References
- C. Jin, Y. C. Mao, N. Q. Zhang, and K. N. Sun, “Fabrication and characterization of Ni-SSZ gradient anodes/SSZ electrolyte for anode-supported SOFCs by tape casting and co-sintering technique,” Int. J. Hydrogen Energy, vol. 40, no. 26, pp. 8433–8441, 2015. DOI: 10.1016/j.ijhydene.2015.04.088.
- C. M. An, J. H. Song, I. Kang, and N. Sammes, “The effect of porosity gradient in a Nickel/Yttria Stabilized Zirconia anode for an anode-supported planar solid oxide fuel cell,” J. Power Sources, vol. 195, no. 3, pp. 821–824, 2010. DOI: 10.1016/j.jpowsour.2009.08.043.
- M. Sukeshini, F. Meisenkothen, P. Gardner, and T. L. Reitz, “Aerosol Jet® Printing of functionally graded SOFC anode interlayer and microstructural investigation by low voltage scanning electron microscopy,” J. Power Sources, vol. 224, pp. 295–303, 2013. DOI: 10.1016/j.jpowsour.2012.09.094.
- J. F. Beltran-Lopez, M. A. Laguna-Bercero, J. Gurauskis, and J. I. Peña, “Fabrication and characterization of graded anodes for anode-supported solid oxide fuel cells by tape casting and lamination,” Electrocatalysis, vol. 5, no. 3, pp. 273–278, 2014. DOI: 10.1007/s12678-014-0193-2.
- X. M. Hao et al., “Co-tape casting fabrication, field assistant sintering and evaluation of a coke resistant La0.2Sr0.7TiO3-Ni/YSZ functional gradient anode supported solid oxide fuel cell,” Int. J. Hydrogen Energy, vol. 40, no. 37, pp. 12790–12797, 2015. DOI: 10.1016/j.ijhydene.2015.07.126.
- S. Lee, I. Park, H. Lee, and D. Shin, “Continuously gradient anode functional layer for BCZY based proton-conducting fuel cells,” Int. J. Hydrogen Energy, vol. 39, no. 26, pp. 14342–14348, 2014. DOI: 10.1016/j.ijhydene.2014.03.135.
- R. J. Gorte and J. M. Vohs, “Nanostructured anodes for solid oxide fuel cells,” Curr. Opin. Colloid Interface Sci., vol. 14, no. 4, pp. 236–244, 2009. DOI: 10.1016/j.cocis.2009.04.006.
- J. McCoppin et al., “Compositional control of continuously graded anode functional layer,” J. Power Sources, vol. 215, pp. 160–163, 2012. DOI: 10.1016/j.jpowsour.2012.05.024.
- C. Wang, “Microscale correlations adoption in solid oxide fuel cell,” J. Fuel Cell Sci. Technol., vol. 12, no. 4, pp. 041006, 2015. DOI: 10.1115/1.4031153.
- C. Wang, “A computational analysis of functionally graded anode in solid oxide fuel cell by involving the correlations of microstructural parameters,” Energies, vol. 9, no. 6, pp. 408, 2016. DOI: 10.3390/en9060408.
- M. Ni, M. K. H. Leung, and D. Y. C. Leung, “Micro-scale modelling of solid oxide fuel cells with micro-structurally graded electrodes,” J. Power Sources, vol. 168, no. 2, pp. 369–378, 2007. DOI: 10.1016/j.jpowsour.2007.03.005.
- J. X. Shi and X. J. Xue, “CFD analysis of a symmetrical planar SOFC with heterogeneous electrode properties,” Electrochim. Acta, vol. 55, no. 18, pp. 5263–5273, 2010. DOI: 10.1016/j.electacta.2010.04.060.
- S. J. Lee, C. H. Jung, K. B. Shim, and S. C. Yi, “Microstructural analysis of the functionally graded electrodes in solid oxide fuel cells,” J. Ceram. Process. Res., vol. 13, pp. 810–815, 2012.
- F. Ramadhani, M. A. Hussain, H. Mokhlis, and S. Hajimolana, “Optimization strategies for solid oxide fuel cell (SOFC) application: a literature survey,” Renew. Sust. Energ. Rev., vol. 76, pp. 460–484, 2017. DOI: 10.1016/j.rser.2017.03.052.
- J. Milewski and K. Świrski, “Modelling the SOFC behaviours by artificial neural network,” Int. J. Hydrogen Energy, vol. 34, no. 13, pp. 5546–5553, 2009. DOI: 10.1016/j.ijhydene.2009.04.068.
