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.

Recent high-fidelity fluid-structure interaction (FSI) simulations of cerebral aneurysms have revealed flow-induced wall vibrations. However, those simulations were conducted under simplified conditions, and the robustness of the predicted vibrations remains unknown. This study aimed to advance the physiological accuracy of previous models and to investigate the sensitivity to parameter uncertainty. We compared the previously used near-linear St. Venant-Kirchhoff wall model with a three-term hyperelastic Mooney-Rivlin (MR3) model fitted to experimental data and also modeled effects of surrounding cerebrospinal fluid (CSF). We then varied flow rate (1.83 mL/s  ± $$ \pm $$  25%), wall stiffness (soft, medium, stiff), and wall thickness (0.25  ± $$ \pm $$  0.1 mm). Our main findings for the four aneurysms considered were as follows: the MR3 model led to an average increase of 35% in pulsation and 240% in vibration amplitude, along with an 18% decrease in frequency. Viscous damping by the CSF reduced the vibration amplitude by 68% but did not affect the frequency or pulsation. Changes in flow rate had no effect on pulsation but increased vibration amplitude by 246%. Wall stiffness and thickness had a comparatively smaller impact on vibration, altering amplitude by 36% and 82% and frequency by 20% and 8%. In conclusion, the more advanced models led to a decrease of vibration amplitude and frequency during the cardiac cycle, consistent with clinical observations. Like computational fluid dynamics, FSI simulations can be sensitive to flow rates but are otherwise robust and can provide a fundamental understanding of aneurysm wall vibration without precise knowledge of wall properties.

Recent computational and experimental studies of intracranial aneurysms have revealed potential mechanisms of aneurysm bruits and murmurs, driven by flow instabilities rather than by stable pulsatile flow. Some of these studies have been conducted under the assumption of constant flow rate (steady flow); however the validity of this assumption has not been evaluated for high-frequency flow instability, or vibrations from fluid-structure interaction (FSI) simulations. We evaluated the time-averaged wall shear stress, flow instability and vibration amplitude of steady flow simulations, performed at both cycle-averaged and peak-systolic flow rates, and compared these to recent pulsatile FSI simulations. Wall shear stress fields of pulsatile flow (time-averaged and peak values) were well-approximated by the respective steady-flow FSI simulations, and the spatial distribution and frequency content of flow instability and vibrations were reasonably approximated by the steady flow simulations at peak-systolic flow rates. However, the level of flow instability and vibration was generally overpredicted by the steady flow simulations at peak-systolic flow rates as flow remained unstable for longer than in the pulsatile simulation, while no flow instability was detected for steady flow at cycle-averaged flow rates. Additionally, the amplitude of flow instability and vibration fluctuated considerably in the steady flow simulations, while the pulsatile simulations exhibited consistent vibration amplitudes (less than 10 % variation at peak systole between cycles). Finally, steady flow simulations at peak-systolic conditions required 2-3x more compute time than the pulsatile simulations for the same time duration. Therefore, we recommend using pulsatile flow simulations when investigating vibrations and flow instabilities.

The mechanism behind hemodialysis arteriovenous fistula (AVF) failure remains poorly understood, despite previous efforts to correlate altered hemodynamics with vascular remodeling. We have recently demonstrated that transitional flow induces high-frequency vibrations in the AVF wall, albeit with a simplified model. This study addresses the key limitations of our original fluid–structure interaction (FSI) approach, aiming to evaluate the vibration response using a more realistic model. A 3D AVF geometry was generated from contrast-free MRI and high-fidelity FSI simulations were performed. Patient-specific inflow and pressure were incorporated, and a three-term Mooney–Rivlin model was fitted using experimental data. The viscoelastic effect of perivascular tissue was modeled with Robin boundary conditions. Prescribing pulsatile inflow and pressure resulted in a substantial increase in vein displacement ( +400 +317 +116 -60

Clinical, experimental, and recent computational studies have demonstrated the presence of wall vibrations in cerebral aneurysms, thought to be induced by blood flow instability. These vibrations could induce irregular, high-rate deformation of the aneurysm wall, and potentially disrupt regular cell behavior and promote deleterious wall remodeling. In order to elucidate, for the first time, the onset and nature of such flow-induced vibrations, in this study we imposed a linearly increasing flow rate on high-fidelity fluid–structure interaction models of three anatomically realistic aneurysm geometries. Prominent narrow-band vibrations in the range of 100–500 Hz were found in two out of the three aneurysm geometries tested, while the case that did not exhibit flow instability did not vibrate. Aneurysm vibrations consisted mostly of fundamental modes of the entire aneurysm sac, with the vibrations exhibiting more frequency content at higher frequencies than the flow instabilities driving those vibrations. The largest vibrations occurred in the case which exhibited strongly banded fluid frequency content, and the vibration amplitude was highest when the strongest fluid frequency band was an integer multiple of one of the natural frequencies of the aneurysm sac. Lower levels of vibration occurred in the case which exhibited turbulent-like flow with no distinct frequency bands. The current study provides a plausible mechanistic explanation for the high-frequency sounds observed in cerebral aneurysms, and suggests that narrow-band (vortex-shedding type) flow might stimulate the wall more, or at least at lower flow rates, than broad-band, turbulent-like flow.

