Theoretical simulation and experimental validation of inverse quasi-one-dimensional steady and unsteady glottal flow models

2008 ◽  
Vol 124 (1) ◽  
pp. 535-545 ◽  
Author(s):  
Julien Cisonni ◽  
Annemie Van Hirtum ◽  
Xavier Pelorson ◽  
Jan Willems
Keyword(s):  
Experimental Validation ◽  
Theoretical Simulation ◽  
One Dimensional ◽  
Flow Models ◽  
Glottal Flow

Experimental validation of quasi-one-dimensional and two-dimensional steady glottal flow models

2010 ◽  
Vol 48 (9) ◽  
pp. 903-910 ◽  
Author(s):  
Julien Cisonni ◽  
Annemie Van Hirtum ◽  
Xiao Yu Luo ◽  
Xavier Pelorson
Keyword(s):  
Experimental Validation ◽  
Two Dimensional ◽  
One Dimensional ◽  
Flow Models ◽  
Glottal Flow

A ONE-DIMENSIONAL TWO-PHASE FLOW MODEL AND EXPERIMENTAL VALIDATION FOR A FLASHING VISCOUS LIQUID IN A SPLASH PLATE NOZZLE

2015 ◽  
Vol 25 (9) ◽  
pp. 795-817 ◽  
Author(s):  
Mika P. Jarvinen ◽  
A. E. P. Kankkunen ◽  
R. Virtanen ◽  
P. H. Miikkulainen ◽  
V. P. Heikkila
Keyword(s):  
Viscous Liquid ◽  
Experimental Validation ◽  
Flow Model ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
One Dimensional

Fractional-Order Viscoelasticity in One-Dimensional Blood Flow Models

2014 ◽  
Vol 42 (5) ◽  
pp. 1012-1023 ◽  
Author(s):  
Paris Perdikaris ◽  
George Em. Karniadakis
Keyword(s):  
Blood Flow ◽  
Fractional Order ◽  
One Dimensional ◽  
Flow Models ◽  
Blood Flow Models

Sensitivity Analysis and Numerical Stability Analysis of the Algorithms for Predicting the Performance of Turbines

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
Ming Wei ◽  
Yonghong Wang ◽  
Huafen Song
Keyword(s):  
Numerical Stability ◽  
Rounding Error ◽  
One Dimensional ◽  
Flow Models ◽  
Numerical Stability Analysis ◽  
Last Stage ◽  
The Stability ◽  
Performance Calculation ◽  
Poor Stability ◽  
Input Error

Sensitivity and numerical stability of an algorithm are two of the most important criteria to evaluate its performance. For all published turbine flow models, except Wang method, can be named the “top-down” method (TDM) in which the performance of turbines is calculated from the first stage to the last stage row by row; only Wang method originally proposed by Yonghong Wang can be named the “bottom-up” method (BUM) in which the performance of turbines is calculated from the last stage to the first stage row by row. To find the reason why the stability of the two methods is of great difference, the Wang flow model is researched. The model readily applies to TDM and BUM. How the stability of the two algorithms affected by input error and rounding error is analyzed, the error propagation and distribution in the two methods are obtained. In order to explain the problem more intuitively, the stability of the two methods is described by geometrical ideas. To compare with the known data, the performance of a particular type of turbine is calculated through a series of procedures based on the two algorithms. The results are as follows. The more the calculating point approaches the critical point, the poorer the stability of TDM is. The poor stability can even cause failure in the calculation of TDM. However, BUM has not only good stability but also high accuracy. The result provides an accurate and reliable method (BUM) for estimating the performance of turbines, and it can apply to all one-dimensional performance calculation method for turbine.