- O. Razbani and M. Assadi, “Artificial neural network model of a short stack solid oxide fuel cell based on experimental data,” J. Power Sources, vol. 246, pp. 581–586, 2014. DOI: 10.1016/j.jpowsour.2013.08.018.
- X. J. Wu, X. J. Zhu, G. Y. Cao, and H. Y. Tu, “Predictive control of SOFC based on a GA-RBF neural network model,” J. Power Sources, vol. 179, no. 1, pp. 232–239, 2008. DOI: 10.1016/j.jpowsour.2007.12.036.
- S. Bozorgmehri and M. Hamedi, “Modeling and optimization of anode-supported solid oxide fuel cells on cell parameters via artificial neural network and genetic algorithm,” Fuel Cells, vol. 12, no. 1, pp. 11–23, 2012. DOI: 10.1002/fuce.201100140.
- F. Zhao and A. V. Virkar, “Dependence of polarization in anode-supported solid oxide fuel cells on various cell parameters,” J. Power Sources, vol. 141, no. 1, pp. 79–95, 2005. DOI: 10.1016/j.jpowsour.2004.08.057.
- M. Brown, S. Primdahl, and M. Mogensen, “Structure/performance relations for Ni/Yttria-stabilized zirconia anodes for solid oxide fuel cells,” J. Electrochem. Soc., vol. 147, no. 2, pp. 475–485, 2000. DOI: 10.1149/1.1393220.
- S. P. Jiang, P. J. Callus, and S. P. S. Badwal, “Fabrication and performance of Ni/3 mol% Y2O3-ZrO2 cermet anodes for solid oxide fuel cells,” Solid State Ionics, vol. 132, no. 1–2, pp. 1–14, 2000. DOI: 10.1016/S0167-2738(00)00729-3.
- M. D. McKay, R. J. Beckman, and W. J. Conover, “Comparison of three methods for selecting values of input variables in the analysis of output from a computer code,” Technometrics, vol. 21, no. 2, pp. 239–245, 1979. DOI: 10.2307/1268522.
- R. Beigzadeh, M. Rahimi, O. Jafari, and A. A. Alsairafi, “Computational fluid dynamics assists the artificial neural network and genetic algorithm approaches for thermal and flow modeling of air-forced convection on interrupted plate fins,” Numer. Heat Transfer A, vol. 70, no. 5, pp. 546–565, 2016. DOI: 10.1080/10407782.2016.1177329.
- S. Bélanger and L. Gosselin, “Utilization of artificial neural networks in the context of materials selection for thermofluid design,” Numer. Heat Transfer A, vol. 55, no. 9, pp. 825–844, 2009. DOI: 10.1080/10407780902925515.
- S. Haykin, Neural Networks and Learning Machines, vol. 3, 3rd ed. Upper Saddle River: Pearson Education, 2009.
- A. T. C. Goh, “Back-propagation neural networks for modeling complex systems,” Artif. Intell. Eng., vol. 9, no. 3, pp. 143–151, 1995. DOI: 10.1016/0954-1810(94)00011-S.
- W. M. Lin, C. D. Yang, J. H. Lin, and M. T. Tsay, “A fault classification method by RBF neural network with OLS learning procedure,” IEEE Trans. Power Deliver., vol. 16, no. 4, pp. 473–477, 2001.
- R. Beigzadeh, M. Rahimi, M. Parvizi, and S. Eiamsa-Ard, “Application of ANN and GA for the prediction and optimization of thermal and flow characteristics in a rectangular channel fitted with twisted tape vortex generators,” Numer. Heat Transfer A, vol. 65, no. 2, pp. 186–199, 2014. DOI: 10.1080/10407782.2013.826010.
- S. R. Shabanian, S. Lashgari, and T. Hatami, “Application of intelligent methods for the prediction and optimization of thermal characteristics in a tube equipped with perforated twisted tape,” Numer. Heat Transfer A, vol. 70, no. 1, pp. 30–47, 2016. DOI: 10.1080/10407782.2016.1139982.
- L. Yang, Z. Min, P. N. Sarwesh, and M. K. Chyu, “Numerical optimizations of hybrid-linked jet impingement heat transfer based on the genetic algorithm,” Numer. Heat Transfer A, vol. 70, no. 11, pp. 1179–1194, 2016. DOI: 10.1080/10407782.2016.1243946.
- F. Shi, X. Wang, and L. Yu, 30 Cases of Applications of Neural Network Using MATLAB. Beijing: Beihang University Press, 2010.