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A Hybrid Power Series Artificial Bee Colony Algorithm to Obtain a Solution for Buckling of Multiwall Carbon Nanotube Cantilevers Near Small Layers of Graphite Sheets

DOI: 10.1155/2012/683483

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Abstract:

A hybrid power series and artificial bee colony algorithm (PS-ABC) method is applied to solve a system of nonlinear differential equations arising from the distributed parameter model of multiwalled carbon nanotube (MWCNT) cantilevers in the vicinity of thin and thick graphite sheets subject to intermolecular forces. The intermolecular forces are modeled using van der Waals forces. A trial solution of the differential equation is defined as sum of two polynomial parts. The first part satisfies the boundary conditions and does contain two adjustable parameters. The second part is constructed as not to affect the boundary conditions, which involves adjustable parameters. The ABC method is applied to find adjustable parameters of trial solution (in first and second part). The obtained results are compared with numerical results as well as analytical solutions those reported in the literature. The results of the presented method represent a remarkable accuracy in comparison with numerical results. The minimum initial gap and the detachment length of the actuator that does not stick to the substrate due to the intermolecular forces, as important parameters in pull-in instability of MWCNT actuator, are evaluated by obtained power series. 1. Introduction Multiwalled carbon nanotubes (MWCNTs) have attracted considerable attention among other nanomaterials because of the potential advantages on markedly improved stiffness, strength, and elimination of main failure mechanism [1]. These novel materials can usually be visualized as nanoscale concentric cylinders rolled up by graphene sheets. MWCNTs are produced by different techniques such as chemical vapor deposition, laser ablation, and arc discharge [1, 2]. The nanotubes can provide various ranges of conductive properties depending on their atomic and geometrical structure [3]. The unusual properties of MWCNTs have motivated worldwide engineers to explore their applications in different fields of science [4]. Experimental investigations show that the conductance of CNTs is strongly influenced by the occurrence of buckling [5]. The repeatable transformation between the buckled state and normal state of CNTs produces good potential applications to create devices such as nanotransistors [5], nano-valve, and so forth, [6]. With recent growth in nanotechnology, MWCNTs are increasingly used in developing atomic force microscope (AFM) probes [1, 3, 7, 8] and nanoelectromechanical system (NEMS) switches [9–11]. In the nanoscale, the surface forces play an important role in the design and operation of the MEMS and NEMS

