Flow simulation-based particle swarm optimization for developing improved hemolysis models.

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Medical Device Design

Background:

• Blood-contacting medical devices require high hemocompatibility.
• In-silico trials (flow simulations) are crucial for device optimization.
• Accurate prediction of blood trauma, like hemolysis, is essential but challenging with current numerical models.

Purpose of the Study:

• To propose a novel optimization strategy for improving existing numerical hemolysis models.
• To enhance the development of future hemolysis prediction models for medical devices.
• To investigate the reliability of stress-based hemolysis models.

Main Methods:

• Performed flow simulations for three turbulent blood flow test cases.
• Numerically predicted hemolysis using widely-applied stress-based models.
• Utilized multiple-objective particle swarm optimization (MOPSO) to correlate flow field stresses with measured hemolysis across over one million predictions.

Main Results:

• The proposed optimization approach demonstrated an improvement over existing hemolysis models.
• The study identified deficiencies and limitations inherent in current stress-based hemolysis prediction models.
• Results indicate areas for future research to enhance model reliability.

Conclusions:

• The novel optimization path offers a viable method for refining numerical hemolysis models.
• Further research is needed to address the identified limitations of stress-based models for more reliable hemocompatibility assessment.
• This work contributes to the development of safer and more effective blood-contacting medical devices.