Verification Benchmarks to Assess the Implementation of Computational Fluid Dynamics Based Hemolysis Prediction Models

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Prasanna Hariharan ◽  
Gavin D’Souza ◽  
Marc Horner ◽  
Richard A. Malinauskas ◽  
Matthew R. Myers
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Power Law ◽  
Analytical Solutions ◽  
Experimental Validation ◽  
Cfd Simulations ◽  
Flow Models ◽  
Flow Acceleration ◽  
Blood Damage ◽  
Eulerian Models

As part of an ongoing effort to develop verification and validation (V&V) standards for using computational fluid dynamics (CFD) in the evaluation of medical devices, we have developed idealized flow-based verification benchmarks to assess the implementation of commonly cited power-law based hemolysis models in CFD. The verification process ensures that all governing equations are solved correctly and the model is free of user and numerical errors. To perform verification for power-law based hemolysis modeling, analytical solutions for the Eulerian power-law blood damage model (which estimates hemolysis index (HI) as a function of shear stress and exposure time) were obtained for Couette and inclined Couette flow models, and for Newtonian and non-Newtonian pipe flow models. Subsequently, CFD simulations of fluid flow and HI were performed using Eulerian and three different Lagrangian-based hemolysis models and compared with the analytical solutions. For all the geometries, the blood damage results from the Eulerian-based CFD simulations matched the Eulerian analytical solutions within ∼1%, which indicates successful implementation of the Eulerian hemolysis model. Agreement between the Lagrangian and Eulerian models depended upon the choice of the hemolysis power-law constants. For the commonly used values of power-law constants (α  = 1.9–2.42 and β  = 0.65–0.80), in the absence of flow acceleration, most of the Lagrangian models matched the Eulerian results within 5%. In the presence of flow acceleration (inclined Couette flow), moderate differences (∼10%) were observed between the Lagrangian and Eulerian models. This difference increased to greater than 100% as the beta exponent decreased. These simplified flow problems can be used as standard benchmarks for verifying the implementation of blood damage predictive models in commercial and open-source CFD codes. The current study used only a power-law model as an illustrative example to emphasize the need for model verification. Similar verification problems could be developed for other types of hemolysis models (such as strain-based and energy dissipation-based methods). And since the current study did not include experimental validation, the results from the verified models do not guarantee accurate hemolysis predictions. This verification step must be followed by experimental validation before the hemolysis models can be used for actual device safety evaluations.

Theoretical simulation and experimental validation of mode-biased wavefront sensor

2011 ◽  
Vol 23 (2) ◽  
pp. 344-348
Author(s):  
刘长海 Liu Changhai ◽  
姜宗福 Jiang Zongfu ◽  
黄盛炀 Huang Shengyang ◽  
习锋杰 Xi Fengjie
Keyword(s):  
Experimental Validation ◽  
Wavefront Sensor ◽  
Theoretical Simulation

Steady one-dimensional nozzle flow solutions of liquid–gas mixtures

2013 ◽  
Vol 737 ◽  
pp. 146-175 ◽  
Author(s):  
S. LeMartelot ◽  
R. Saurel ◽  
O. Le Métayer
Keyword(s):  
Two Phase Flow ◽  
Gas Mixtures ◽  
Ideal Gas ◽  
Nozzle Flow ◽  
Pure Liquid ◽  
Phase Flow ◽  
Two Phase ◽  
One Dimensional ◽  
Flow Models ◽  
Relaxation Effects

AbstractExact compressible one-dimensional nozzle flow solutions at steady state are determined in various limit situations of two-phase liquid–gas mixtures. First, the exact solution for a pure liquid nozzle flow is determined in the context of fluids governed by the compressible Euler equations and the ‘stiffened gas’ equation of state. It is an extension of the well-known ideal-gas steady nozzle flow solution. Various two-phase flow models are then addressed, all corresponding to limit situations of partial equilibrium among the phases. The first limit situation corresponds to the two-phase flow model of Kapila et al. (Phys. Fluids, vol. 13, 2001, pp. 3002–3024), where both phases evolve in mechanical equilibrium only. This model contains two entropies, two temperatures and non-conventional shock relations. The second one corresponds to a two-phase model where the phases evolve in both mechanical and thermal equilibrium. The last one corresponds to a model describing a liquid–vapour mixture in thermodynamic equilibrium. They all correspond to two-phase mixtures where the various relaxation effects are either stiff or absent. In all instances, the various flow regimes (subsonic, subsonic–supersonic, and supersonic with shock) are unambiguously determined, as well as various nozzle solution profiles.

Spectral analysis of glottal flow models

2004 ◽  
Vol 115 (5) ◽  
pp. 2609-2610
Author(s):  
Matthew E. Lee ◽  
Mark J. T. Smith
Keyword(s):  
Spectral Analysis ◽  
Flow Models ◽  
Glottal Flow
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