Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Neuro-fuzzy modeling of deformation parameters for fusion-barriers

  • Akkoyun, Serkan (Department of Physics, Faculty of Sciences, Sivas Cumhuriyet University) ;
  • Torun, Yunis (Department of Electric-Electronics Engineering, Sivas Cumhuriyet University)
  • Received : 2020.06.24
  • Accepted : 2020.10.26
  • Published : 2021.05.25
Download PDF
⟨ Previous Next ⟩

Abstract

The fusion-barrier distribution is very sensitive to the structure of the colliding nuclei such as nuclear quadrupole and hexadecapole deformation parameters and their signs. If the nuclei that enter the fusion reaction are deformed, the barrier problem becomes complicated. Therefore the deformation parameters are taken into account in the calculations. In this study, Neuro-Fuzzy approach, ANFIS, method has been used for the estimation of ground-state quadrupole (𝜀2) and hexadecapole (𝜀4) deformation parameters for the nuclei. According to the results, the method is suitable for this task and one can confidently use it to obtain the data that is not available in the literature.

Keywords

References

  1. A.B. Balantekin, N. Takigawa, Quantum tunneling in nuclear fusion, Rev. Mod. Phys. 70 (1998) 77-100, https://doi.org/10.1103/revmodphys.70.77.
  2. M. Dasgupta, D.J. Hinde, N. Rowley, A.M. Stefanini, Measuring barriers to fusion, Annu. Rev. Nucl. Part Sci. 48 (1998) 401-461, https://doi.org/10.1146/annurev.nucl.48.1.401.
  3. R.C. Lemmon, J.R. Leigh, J.X. Wei, C.R. Morton, D.J. Hinde, J.O. Newton, J.C. Mein, M. Dasgupta, N. Rowley, Strong dependence of sub-barrier fusion on the nuclear hexadecapole deformation, Phys. Lett. B 316 (1993) 32-37, https://doi.org/10.1016/0370-2693(93)90653-Y.
  4. W. Nan, D. Liang, Z. En-Guang, W. Scheid, Nuclear hexadecapole deformation effects on the production of super-heavy elements, Chin. Phys. Lett. 27 (2010), 062502, https://doi.org/10.1088/0256-307X/27/6/062502.
  5. P. Moller, A.J. Sierk, T. Ichikawa, H. Sagawa, Nuclear ground-state masses and deformations: FRDM(2012), Atomic Data Nucl. Data Tables 109-110 (2016) 1-204, https://doi.org/10.1016/j.adt.2015.10.002.
  6. K. Hagino, S. Sakaguchi, Subbarrier fusion reactions of an aligned deformed nucleus, Phys. Rev. C 100 (2019), 064614, https://doi.org/10.1103/PhysRevC.100.064614.
  7. D. Jain, R. Kumar, M.K. Sharma, Effect of deformation and orientation on interaction barrier and fusion cross-sections using various proximity potentials, Nucl. Phys. 915 (2013) 106-124, https://doi.org/10.1016/j.nuclphysa.2013.07.002.
  8. G. Montagnoli, A.M. Stefanini, Recent experimental results in sub- and near-barrier heavy-ion fusion reactions, Eur. Phys. J. A. 53 (2017) 169, https://doi.org/10.1140/epja/i2017-12350-2.
  9. A. Iwamoto, P. Moller, Nuclear deformation and sub-barrier fusion cross sections, Nucl. Phys. 605 (1996) 334-358, https://doi.org/10.1016/0375-9474(96)00155-8.
  10. C.H. Dasso, S. Landowne, A. Winther, Channel-coupling effects in heavy-ion fusion reactions, Nucl. Physics, Sect. A. 405 (1983) 381-396, https://doi.org/10.1016/0375-9474(83)90578-X.
  11. S.V.S. Sastry, S. Santra, Structure information from fusion barriers, Pramana - J. Phys. 54 (2000) 813-826, https://doi.org/10.1007/s12043-000-0177-z.
  12. E. Wesolowski, The quadrupole deformation parameters of charge distributions in nuclei, Acta Phys. Pol. Ser. B. 15 (1984) 559-568.
  13. S.G. Nilsson, C.F. Tsang, A. Sobiczewski, Z. Szymanski, S. Wycech, C. Gustafson, I.L. Lamm, P. Moller, B. Nilsson, On the nuclear structure and stability of heavy and superheavy elements, Nucl. Physics, Sect. A. 131 (1969) 1-66, https://doi.org/10.1016/0375-9474(69)90809-4.
