Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

312
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
312
Typical Model Studies01:30

Typical Model Studies

602
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
602
Navier–Stokes Equations01:28

Navier–Stokes Equations

2.0K
For incompressible Newtonian fluids, where density remains constant, stresses show a linear relationship with the deformation rate, defined by normal and shear stresses. Normal stresses depend on the pressure exerted on the fluid and the rate of deformation in specific directions, which determines how fluid flows under varying pressures. Shear stresses, on the other hand, act tangentially across fluid layers. They explain how adjacent fluid layers slide relative to one another, connecting...
2.0K
Bernoulli's Equation for Flow Along a Streamline01:30

Bernoulli's Equation for Flow Along a Streamline

1.4K
Bernoulli's equation relates the energy conservation in a fluid moving along a streamline. The equation applies to incompressible and inviscid fluids under steady flow. For such a flow, Newton's second law is applied to a small fluid element, which experiences forces due to pressure differences, gravity, and velocity variations. The force balance leads to the following form of Bernoulli's equation:
1.4K
Autoregulation of Blood Flow01:17

Autoregulation of Blood Flow

7.4K
Autoregulation mechanisms are characterized by their inherent capacity for self-regulation without necessitating specific nervous stimulation or endocrine control. These mechanisms facilitate the adjustment of blood flow and, therefore, perfusion specific to each tissue region. This self-regulation encompasses chemical signals and myogenic controls.
Chemical Signaling in Autoregulation
Chemical signaling operates at the precapillary sphincter level, inciting either contraction or relaxation....
7.4K
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

363
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures enhance...
363

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

MRI-Based Pressure Gradient Mapping in Patient-Specific Models of Coarctation of the Aorta.

medRxiv : the preprint server for health sciences·2026
Same author

Impact of guideline definitions on right ventricular diameter in echocardiography: an automated analysis in controls and patients with pulmonary hypertension.

Echo research and practice·2026
Same author

SDFStent: Real-time interactive virtual stenting via SDF deformation fields.

ArXiv·2026
Same author

Per-vessel myocardial blood flow improvement after coronary artery bypass graft surgery quantified by CT myocardial perfusion imaging.

Journal of cardiovascular computed tomography·2026
Same author

A Continuum of Atrial Peristalsis Initiates the Bicuspid to Quadricuspid Valve Transition.

bioRxiv : the preprint server for biology·2026
Same author

Simulations predict improved valve performance without direct leaflet intervention after neonatal truncus arteriosus repair.

The Journal of thoracic and cardiovascular surgery·2026
Same journal

SynTME: A tumor microenvironment-aware, pharmacology-inspired multi-stage framework for drug synergy prediction.

Computer methods and programs in biomedicine·2026
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.3K

Data-driven bifurcation handling in physics-based reduced-order vascular hemodynamic models.

Natalia L Rubio1, Eric F Darve2, Alison L Marsden3

  • 1Stanford University - Department of Mechanical Engineering, United States of America.

Computer Methods and Programs in Biomedicine
|January 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning-enhanced numerical model for cardiovascular blood flow simulations. The new model significantly improves accuracy in predicting blood flow dynamics at vessel bifurcations, making it suitable for clinical applications.

Keywords:
Bifurcation pressure lossesCardiovascular flowsReduced-order modelingVascular trees

More Related Videos

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

7.0K
Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery
06:18

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Published on: December 6, 2024

966

Related Experiment Videos

Last Updated: Jan 7, 2026

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.3K
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

7.0K
Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery
06:18

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Published on: December 6, 2024

966

Area of Science:

  • Computational fluid dynamics
  • Cardiovascular biomechanics
  • Machine learning in medicine

Background:

  • High-fidelity 3D simulations of cardiovascular flows are computationally intensive, limiting clinical use.
  • Reduced-order models (ROMs) are efficient but lack accuracy, especially at vessel bifurcations.
  • Standard assumptions in ROMs fail to capture complex flow physics at bifurcations.

Purpose of the Study:

  • To develop an enhanced numerical framework integrating machine learning with hemodynamic solvers.
  • To improve the accuracy of reduced-order models (ROMs) for cardiovascular flow simulations.
  • To maintain computational efficiency for clinical applicability.

Main Methods:

  • Developed a resistor-resistor-inductor (RRI) model using neural networks to predict pressure-flow relationships at bifurcations.
  • Incorporated linear/quadratic resistance and inductive effects, using physics-based non-dimensionalization.
  • Integrated the RRI model into a zero-dimensional (0D) cardiovascular flow model and validated against 3D simulations.

Main Results:

  • The RRI method reduced inlet pressure errors from 45% (54 mmHg) in standard 0D models to 17% (25 mmHg).
  • A simplified resistor-inductor (RI) variant achieved 26% (31 mmHg) error.
  • Enhanced models showed particular effectiveness at high Reynolds numbers and in complex vascular networks.

Conclusions:

  • The hybrid numerical approach enables accurate, real-time hemodynamic modeling.
  • This method supports clinical decision support, uncertainty quantification, and digital twin applications.
  • Facilitates advancements in cardiovascular biomedical engineering.