Recovery of cell-free layer and wall shear stress profile symmetry downstream of an arteriolar bifurcation

Using a simplified two-dimensional divider-channel setup, we simulate the development process of red blood cell (RBC) flows in the entrance region of microvessels to study the wall shear stress (WSS) behaviors. Significant temporal and spatial variation in WSS is noticed. The maximum WSS magnitude and the strongest variation are observed at the channel inlet due to the close cell-wall contact. From the channel inlet, both the mean WSS and variation magnitude decrease, with a abrupt drop in the close vicinity near the inlet and then a slow relaxation over a relatively long distance; and a relative stable state with approximately constant mean and variation is established when the flow is well developed. The correlations between the WSS variation features and the cell free layer (CFL) structure are explored, and the effects of several hemodynamic parameters on the WSS variation are examined. In spite of the model limitations, the qualitative information revealed in this study could be useful for better understanding relevant processes and phenomena in the microcirculation.

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Microscopic Velocimetry With a Scaled-Up Model for Evaluating a Flow Field Over Cultured Endothelial Cells

Journal of Biomechanical Engineering ◽

10.1115/1.1449490 ◽

2002 ◽

Vol 124 (2) ◽

pp. 176-179 ◽

Cited By ~ 2

Author(s):

Shuichiro Fukushima ◽

Takaaki Deguchi ◽

Makoto Kaibara ◽

Kotaro Oka ◽

Kazuo Tanishita

Keyword(s):

Endothelial Cells ◽

Shear Stress ◽

Wall Shear Stress ◽

Flow Field ◽

Cell Model ◽

Stress Profile ◽

Measured Velocity ◽

Cultured Endothelial Cells ◽

Sinusoidal Cell ◽

Wall Shear

A microscopic velocimetry technique for evaluating the flow field over cultured endothelial cells was developed. Flow around a cell model scaled up by a factor of 100 was visualized by using an optical microscope and was quantified by using particle-tracking velocimetry. Wall shear stress on the model surface was determined from a two-dimensional velocity field interpolated from measured velocity vectors. Accuracy of the velocimetry was verified by measuring the flow over a sinusoidal cell model that had a wall shear stress profile analytically determined with linear perturbation theory. Comparison of the experimental results with the analytical solution revealed that the total error of the measured wall shear stress was 6 percent.

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Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning

10.20944/preprints202106.0457.v1 ◽

2021 ◽

Author(s):

Harshal D. Akolekar ◽

Fabian Waschkowski ◽

Yaomin Zhao ◽

Roberto Pacciani ◽

Richard D. Sandberg

Keyword(s):

Machine Learning ◽

Shear Stress ◽

Wall Shear Stress ◽

Low Pressure ◽

Stress Profile ◽

Transition Model ◽

Optimization Approach ◽

Multi Objective Optimization ◽

Multi Objective ◽

Wall Shear

Existing Reynolds Averaged Navier-Stokes based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this study, a novel framework based on computational fluids dynamics driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models which can improve the transition prediction for the T106A low pressure turbine at an isentropic exit Reynolds number of Re2is=100,000. Model formulations are proposed for the transfer and laminar eddy viscosity terms of the laminar kinetic energy transition model using seven non-dimensional pi groups. The multi-objective optimization approach makes use of cost functions based on the suction-side wall-shear stress and the pressure coefficient. A family of solutions is thus developed, whose performance is assessed using Pareto analysis and in terms of physical characteristics of separated-flow transition. Two models are found which bring the wall-shear stress profile in the separated region at least two times closer to the reference high-fidelity data than the baseline transition model. As these models are able to accurately predict the flow coming off the blade trailing edge, they are also able to significantly enhance the wake-mixing prediction over the baseline model.

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Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning

Energies ◽

10.3390/en14154680 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4680

Author(s):

Harshal D. Akolekar ◽

Fabian Waschkowski ◽

Yaomin Zhao ◽

Roberto Pacciani ◽

Richard D. Sandberg

Keyword(s):

Machine Learning ◽

Shear Stress ◽

Wall Shear Stress ◽

Low Pressure ◽

Stress Profile ◽

Transition Model ◽

Multi Objective Optimization ◽

Multi Objective ◽

Transition Prediction ◽

Wall Shear

Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models which can improve the transition prediction for the T106A low pressure turbine at an isentropic exit Reynolds number of Re2is=100,000. Model formulations are proposed for the transfer and laminar eddy viscosity terms of the laminar kinetic energy transition model using seven non-dimensional pi groups. The multi-objective optimization approach makes use of cost functions based on the suction-side wall-shear stress and the pressure coefficient. A family of solutions is thus developed, whose performance is assessed using Pareto analysis and in terms of physical characteristics of separated-flow transition. Two models are found which bring the wall-shear stress profile in the separated region at least two times closer to the reference high-fidelity data than the baseline transition model. As these models are able to accurately predict the flow coming off the blade trailing edge, they are also able to significantly enhance the wake-mixing prediction over the baseline model. This is the first known study which makes use of `CFD-driven’ machine learning to enhance the transition prediction for a non-canonical flow.

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