Physiological Stress Modelling and Hemolysis Prediction for High Shear Stress Flows using Computational Hemodynamics


1 Department‎ of Mechanical Engineering, Federal University of Pernambuco, Recife, Pernambuco, 50740-550, Brazil

2 Department‎ of Mechanical Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, São Paulo, 13566-590, Brazil

3 Department‎ of Mechanical Engineering, Federal Institute of São Paulo, São Paulo, São Paulo, Brazil


Predicting hemolysis is a mandatory task when designing blood flow related mechanisms. For decades, researchers have tried to estimate trauma in red blood cell (RBC) for applying in assist mechanisms development, but the specificity and absence of more physical details have limited models for this purpose into ranges of applications. This work aims to present a new method for modelling hemolysis considering a stress threshold that RBC could stand and, bellow that, a Physiological Stress. Complementing this application, simulations in Ventricular Assist Device (VAD) was performed using Computational Fluid Dynamics (CFD) for the hemodynamics. For hemolysis risk analyses, critical regions were established by a mean stress magnitude, also purposed here. The mean stress magnitude is presented including turbulent parameters, trying to reduce the error in calculating the mean stress tensor by mean velocity magnitudes in Reynolds Average Navier-Stokes models for turbulent flows. Five turbulent models were tested: Standard κ-ε, κ-ε RNG, κ-ε Realizable, Standard κ-ω, κ-ω SST and Spalart-Allmaras models. Results indicate similar results for considering Physiological Stress compared to traditional model applications, even using adapted coefficients, what induces specific coefficients for models applying Physiological Stress might improve hemolysis estimations. The κ-ε RNG and κ-ω SST models had better agreement with data and physical expectations and the best scenarios for applying traditional and improved models purposed for future uses.


  • Received: 16 September 2020
  • Revised: 23 December 2020
  • Accepted: 30 December 2020
  • Available online: 10 March 2021