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The computational simulations were performed using a Reynolds Averaged Navier-Stokes 3D CFD code with included Shear Stress Transport turbulence model.
Results of the performed steady simulations demonstrated that when the flow was swirled to 50% of the rim speed, the deviated flow left the cavity with less cross flow.
Numerical formulation Numerical simulations were fulfilled in Fluent.
[20] Henrik Orsan, Investigation of secondary flow in low aspect ratio turbines using CFD, Ph.
[22] Gardzilewicz A., Lampart P., Świrydczuk J., Kosowski K., Investigation of flow losses in turbine blading systems using CFD, Part I.

In the turbulent flow over a backward facing step simulation [2], Menter closure predicted with the closest recirculation length to experimental result [3] among a set of popular turbulence models.
The max percentage of error between results obtained from our simulation to the published standard velocity profiles (for the finest grid) was estimated to be approximately 0.023%.
In Fig. 7, 2D simulation tends to overestimate the leeward velocity profiles, which yield average 6.7% difference in comparison with 3D.
Cheah, Wall y+ approach for dealing with turbulent flows over a surface mounted cube: part 2 - high Reynolds number, in: Proceedings of Seventh International Conference on CFD in the Minerals and Process Industries, Melbourne, Australia, 2009
Nikitin, Direct numerical simulation of turbulent flow around a wall-mounted cube: spatio-temporal evolution of large-scale vortices.

It has three important features comparing with the other conventional CFD models: (i) the convection operator (or streaming operator) in phase space (or velocity space) is linear.
A parabolic inlet velocity profile for flow entering to the channel was assumed for the present simulations.
Computational simulations were performed for Re = 300, and the grids are 120´120.
A two dimensional D2Q9 BGK model was used in the simulation.
In order to confirm the accuracy of the present LBM simulations, we also did the work.

Numerical Simulation Analysis of Monotectic Alloy during Solidification In this paper, the CFD(Computational Fluid Dynamics) commercial software and above-established mathematical model are used to achieve the numerical analysis of Al-5wt% Pb alloy and Al-10wt% Pb alloy in monotectic alloy immiscible regions during solidification process, with the same alloy size and cooling condition.
In addition, the geometric model established in numerical simulation, the meshing and the whole process of numerical simulation computation are simplified under the premise that the simulated result is guaranteed to be stable and reliable.
Modelling and simulation of the microstructure formation in a strip cast Al-Pb alloy ,J.
Simulation and analysis of liquid-liquid separation mechanism of hypermonotectic Al-Bi alloy, J.
Model for Solidification Microstructure Simulation of Ag-Cu Alloy,J.

Simulation and analysis of grinding fluid VOF model Geometric model and simulation parameters.
At the same time, in order to close the follow experiment, the simulation used a rectangular spray nozzle, spray width is 1mm.
Table.1 Simulation parameters Simulation Parameters Value Gringding Wheel Rotate speed vs (m/s) 20,40 Minimum clearance dmin (mm) 0.1 The length of the model l (mm) 130 Grinding fluid density ρ (kg/m3) 1000 Work speed vw(m/s) 0.1 Spray angle θ (°) 0-15 Spray horizontal distance a (mm) 40-65 Spray vertical distance h (mm) 5-20 Spray velocity v (m/s) 0.1-9 Results and analysis of VOF model simulation.
Ultimately the simulation results were acquired with considering the simulation parameters (pressure reference position, pressure difference solution, discrete equation scheme and relaxation factor etc.) and turbulence parameters etc.
In order to connect with the simulation, the experiment has chosen M7130 grinding machine, plane grinding, non-quenched and tempered 45 steel workpiece, and the other conditions such as nozzle shape and the coolant supply is close to the simulation.