Background Since the 1960s, the origins of intracranial aneurysm bruits and musical murmurs have been debated, with proposed mechanisms ranging from self-excitation (i.e., resonance) by stable pulsatile flow, to vibration caused by unstable (laminar vortex shedding or turbulent) flow. This knowledge gap has impeded the use of intracranial sounds a marker of aneurysm remodelling or rupture risk. New computational techniques now allow us to model these phenomena. Methods We performed high-fidelity fluid-structure interaction simulations capable of understanding the magnitude and mechanisms of such flow-induced vibrations, under pulsatile flow conditions. Six cases from a previous cohort were used. Results In five cases, underlying flow instabilities present as broad-band, random vibrations, consistent with previously-described bruits, while the sac also exhibits resonance, rocking back and forth in different planes of motion, consistent with previously described musical murmurs. Both types of vibration have amplitudes in the range of 0.1 to 1 μm. The murmurs extend into diastole, after the underlying flow instability has dissipated, and do not exhibit the characteristic repeating frequency harmonics of previously hypothesized vortex-shedding mechanisms. The remaining case with stable pulsatile flow does not vibrate. Spectrograms of the simulated vibrations are consistent with previously reported microphone and Doppler ultrasound recordings. Conclusions Our results provide a plausible explanation for distinct intracranial aneurysm sounds and characterize the mechanical environment of a vibrating aneurysm wall. Future work should aim to quantify the deleterious effects of these overlooked stimuli on the vascular wall, to determine which changes to the wall makeup are associated with vibration.

Computational human body models (HBM) present a novel approach to predict brain response in football impact scenarios, with prescribed kinematic boundary conditions for the HBM skull typically used at present. However, computational optimization of helmets requires simulation of the coupled helmet and HBM model; which is much more complex and has not been assessed in the context of brain deformation and existing simplified approaches. In the current study, two boundary conditions and the resulting brain deformations were compared using a HBM head model: (1) a prescribed skull kinematics (PK) boundary condition using measured head kinematics from experimental impacts; and (2) a novel detailed simulation of a HBM head and neck, helmet and linear impactor (HBM-S). While lateral and rear impacts exhibited similar levels of maximum principal strain (MPS) in the brain tissue using both boundary conditions, differences were noted in the frontal orientation (at 9.3 m/s, MPS was 0.39 for PK, 0.54 for HBM-S). Importantly, both PK and HBM-S boundary conditions produced a similar distribution of MPS throughout the brain for each impact orientation considered. Within the corpus callosum and thalamus, high MPS was associated with lateral impacts and lower values with frontal and rear impacts. The good correspondence of both boundary conditions is encouraging for future optimization of helmet designs. A limitation of the PK approach is the need for experimental head kinematics data, while the HBM-S can predict brain response for varying impact conditions and helmet configurations, with potential as a tool to improve helmet protection performance.

It has been proposed that neck muscle activation may play a role in head response resulting from impacts in American Football. The importance of neck stiffness and active musculature in the standard linear impactor helmet test was assessed using a detailed head and neck finite element (FE) model from a current human body model (HBM) compared to a validated hybrid III head and neck FE model. The models were assessed for bare-head and helmeted impacts at three speeds (5.5, 7.4, and 9.3 m/s) and three impact orientations. The HBM head and neck was assessed without muscle activation and with a high level of muscle activation representing a braced condition. The HBM and hybrid III had an average cross-correlation rating of 0.89 for acceleration in the primary impact direction, indicating excellent correspondence regardless of muscle activation. Differences were identified in the axial head acceleration, attributed to axial neck stiffness (correlation rating of 0.45), but these differences did not have a large effect on the overall head response using existing head response metrics (head injury criteria, brain injury criteria, and head impact power). Although responses that develop over longer durations following the impact differed slightly, such as the moment at the base of the neck, this occurred later in time, and therefore, did not considerably affect the short-term head kinematics in the primary impact direction. Though muscle activation did not play a strong role in the head response for the test configurations considered, muscle activation may play a role in longer duration events.

Head injury in contact sports can be mitigated, in part, through the enhancement of protective helmets that may be enabled by detailed finite element models. However, many contemporary helmet FE models include simplified geometry and material properties and have limited verification and validation over a representative range of impact conditions. To address these limitations, a detailed numerical model of a modern football helmet was developed, integrated with two headforms and assessed for 60 impact conditions with excellent ratings (0.79–0.93). The strain energy of the helmet components was investigated for eight impact locations and three impact speeds. In general, the helmet shell had the highest strain energy followed by the compression shocks; however, the facemask and straps had the highest strain energy for impacts involving the facemask. The component strain energy was positively correlated with the Head Injury Criterion, while the strain energy was not strongly correlated with the Brain Injury Criterion due to the dependence on rotational kinematics. This study demonstrated the applicability of a detailed football helmet finite element model to investigate a range of impact conditions and to assess energy distribution as a function of impact location and severity as a means of future helmet optimization.

M.A. Corrales’s name appeared incorrectly on the original publication of this article. It is corrected here.