VaSP: Vascular Fluid–Structure Interaction Pipeline
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it