In cooperation with the Iranian Nuclear Society

Document Type : Research Paper

Authors

1 Nuclear Fuel Cycle Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box:11365-8486, Tehran-Iran

2 Department of Energy Engineering, Sharif University of Technology, P.O.Box: 14565-1114, Tehran-Iran

3 Advanced Technology Company of Iran, AEOI, P.O.Box: 14399-55431, Tehran-Iran

Abstract

To increase the performance of a gas centrifuge, it is necessary to stabilize an axial flow inside the rotor. For this purpose, computer simulation is an effective method. In the present work, a specific solver (ICDB) was developed in OpenFOAM to simulate the gas flow in the rotor. Creating a solver is important because OpenFOAM is free and open-source software. According to a study carried out by a research group, high-speed airflow is considered on a flat plate to assess the accuracy of this solver. To validate the ICDB, the results are compared with those of Fluent software and other researchers. It is found that the results corresponding to ICDB solver are in good agreement with other effects. Moreover, using the ICDB solver and the fluent software, the uranium hexafluoride gas flow inside the axisymmetric rotor was simulated considering different drives such as the thermal drive (wall and caps) and the mechanical drive (scoop). The obtained results show that the developed solver in OpenFOAM can simulate the uranium hexafluoride gas flow inside the rotor. 

Highlights

1.     S. Bogovalov, V. kislov and I. Tronin, Impact of the pulsed braking force on the axial circulation in a gas centrifuge, Applied mathematics and computation. 272, 670 (2016).

 

2.   J. Hu, C. Ying and S. Zeng, Overall Separation Factor in a Gas Centrifuge Using a Purely Axial Flow Model, Separation Science and Technology. 40, 2139 (2005).

 

3.     D. R. Olander, The Theory of Uranium Enrichment by the Gas Centrifuge, Progress in Nuclear Energy. 8, 1 (1981).

 

4.  J. Brouwers, On Compressible Flow in a Gas Centrifuge and its Effect on the Maximum Separative Power, Nuclear Technology. 39, (1978).

 

5.   P. Migliorini, PhD. thesis, University of Virginia, 2013.

 

6.     T. Nakayama and W. Takuji, Numerical Analysis of Separative Power of Isotope Centrifuges,  Journal of Nuclear Science and Technology. 11, 495 (1974).

 

7.   T. Kai, Basic Characteristics of Centrifuges, (III) Analysis of Fluid Flow in Centrifuges, Journal of Nuclear Science and Technology. 14, 267 (1976).

 

8.     T. Takuji, Numerical Analysis of Separative Power of Isotope Centrifuges, (II), Journal of Nuclear Science and Technology. 14, 901 (1977).

 

9.  L. Cloutman, Numerical simulation of the countercurrent in a gas centrifuge, Los Alamos National Laborator. 8972 (1983).

 

10.   D. Jiang and S. Zeng, in: International Conference on Nuclear Engineering ,(Miami, Florida, USA, 2006).

 

11.   P. Omnes, Numerical and physical comparisons of two models of a gas centrifuge, Computers & Fluids. 36, 1028 (2007).

 

12.   S. Bogovalov, and et al., Verification of numerical codes for modeling of the flow and isotope separation in gas centrifuges, Computers & Fluids. 86, 177 (2013).

 

13.  S. Chen, S. Feng Xiao and X. Xinlin, Analysis on capabilities of density-based solvers within OpenFOAM to distinguish aerothermal variables in diffusion boundary layer, Chinese Journal of Aeronautics. 26,  1370 (2013).

 

14.   J. Karimi Sabet, and et al., Simulation to determine the concentration profile for a three-component gas of a gas centrifuge machine under total reflux flow conditions by using the DSMC method, Journal of Nuclear Science and Technology, (2019) (In Persian).

 

15.  S. Yousefi nasab, and et al., Investigation Gas Behavior Inside a Gas Centrifuge Using DSMC Code Developed And dsmcFOAM Solver, Journal of Nuclear Science and Technology, (2019) (In Persian).

 

16.  V. Ghazanfari, and et al., OpenFoam application for numerical simulation of thermal drive effect on gas flow in a gas centrifuge for total reflux, Journal of Nuclear Science and Technology, (2019) (In Persian).

 

17.  S. Chun, S. Fengxian and X. Xinlin, Analysis on capabilities of density-based solvers within OpenFOAM to distinguish aerothermal variables in diffusion boundary layer, Chinese Journal of Aeronautics. 26, 1370 (2013).

 

18.   J. Blazek, Computational Fluid Dynamic Principle And Application, (Elsevier, 2001).

 

19.  S. Ye, W. Yang and X. Xu, in: Computing Machinery, (Wuhan, China, 2017) .

 

20.   S. Chun, and et al., Implementation of density based implicit LU-SGS solver in the framework of OpenFOAM, Advances in Engineering Software. 91,  80 (2016).

 

21. K. Kitamura and A. Hashimoto, Reduced dissipation AUSM-family fluxes: HR-SLAU2 and HR-AUSM+-up for high resolution unsteady flow simulations, Computers & Fluids. 126, 41 (2016).

 

22.   M. Liou, A sequel to AUSM, PartII:AUSM+-up for all speeds, Journal of Computational Physics. 214, 137 (2006).

 

23.   E. Blosch, and et al., Development and validation of transonic flutter prediction methodology using CFD-FASTRAN, AIAA. 2006 (2007).

Keywords

1.     S. Bogovalov, V. kislov and I. Tronin, Impact of the pulsed braking force on the axial circulation in a gas centrifuge, Applied mathematics and computation. 272, 670 (2016).
 
2.   J. Hu, C. Ying and S. Zeng, Overall Separation Factor in a Gas Centrifuge Using a Purely Axial Flow Model, Separation Science and Technology. 40, 2139 (2005).
 
3.     D. R. Olander, The Theory of Uranium Enrichment by the Gas Centrifuge, Progress in Nuclear Energy. 8, 1 (1981).
 
4.  J. Brouwers, On Compressible Flow in a Gas Centrifuge and its Effect on the Maximum Separative Power, Nuclear Technology. 39, (1978).
 
5.   P. Migliorini, PhD. thesis, University of Virginia, 2013.
 
6.     T. Nakayama and W. Takuji, Numerical Analysis of Separative Power of Isotope Centrifuges,  Journal of Nuclear Science and Technology. 11, 495 (1974).
 
7.   T. Kai, Basic Characteristics of Centrifuges, (III) Analysis of Fluid Flow in Centrifuges, Journal of Nuclear Science and Technology. 14, 267 (1976).
 
8.     T. Takuji, Numerical Analysis of Separative Power of Isotope Centrifuges, (II), Journal of Nuclear Science and Technology. 14, 901 (1977).
 
9.  L. Cloutman, Numerical simulation of the countercurrent in a gas centrifuge, Los Alamos National Laborator. 8972 (1983).
 
10.   D. Jiang and S. Zeng, in: International Conference on Nuclear Engineering ,(Miami, Florida, USA, 2006).
 
11.   P. Omnes, Numerical and physical comparisons of two models of a gas centrifuge, Computers & Fluids. 36, 1028 (2007).
 
12.   S. Bogovalov, and et al., Verification of numerical codes for modeling of the flow and isotope separation in gas centrifuges, Computers & Fluids. 86, 177 (2013).
 
13.  S. Chen, S. Feng Xiao and X. Xinlin, Analysis on capabilities of density-based solvers within OpenFOAM to distinguish aerothermal variables in diffusion boundary layer, Chinese Journal of Aeronautics. 26,  1370 (2013).
 
14.   J. Karimi Sabet, and et al., Simulation to determine the concentration profile for a three-component gas of a gas centrifuge machine under total reflux flow conditions by using the DSMC method, Journal of Nuclear Science and Technology, (2019) (In Persian).
 
15.  S. Yousefi nasab, and et al., Investigation Gas Behavior Inside a Gas Centrifuge Using DSMC Code Developed And dsmcFOAM Solver, Journal of Nuclear Science and Technology, (2019) (In Persian).
 
16.  V. Ghazanfari, and et al., OpenFoam application for numerical simulation of thermal drive effect on gas flow in a gas centrifuge for total reflux, Journal of Nuclear Science and Technology, (2019) (In Persian).
 
17.  S. Chun, S. Fengxian and X. Xinlin, Analysis on capabilities of density-based solvers within OpenFOAM to distinguish aerothermal variables in diffusion boundary layer, Chinese Journal of Aeronautics. 26, 1370 (2013).
 
18.   J. Blazek, Computational Fluid Dynamic Principle And Application, (Elsevier, 2001).
 
19.  S. Ye, W. Yang and X. Xu, in: Computing Machinery, (Wuhan, China, 2017) .
 
20.   S. Chun, and et al., Implementation of density based implicit LU-SGS solver in the framework of OpenFOAM, Advances in Engineering Software. 91,  80 (2016).
 
21. K. Kitamura and A. Hashimoto, Reduced dissipation AUSM-family fluxes: HR-SLAU2 and HR-AUSM+-up for high resolution unsteady flow simulations, Computers & Fluids. 126, 41 (2016).
 
22.   M. Liou, A sequel to AUSM, PartII:AUSM+-up for all speeds, Journal of Computational Physics. 214, 137 (2006).
 
23.   E. Blosch, and et al., Development and validation of transonic flutter prediction methodology using CFD-FASTRAN, AIAA. 2006 (2007).