VaSP: Vascular Fluid–Structure Interaction Pipeline
Bibliographic record
Abstract
A variety of commercial and open-source software packages exist for modeling blood flow dynamics and vascular wall mechanics of cardiovascular systems via fluid–structure interaction simulations (FSI). While these existing tools are feature-rich, this breadth often comes at the cost of increased complexity and large codebases, which, combined with implementation in low-level programming languages, effectively limit transparency and accessibility. Moreover, many of these software packages primarily rely on graphical user interfaces for a more intuitive experience; however, this can hinder reproducibility and automation of research workflows. Here, we present Vascular Fluid–Structure Interaction Pipeline (VaSP), a transparent, flexible, and compact Python package with a command-line interface. In contrast to the vast majority of existing software, VaSP is tailored for transitional flow and high-frequency vascular wall vibrations. VaSP takes a medical image-derived surface model as input, generates a volumetric mesh with fluid and solid domains for FSI simulations, and post-processes the results for hemodynamic and wall mechanical analyses. By leveraging high-level Python packages such as FEniCS and VMTK, VaSP ensures accessibility for users of diverse expertise levels and promotes reproducible, scriptable workflows.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.020 |
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".