) model shows strong agreement with accepted normal values. Inertial load was then applied along the longitudinal axis of the arch in multiples of standard gravity to a maximum of 8+Gz. This load increased arch flow velocities, and reduced mass flow in the ascending brachiocephalic and carotid arteries. Blood flow from the arch to the upper body and brain ceased near 8+Gz. Although the presented results are preliminary, the feasibility of such an analysis has been successfully demonstrated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".