References

[1]  A. M. K. Esawi and M. M. Farag, “Carbon nanotube reinforced composites: potential and current challenges,” Materials and Design, vol. 28, no. 9, pp. 2394–2401, 2007.
[2]  S. J. Chowdhury and B. Howard, “Thermo-mechanical properties of graphite-epoxy composite,” International Review of Mechanical Engineering, vol. 4, no. 6, pp. 785–790, 2010.
[3]  C. Li, E. T. Thostenson, and T. W. Chou, “Sensors and actuators based on carbon nanotubes and their composites: a review,” Composites Science and Technology, vol. 68, no. 6, pp. 1227–1249, 2008.
[4]  M. S. Dresselhaus, G. Dresselhaus, and A. Jorio, “Unusual properties and structure of carbon nanotubes,” Annual Review of Materials Research, vol. 34, pp. 247–278, 2004.
[5]  H. W. C. Postma, T. Teepen, Z. Yao, M. Grifoni, and C. Dekker, “Carbon nanotube single-electron transistors at room temperature,” Science, vol. 293, no. 5527, pp. 76–79, 2001.
[6]  M. Grujicic, G. Cao, and W. N. Roy, “Computational analysis of the lattice contribution to thermal conductivity of single-walled carbon nanotubes,” Journal of Materials Science, vol. 40, no. 8, pp. 1943–1952, 2005.
[7]  S. Akita, “Nanotweezers consisting of carbon nanotubes operating in an atomic force microscope,” Applied Physics Letters, vol. 79, pp. 1591–1593, 2001.
[8]  Y. Cao, Y. Liang, S. Dong, and Y. Wang, “A multi-wall carbon nanotube (MWCNT) relocation technique for atomic force microscopy (AFM) samples,” Ultramicroscopy, vol. 103, no. 2, pp. 103–108, 2005.
[9]  M. Paradise and T. Goswami, “Carbon nanotubes—production and industrial applications,” Materials and Design, vol. 28, no. 5, pp. 1477–1489, 2007.
[10]  R. H. Baughman, C. Cui, A. A. Zakhidov et al., “Carbon nanotube actuators,” Science, vol. 284, no. 5418, pp. 1340–1344, 1999.
[11]  C. H. Ke, N. Pugno, B. Peng, and H. D. Espinosa, “Experiments and modeling of carbon nanotube-based NEMS devices,” Journal of the Mechanics and Physics of Solids, vol. 53, no. 6, pp. 1314–1333, 2005.
[12]  G. L. Klimchitskaya, E. V. Blagov, and V. M. Mostepanenko, “Van der Waals and Casimir interactions between atoms and carbon nanotubes,” Journal of Physics A, vol. 41, no. 16, Article ID 164012, 2008.
[13]  A. Koochi, A. S. Kazemi, A. Noghrehabadi, A. Yekrangi, and M. Abadyan, “New approach to model the buckling and stable length of multi walled carbon nanotube probes near graphite sheets,” Materials and Design, vol. 32, no. 5, pp. 2949–2955, 2011.
[14]  J. A. Khan, R. M. A. Zahoor, and I. M. Qureshi, “Swarm Intelligence for the problems of non-linear ordinary differential equations and its application to well known Wessinger's equation,” European Journal of Scientific Research, vol. 34, no. 4, pp. 514–525, 2009.
[15]  I. E. Lagaris, A. Likas, and D. I. Fotiadis, “Artificial neural networks for solving ordinary and partial differential equations,” IEEE Transactions on Neural Networks, vol. 9, no. 5, pp. 987–1000, 1998.
[16]  M. Ghalambaz, A. R. Noghrehabadi, M. A. Behrang, E. Assareh, A. Ghanbarzadeh, and N. Hedayat, “A hybrid neural network and gravitational search algorithm (HNNGSA) method to solve well known Wessinger's equation,” Proceedings of World Academy of Science, Engineering and Technology, vol. 73, pp. 803–807, 2011.
[17]  A. Noghrehabadi, M. Ghalambaz, and M. Ghalambaz, “A hybrid power series—artificial bee colony to solve magnetohydrodynamic viscous flow over a nonlinear permeable shrinking sheet,” International Review on Modelling and Simulations, vol. 4, no. 5, pp. 2696–2700, 2011.
[18]  M. A. Behrang, M. Ghalambaz, E. Assareh, and A. R. Noghrehabadi, “A new solution for natural convection of darcian fluid about a vertical full cone embedded in porous media prescribed wall temperature by using a hybrid neural network-particle swarm optimization method,” World Academy of Science, Engineering and Technology, vol. 73, pp. 1108–1113, 2011.
[19]  E. Assareh, M. A. Behrang, M. Ghalambaz, A. R. Noghrehabadi, and A. Ghanbarzadeh, "A New Approach to Solve Blasius Equation using Parameter Identification of Nonlinear Functions based on the Bees Algorithm (BA), vol. 73, World Academy of Science, Engineering and Technology, 2001.
[20]  M. Ghalambaz, A. Noghrehabadi, and A. Vosoogh, “A hybrid Power series—Artificial Bee Colony algorithm to solve electrostatic pull-in instability and deflection of nano cantilever actuators considering Casimir Attractions,” International Review of Mechanical Engineering, vol. 5, no. 4, 2011.
[21]  A. Yekrangi, M. Ghalambaz, A. Noghrehabadi et al., “Approximate solution for a simple pendulum beyond the small angles regimes using hybrid artificial neural network and particle swarm optimization algorithm,” Procedia Engineering, vol. 10, pp. 3742–3748, 2011.
[22]  H. Lee and I. S. Kang, “Neural algorithm for solving differential equations,” Journal of Computational Physics, vol. 91, no. 1, pp. 110–131, 1990.
[23]  A. J. Meade Jr. and A. A. Fernandez, “The numerical solution of linear ordinary differential equations by feedforward neural networks,” Mathematical and Computer Modelling, vol. 19, no. 12, pp. 1–25, 1994.
[24]  A. Malek and R. S. Beidokhti, “Numerical solution for high order differential equations using a hybrid neural network—optimization method,” Applied Mathematics and Computation, vol. 183, no. 1, pp. 260–271, 2006.
[25]  D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
[26]  D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, 2007.
[27]  D. Karaboga and B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Applied Mathematics and Computation, vol. 214, no. 1, pp. 108–132, 2009.
[28]  W. H. Lin and Y. P. Zhao, “Casimir effect on the pull-in parameters of nanometer switches,” Microsystem Technologies, vol. 11, no. 2-3, pp. 80–85, 2005.
[29]  L. A. Girifalco, M. Hodak, and R. S. Lee, “Carbon nanotubes, buckyballs, ropes, and a universal graphitic potential,” Physical Review B, vol. 62, no. 19, pp. 13104–13110, 2000.
[30]  M. Dequesnes, S. V. Rotkin, and N. R. Aluru, “Calculation of pull-in voltages for carbon-nanotube-based nanoelectromechanical switches,” Nanotechnology, vol. 13, no. 1, pp. 120–131, 2002.
[31]  B. Akay and D. Karaboga, “A modified Artificial Bee Colony algorithm for real-parameter optimization,” Information Sciences, vol. 192, pp. 120–142, 2012.
[32]  U. Ascher, R. Mattheij, and R. Russell, Numerical Solution of Boundary Value Problems for Ordinary Differential Equations, vol. 13 of SIAM Classics in Applied Mathematics, 1995.
[33]  U. Ascher and L. Petzold, Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations, SIAM, Philadelphia, Pa, USA, 1998.

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