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With the development of computing technology and fluid mechanics, computer simulation by Computational Fluid Dynamics (CFD) was used on study of cerebral aneurysm hemodynamics and achieved some valuable results.
Using CFD technology, we studied the impact of arterial geometrical changes on blood flow patterns in the bifurcation, analyzed the blood velocity and wall pressure.
The results of numerical simulation included the value of velocity field and dynamics pressure.
Hemodynamics simulation of carotid artery bifurcation.
Hemodynamics Simulation of the Intracranial Bifurcation Vessel with Different Shape.

Yeltsin a haitanatoliy@gmail.com, hait@mail.ru, b referetSF@yandex.ru Keywords: Ranque-Hilsch effect, energy separation, temperature separation, vortex tube, computational fluid dynamics, numerical simulation, energy conservation equation.
Standard k-ε turbulence model had been used in the simulations.
Numerical solution of the equations system was carried out in open source CFD software OpenFOAM.
Numerical simulation results Results of numerical simulations of air spiral flow with use of all three types of energy conservation equation are presented on Fig. 2 and Fig. 3.
Turbulence modeling for CFD.

Import the flow channel model into meshing in CFD software based on the internal flow channel geometry and flow characteristics.
Numerical Simulation of Fluid Field Modeling and meshing of pressure reducing valve.
Control equations of numerical simulation.
The equations are as follows: (4) Numerical simulation calculation and analysis of results.
The fluid field characteristics were accurately analyzed by using CFD simulation experiment.

The value of the resistance coefficient computed using the CFD codes should be considered approximate only until it is verified by using either a physical experiment or a detailed numerical calculation which allows a direct simulation of the actual geometry of a covered smokestack.
Iaccarino, Numerical simulation of the flow around a circular cylinder at high Reynolds numbers, J.
Breuer, A challenging test case for large eddy simulation: high Reynolds number circular cylinder flow, J.
Lu, Large-Eddy and Detached-Eddy Simulations of the separated flow around a circular cylinder, J.
Zhang, Unsteady RANS and detached-eddy simulations of flow around a circular cylinder in ground effect, J.

Therefore, the shearing stress and relative velocity simulations were executed using the commercial computational fluid dynamics (CFD) software FLOTRAN developed by ANSYS.
Fig. 2 Finite element mesh model The CFD Model.
Considering the working zone was axial symmetric, quarter of the annular region between the work-piece and the wall of container was selected as the analysis region, and the FLUID142 was selected as the CFD element.
Based on the above CFD model, the velocity isolines (Fig. 3) and velocity vectorgraphs (Fig. 4) of FMA with the work-piece rotated at the rotation speed of 600r/min, 900r/min and1600r/min respectively were obtained.
There are some differences between the simulation results and the static yield shearing stress.

In the process, ANSYS-FLOTRAN CFD program is employed.
Finite element method ANSYS-FLOTRAN CFD is an advanced tool used to analyze the two and three dimensional flowing fields[2].
It usually includes three steps as follows: model building, loads applying and equation solving. 3.1 model building （1）Analysis type choice:Choose the analysis function of ANSYS-FLOTRAN CFD
Conclusions Using the finite element method and ANSYS-FLOTRAN CFD program,the temperature field is calculated in different temperatures.The calculation results and analysis results show: (1) The temperature difference between two thermistors changes with the tilt angle's change
[2] Guoqiang Wang, Numerical simulation and practice on ANSYS [M], Xi'an: Northwest Industry University Press, 1999. 221

This paper aims to discuss the CFD analysis on the flame and flue gas temperature distribution in a full scale microgas turbine operating on syngas.
Simulation results with syngas show similar flame temperature distribution as natural gas combustion.
A computational fluid dynamics (CFD) analysis was done on a full scale micro gas turbine.
In CFD, quality of mesh plays important role for the calculation of flow properties.
Lazzaretto, Numerical simulation of a hydrogen fuelled gas turbine combustor, International Journal of Hydrogen Energy 36 (2011) 7993-8002

To find out and compare the performance of both of turbines, the numerical study was conducted using commercial CFD Ansys.
Simulations are using Fluent with analysis type of 3D and viscous model kω-SST.
Figures 4 and 5 showed the turbine models and CFD analysis result of fluid flow inside turbines.
The efficiencies of VLHSRT are dominantly higher than VLHCRT for every single of flow rate simulations.
Static pressure distribution inside VLHCRT Simulation results for blade loading charts have been shown on figure 14.

With the development of computer technology, numerical simulation has been widely used.
Numerical simulation greatly improves the efficiency of system development and reduces experimental costs.
Xiayi Yuan [2] used the numerical simulation methods, studied the cabin heat flow field in detail.
Coupled model Based on the following assumptions, the CFD three-dimensional model of engine cabin has been established: 1.
For further simplifying this CFD model, an interface-fan model has been used as a cooling fan.

The methodology of wind resource assessment based on Monte Carlo simulation methodology was proposed to be able to evaluate wind generation covering large area [4].
The microscale wind resource map based on CFD wind flow modelling is displayed in Figure 4.
It was found that wake loss was 0.59% and 0.60% for linearized and CFD wind flow modelling for 5X2.0 MW wind farm.
Gross AEP (GWh/year) Wind Flow Modeling WTG Model A (5X2.0 MW) WTG Model B (4X2.5 MW) Linearized Model 18.915 35.080 CFD Model 21.025 36.756 Wake Loss (%) Wind Flow Modeling WTG Model A (5X2.0 MW) WTG Model B (4X2.5 MW) Linearized Model 0.59 0.36 CFD Model 0.60 0.40 Net AEP (GWh/year) Wind Flow Modeling WTG Model A (5X2.0 MW) WTG Model B (4X2.5 MW) Linearized Model 18.804 34.955 CFD Model 20.900 36.600 Capacity Factor (%) Wind Flow Modeling WTG Model A (5X2.0 MW) WTG Model B (4X2.5 MW) Linearized Model 21.47 39.90 CFD Model 23.86 41.78 Cost breakdown and benefit breakdown are shown in Fig. 6.
The net AEP for 4X2.5 MW wind farm is 36.6 GWh/year based on CFD wind flow modelling while wake loss is 0.4%.