scholarly journals Wall Shear Stress Profile of the Entrance Flow in a Storongly Curved Tube.

1992 ◽  
Vol 58 (548) ◽  
pp. 1098-1103
Author(s):  
Jyunichi SUZUKI ◽  
Naobumi OHISHI ◽  
Kuzuo TANISHITA
Keyword(s):  
Shear Stress ◽  
Wall Shear Stress ◽  
Stress Profile ◽  
Curved Tube ◽  
Shear Stress Profile ◽  
Wall Shear ◽  
Entrance Flow

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

2002 ◽  
Vol 124 (2) ◽  
pp. 176-179 ◽  
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.

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

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.

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

Energies ◽  
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.

Correction: Development of a Two-Dimensional Wall Shear Stress Sensor for Wind Tunnel Applications

2019 ◽  
Author(s):  
Brett Freidkes ◽  
David A. Mills ◽  
Casey Keane ◽  
Lawrence S. Ukeiley ◽  
Mark Sheplak
Keyword(s):  
Shear Stress ◽  
Wind Tunnel ◽  
Wall Shear Stress ◽  
Two Dimensional ◽  
Stress Sensor ◽  
Shear Stress Sensor ◽  
Wall Shear
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