  14. P. Moller, A. Iwamoto, Macroscopic potential-energy surfaces for arbitrarily oriented, deformed heavy ions, Nucl. Phys. 575 (1994) 381-411, https://doi.org/10.1016/0375-9474(94)90197-X.
  15. T. Bayram, S. Akkoyun, S.O. Kara, A study on ground-state energies of nuclei by using neural networks, Ann. Nucl. Energy 63 (2014) 172-175, https://doi.org/10.1016/j.anucene.2013.07.039.
  16. S. Akkoyun, T. Bayram, T. Turker, Estimations of beta-decay energies through the nuclidic chart by using neural network, Radiat. Phys. Chem. 96 (2014) 186-189, https://doi.org/10.1016/j.radphyschem.2013.10.002.
  17. S. Akkoyun, T. Bayram, Estimations of fission barrier heights for Ra , Ac , Rf and Db nuclei by neural networks, Int. J. Mod. Phys. E. 23 (2014) 1450064, https://doi.org/10.1142/S0218301314500645.
  18. S. Akkoyun, Estimation of fusion reaction cross-sections by artificial neural networks, Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms 462 (2020) 51-54, https://doi.org/10.1016/j.nimb.2019.11.014.
  19. I. Eker, Y. Torun, Fuzzy logic control to be conventional method, Energy Convers. Manag. 47 (2006) 377-394, https://doi.org/10.1016/j.enconman.2005.05.008.
  20. V. Novak, I. Perfilieva, A. Dvorak, Insight into fuzzy modeling. https://doi.org/10.1002/9781119193210, 2016.
  21. M. Sugeno, K. Tanaka, Successive identification of a fuzzy model and its applications to prediction of a complex system, Fuzzy Set Syst. 42 (1991) 315-334, https://doi.org/10.1016/0165-0114(91)90110-C.
  22. A. Adineh-Vand, M. Torabi, G.H. Roshani, M. Taghipour, S.A.H. Feghhi, M. Rezaei, S.M. Sadati, Application of adaptive neuro-fuzzy inference system for prediction of neutron yield of IR-IECF facility in high voltages, J. Fusion Energy 33 (2014) 13-19, https://doi.org/10.1007/s10894-013-9631-z.
  23. J. Wu, H. Wu, Y. Song, T. Zhang, J. Zhang, Y. Cheng, Adaptive Neuro-fuzzy inference system based estimation of EAMA elevation joint error compensation, Fusion Eng. Des. 126 (2018) 170-173, https://doi.org/10.1016/j.fusengdes.2017.11.025.
  24. X.K. Wang, X.H. Yang, G. Liu, H. Qian, Adaptive neuro-fuzzy inference system pid controller for SG water level of nuclear power plant, Proc. 2009 Int. Conf. Mach. Learn. Cybern. (2009) 567-572, https://doi.org/10.1109/ICMLC.2009.5212517.
  25. S.A. Hosseini, I. Esmaili Paeen Afrakoti, Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS), J. Radiat. Res. 59 (2018) 436-441, https://doi.org/10.1093/jrr/rrx087.
  26. S.A. Ibrahem, A. Sahiner, A.A. Ibrahim, Fuzzy logic modeling for prediction of the nuclear tracks, J. Multidiscip. Model. Optim. 1 (2018) 33-40. https://dergipark.org.tr/tr/pub/jmmo/issue/38716/392082#article_cite.
  27. A.C.F. Guimaraes, D.C. Cabral, C.M.F. Lapa, Adaptive fuzzy system for degradation study in nuclear power plants' passive components, Prog. Nucl. Energy 48 (2006) 655-663, https://doi.org/10.1016/j.pnucene.2006.05.002.
  28. M. Dlomo, S. Chowdhury, S.P. Chowdhury, ANFIS based modelling of Boron concentration in a Pressurized Water Reactor in response to changes in power generation, in: IEEE PES Gen. Meet, IEEE, 2010, pp. 1-8, https://doi.org/10.1109/PES.2010.5588203.
  29. P. Moller, R. Bengtsson, B.G. Carlsson, P. Olivius, T. Ichikawa, H. Sagawa, A. Iwamoto, Axial and reflection asymmetry of the nuclear ground state, Atomic Data Nucl. Data Tables 94 (2008) 758-780, https://doi.org/10.1016/j.adt.2008.05.002.
  30. L.A. Zadeh, Fuzzy sets, Inf. Control 8 (1965) 338-353, https://doi.org/10.1016/S0019-9958(65)90241-X.
  31. Y. Torun, G. Tohumolu, Designing simulated annealing and subtractive clustering based fuzzy classifier, Appl. Soft Comput. J. 11 (2011) 2193-2201, https://doi.org/10.1016/j.asoc.2010.07.020.