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Non-dimensional parameters for blood-trauma characterization in high-shear flows.

Theodosios Alexander1, Xingbang Chen1, Shahid Imran2

  • 1School of Science and Engineering, Saint Louis University, St. Louis, Missouri, 63103, United States of America.

Medical Engineering & Physics
|February 20, 2026
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This study introduces non-dimensional blood trauma parameters to correlate hemolysis data and optimize cardiovascular devices. The new method organizes blood trauma datasets for AI applications and design improvements.

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cardiovascularhemolysisnon-dimensionalprostheticshearstressvortex

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Area of Science:

  • Biomedical Engineering
  • Fluid Mechanics
  • Cardiovascular Devices

Background:

  • Cardiovascular devices can cause blood trauma, leading to adverse patient outcomes.
  • Current methods for quantifying blood trauma are limited in their ability to correlate data across different conditions.
  • Optimizing device design requires robust methods to predict and minimize blood trauma.

Purpose of the Study:

  • To develop non-dimensional blood trauma parameters for correlating diverse blood trauma data.
  • To improve the optimization of cardiovascular devices using non-dimensional analysis.
  • To establish a framework for organizing blood trauma datasets for AI-driven design.

Main Methods:

  • Formulated non-dimensional descriptors for hemolysis (MIH/LDH), platelet activation, and von Willebrand factor degradation.
  • Utilized non-dimensional exposure time, Capillary number (Ca), Taylor number (Ta), and geometry-related Reynolds numbers.
  • Evaluated the method using MIH/LDH measurements from a Couette blood-shear apparatus across various flow regimes.

Main Results:

  • Developed relations linking non-dimensional hemolysis indicators to exposure time, Ca, Ta, and Reynolds numbers.
  • Successfully collapsed dimensionless hemolysis indices (MIH'' and LDH p'') onto consistent trends across a wide range of operating conditions.
  • Demonstrated the method's ability to organize large blood trauma datasets, facilitating comparison across different experimental setups.

Conclusions:

  • The proposed non-dimensional parameters effectively correlate blood trauma data, enabling consistent analysis.
  • This approach supports data-driven design and generative AI workflows for cardiovascular devices.
  • The findings aid in making informed design choices to reduce blood trauma and improve device safety.