Salcedo et al. [9] they carried out a numerical simulation of mixed convection heat transfer from a downward flow of Newtonian fluid around a tandem circular cylinder, the simulation are performed at Re = 200 and the governing equations are solved in unsteady laminar regime, Ri = -1 to 4, Pr = 0.7 and Blockage ratio = 0.2.
The numerical simulations are done for the range of these conditions as: Re = 5 to 40, Ri = 1, Pr = 1, β = 0.2 and distance between the cylinders S = 0 to 5d (d is the diameter of the cylinder).
(10) Numerical Details The numerical simulation is carried out by using the commercial CFD package ANSYS-CFX.
Results and Discussion Numerical simulations are represented mainly in term of streamlines and isotherm contours for the following range of conditions: Re = 5 to 40, distance between cylinders, S = 0 to 5d at fixed values of Richardson number Ri = 1, Prandtl number, Pr = 1 and blockage ratio, β = 0.2.
Summary The numerical simulations of incompressible fluid around a tandem of confined circular cylinders exposed to downward flow are studied under the effect of opposing thermal buoyancy in order to understand and to determine the combined effect of buoyancy strength and the distance between the cylinders on fluid flow and heat transfer rate in the range of these conditions: Re = 5 to 40, S = 0 to 5d at fixed values of Ri = 1, Pr = 1 and β = 1/5.

The simulations were undertaken to inform on how the fluid flowed within the tube passages in different tube bundle diameter that gives different gaps between the tubes, where the fluid must pass.
So, in the simulations, only a symmetrical half of a flow passage between the tubes is used.
The simulation was run until the residual of the pressure and velocities were less than 0.00001.
Less mesh cells reduce CPU time during CFD simulation which permits a significant number of cases to be run.

Numerical Study on Indoor Air Quality of Commercial Kitchen in China Xiaotan Hou1,2,a, Angui Li 1,b, Zhihua Wang 1,3,c, Yujiao Zhao 1,d 1Xi’an University of Architecture and Technology, Shaanxi, Xi’an,710055, China 2Hefei University of Technology, Tunxi Road No.193 Hefei, 230009, Anhui.China 3Xi'an Jiaotong University, 28 Xian Ning Road, Xi'an 710049, China awaterhxt@126.com, b liag@xauat.edu.cn, cZHWang3123@stu.xjtu.edu.cn, dasaall@sina.com Key words: CFD, Commercial kitchen, IAQ, Displacement ventilation Abstract: Indoor air quality of commercial kitchen is investigated and analyzed through velocity, temperature, humidity, and CO2 concentration under different air change rate and supply air temperature. the best air change rate is 30 times per hour and air supply temperature is 301.15K for kitchen, the mean value of the minimum velocity and standard deviation is 0.410m/s and 0.129 respectively, the maximum of the average concentration of CO2 is 659.78ppm, which is less than the
(a) 30 times (b) 35 times (c) 40 times Fig.4 Indoor CO2 concentration distribution under different air change rate and 26˚C at the plane Z=1.2 m Table 4 Simulation results of different air change rate Air change rate Supply Temperature speed average speed standard deviation Temperature averages Temperature and standard deviation CO2 average CO2 standard deviation drainage efficiency energy utilization coefficient 30 26 0.413 0.131 33.58 3.285 659.03 132.73 71.23% 1.258 27 0.411 0.130 33.98 3.368 659.78 133.51 71.24% 1.302 28 0.410 0.129 34.36 3.464 660.65 134.17 71.24% 1.352 35 26 0.461 0.166 33.82 3.430 645.72 131.84 71.84% 1.216 27 0.448 0.171 33.01 3.225 647.10 140.76 71.84% 1.095 28 0.446 0.169 33.48 3.294 659.33 135.13 71.85% 1.123 40 26 0.516 0.205 34.16 3.511 634.62 129.93 72.35% 1.180 27 0.514 0.203 34.65 3.571 635.73 130.36 72.36% 1.206 28 0.513 0.201 35.14 3.651 636.70 130.78 72.36% 1.240 From table 4, with the increase of air change
Numerical Simulation of IAQ under Up-suction Exhaust Hood in Commercial Kitchen[J].Advanced Materials Research Vols. 250-253 (2011) pp 3228-3231

The commercial CFD software ANSYS-FLUENT© was used to solve this numerical problem with the governing differential equations discretized by a control volume approach.
Simulations found that by increasing the nanoparticle volume fraction, heat transfer can be increased.
Hooman, Numerical simulation of natural convection and mixed convection of the nanofluid in a square cavity using Buongiorno model, Powder Technology, 268 (2014) 279-292

[5] Fujun Wang: Computational fluid dynamics analysis—Principle and application of CFD software (Tsinghua university Press, Beijing 2004) (In Chinese)
[6] Zhihua Wang: Numerical simulation and analysis of gas diffusion in confined space (Dalian university of technology press, Dalian 2009) (In Chinese).