Pulsatile non-newtonian blood flow in image-based models of carotid bifurcation

Present hemodynamical study is motivated by the ongoing clinical research at the University Hospital in Pilsen, Czech Republic. On the basis of provided CT scans, several carotid artery models were reconstructed and used for this numerical study of pulsatile blood flow. The blood is modelled as a shear-dependent incompressible fluid, motion of which is described by the non-linear system of Navier-Stokes equations coupled with the Carreau-Yasuda model. The mathematical model is solved using in-house software based on the principle of the SIMPLE algorithm and cell-centred finite volume method (FVM) formulated for hybrid unstructured tetrahedral grids. The discussion of obtained numerical results is performed with special emphasis placed on the analysis of velocity field and distribution of main hemodynamic factors such as cycle-averaged WSS and oscillatory shear index (OSI) in areas prone to atherosclerosis

Numerical analysis of pulsatile blood flow in realistic coronary bypass models

The paper’s objective lies in numerical modelling of pulsatile blood flow in complete aorto-coronary bypass models reconstructed from CT data, especially in models with individual and sequential bypass grafts. Unsteady blood flow is described by the nonlinear system of the incompressible Navier-Stokes equations in 3D, which is numerically solved using developed computational algorithm based on the fully implicit projection method and on the cell-centred finite volume method for hybrid unstructured tetrahedral grids. Obtained numerical results are discussed with regard to distribution of velocity, wall shear stress and oscillatory shear index at proximal and distal anastomoses, i.e., in areas prone to development of intimal hyperplasia

Neural network prediction of the flow field in a periodic domain with hyper-neural network parametrization

This paper is concerned with fast flow field prediction in a blade cascade for variable blade shapes as well as variable Reynolds number using the machine-learning architecture called convolutional neural network. To generate flow field for a specific Reynolds number, an encoder-decoder convolutional neural network, also called U-Net, is used. The values 500, 1000 and 1500 of the Reynolds number are chosen as the training set. Three U-Nets were trained on CFD results for 100 blade profiles, each U-Net for a different Reynolds number. In order to get a prediction for variable Reynolds number, a so-called hypernetwork in employed. The hypernetwork essentially interpolates between the two trained U-Nets. The architecture of the hypernetwork is fully-connected feedforward neural network with one input neuron corresponding to the Reynolds number, one hidden layer and the output layer corresponds to the weights for the interpolated U-Net. The concept of the hypernetwork-based parametrization is tested on a problem of compressible fluid flow through a blade cascade with three unseen blade profiles and unseen Reynolds number

Matematicke modelovani proudeni stlacitelne tekutiny ve vnitrni aerodynamice.

This thesis is focused on the mathematical modelling of two-dimensional and three-dimensional compressible flow problem in internal aerodynamics. The first part of the thesis is devoted to the numerical computation of the compressible inviscid fluid flow. The mathematical model of compressible inviscid flow is described by the nonlinear conservative system of the Euler euqations which is completed with an equation of state defining the thermodynamical properties of the considered fluid. The mathematical properties of this system and the basic types of the boundary conditions which are used for the numerical solution of the hyperbolic system of the Euler equations in 2D and 3D are studied. The main attention of this work is turned towards the numerical solution of the nonlinear system of the Navier-Stokes equations which are discretized by means of the cell-centred finite volume method on a structured quadrilateral grid.Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi