In cooperation with the Iranian Nuclear Society

Document Type : Research Paper

Authors

1 Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute, P.O.Box: 14155-1339, Tehran - Iran

2 Faculty of Mechanical Engineering, Shiraz University, Postalcode: 7193616548, Shiraz – Iran

3 Faculty of Engineering, Shahed University, Postalcode: 3319118651, Tehran – Iran

Abstract

The multi-objective optimization methods are widely used in various areas of science and engineering. In this paper, a multi-objective optimization algorithm has been used to design the optimal placement of fuel assemblies in the KWU reactor with the purposes of power flattening and increasing the multiplication factor. Also, thermal-hydraulic analysis has been performed to investigate the new arrangement during a hypothetical loss of coolant accident. For this purpose, the PESA-II algorithm is coupled with Nodal expansion code (for neutronic calculations) and COBRA-EN code (for thermal-hydraulic calculations). The results showed that the neutronic parameters including power flattening and the multiplication factor (increases from 1.0047 to 1.0058) improved by optimizing the fuel assemblies’ placement. It was also investigated that during a hypothetical loss of coolant accident up to 15%, the reactor could continue to operate in the safe mode. In addition, the results have emphasized the better performance of the PESA-II algorithm than the SPEA algorithm.

Highlights

1. A. David, P.E. Moore, The regulation of inherent safety, IChemE Symposium Series, 149 (2003).

 

2. S. Ishiguro, E. Tomohir, Y. Akio, Loading pattern optimization for a PWR using Multi- Swarm Moth Flame Optimization Method with Predator, J. Nucl. Sci. Technol., 1-14 (2019).

 

3. M.A. Nasr, et al., Neutronic and thermal-hydraulic aspects of loading pattern optimization during the first cycle of VVER-1000 reactor using Polar Bear Optimization method, Ann. Nucl. Energy, 133, 538 (2019).

 

4. R. Akbari, et al., A novel multi-objective optimization method, imperialist competitive algorithm, for fuel loading pattern of nuclear reactors, Prog. Nucl. Energy, 108, 391 (2018).

 

5. R. Gharari, et al., Implementation of strength pareto evolutionary algorithm-II in the multi objective burnable poison placement optimization of KWU pressurized water reactor, Nucl. Eng. Technol. 48, 1126 (2016).

 

6. S.M. Mahmoudi, M. Aghaie, Evaluation of fuzzy based HS and GSA on reloading cycle length optimization of PWR nuclear power plant, Ann. Nucl. Energy, 134, 1-10 (2019).

 

7. A. Zameer, et al., Fractional-order particle swarm based multi-objective PWR core loading pattern optimization, Ann. Nucl. Energy, 135, 106982 (2019).

 

8. T.J. Rogers, PWR fuel assembly optimization using adaptive simulated annealing coupled with translat, Doctoral dissertation, Texas A & M University (2010).

 

9. F. Alim, K. Ivanov, S.H. Levine, New genetic algorithms (GA) to optimize PWR reactors: Part I: Loading pattern and burnable poison placement optimization techniques for PWRs, Ann. Nucl. Energy, 35, 93-112 (2008).

 

10. S. Yilmaz, K. Ivanov, S. Levine, Application of genetic algorithm to optimize burnable poison placement in pressurized water reactors. In Proceedings of the 7th annual conference on Genetic and evolutionary computation, 1477-1483 (2005).

 

11. F. Khoshahval, et al., A new hybrid method for multi-objective fuel management optimization using parallel PSO-SA, Prog. Nucl. Energy, 76, 112-121 (2014).

 

12. G. Chen, L. Junhua, A diversity ranking based evolutionary algorithm for multi objective and many objective optimization, Swarm Evolution. Comput. 48, 274-287 (2019).

 

13. L. Lv, et al., Multi objective firefly algorithm based on compensation factor and elite learning, Fut. Gen.Com. Sys. 91, 37-47 (2019).

 

14. Y.L.T. Silva, A.B. Herthel, A. Subramanian, A multi objective evolutionary algorithm for a class of mean-variance portfolio selection problems, Exp. Sys. App. 133, 225-241 (2019).

 

15. W.D. Corne, et al., PESA-II: Region-based selection in evolutionary multi objective optimization, Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (2001).

 

16. N. Poursalehi, A. Zolfaghari, A. Minuchehr, Performance comparison of zeroes order nodal expansion methods in 3D rectangular geometry, Nucl. Eng. Des. 252, 248-266 (2012).

 

17. Preliminary Safety Analysis Report (PSAR), Power plant Iran1, 2, prepared for AEOI by KWU (1976).

 

18. D. Basile, et al., COBRA-EN manual (1999).

Keywords

1. A. David, P.E. Moore, The regulation of inherent safety, IChemE Symposium Series, 149 (2003).
 
2. S. Ishiguro, E. Tomohir, Y. Akio, Loading pattern optimization for a PWR using Multi- Swarm Moth Flame Optimization Method with Predator, J. Nucl. Sci. Technol., 1-14 (2019).
 
3. M.A. Nasr, et al., Neutronic and thermal-hydraulic aspects of loading pattern optimization during the first cycle of VVER-1000 reactor using Polar Bear Optimization method, Ann. Nucl. Energy, 133, 538 (2019).
 
4. R. Akbari, et al., A novel multi-objective optimization method, imperialist competitive algorithm, for fuel loading pattern of nuclear reactors, Prog. Nucl. Energy, 108, 391 (2018).
 
5. R. Gharari, et al., Implementation of strength pareto evolutionary algorithm-II in the multi objective burnable poison placement optimization of KWU pressurized water reactor, Nucl. Eng. Technol. 48, 1126 (2016).
 
6. S.M. Mahmoudi, M. Aghaie, Evaluation of fuzzy based HS and GSA on reloading cycle length optimization of PWR nuclear power plant, Ann. Nucl. Energy, 134, 1-10 (2019).
 
7. A. Zameer, et al., Fractional-order particle swarm based multi-objective PWR core loading pattern optimization, Ann. Nucl. Energy, 135, 106982 (2019).
 
8. T.J. Rogers, PWR fuel assembly optimization using adaptive simulated annealing coupled with translat, Doctoral dissertation, Texas A & M University (2010).
 
9. F. Alim, K. Ivanov, S.H. Levine, New genetic algorithms (GA) to optimize PWR reactors: Part I: Loading pattern and burnable poison placement optimization techniques for PWRs, Ann. Nucl. Energy, 35, 93-112 (2008).
 
10. S. Yilmaz, K. Ivanov, S. Levine, Application of genetic algorithm to optimize burnable poison placement in pressurized water reactors. In Proceedings of the 7th annual conference on Genetic and evolutionary computation, 1477-1483 (2005).
 
11. F. Khoshahval, et al., A new hybrid method for multi-objective fuel management optimization using parallel PSO-SA, Prog. Nucl. Energy, 76, 112-121 (2014).
 
12. G. Chen, L. Junhua, A diversity ranking based evolutionary algorithm for multi objective and many objective optimization, Swarm Evolution. Comput. 48, 274-287 (2019).
 
13. L. Lv, et al., Multi objective firefly algorithm based on compensation factor and elite learning, Fut. Gen.Com. Sys. 91, 37-47 (2019).
 
14. Y.L.T. Silva, A.B. Herthel, A. Subramanian, A multi objective evolutionary algorithm for a class of mean-variance portfolio selection problems, Exp. Sys. App. 133, 225-241 (2019).
 
15. W.D. Corne, et al., PESA-II: Region-based selection in evolutionary multi objective optimization, Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (2001).
 
16. N. Poursalehi, A. Zolfaghari, A. Minuchehr, Performance comparison of zeroes order nodal expansion methods in 3D rectangular geometry, Nucl. Eng. Des. 252, 248-266 (2012).
 
17. Preliminary Safety Analysis Report (PSAR), Power plant Iran1, 2, prepared for AEOI by KWU (1976).
 
18. D. Basile, et al., COBRA-EN manual (1999).