• Steel and Composite Structures
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• v.34 no.2
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• pp.261-277
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• 2020

The main objective of this research paper is to consider vibration analysis of vacancy defected graphene sheet as a nonisotropic structure via molecular dynamic and continuum approaches. The influence of structural defects on the vibration of graphene sheets is considered by applying the mechanical properties of defected graphene sheets. Molecular dynamic simulations have been performed to estimate the mechanical properties of graphene as a nonisotropic structure with single- and double- vacancy defects using open source well-known software i.e., large-scale atomic/molecular massively parallel simulator (LAMMPS). The interactions between the carbon atoms are modelled using Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potential. An isogeometric analysis (IGA) based upon non-uniform rational B-spline (NURBS) is employed for approximation of single-layered graphene sheets deflection field and the governing equations are derived using nonlocal elasticity theory. The dependence of small-scale effects, chirality and different defect types on vibrational characteristic of graphene sheets is investigated in this comprehensive research work. In addition, numerical results are validated and compared with those achieved using other analysis, where an excellent agreement is found. The interesting results indicate that increasing the number of missing atoms can lead to decrease the natural frequencies of graphene sheets. It is seen that the degree of the detrimental effects differ with defect type. The Young's and shear modulus of the graphene with SV defects are much smaller than graphene with DV defects. It is also observed that Single Vacancy (SV) clusters cause more reduction in the natural frequencies of SLGS than Double Vacancy (DV) clusters. The effectiveness and the accuracy of the present IGA approach have been demonstrated and it is shown that the IGA is efficient, robust and accurate in terms of nanoplate problems.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.74.2-74.2
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• 2014

Magnetohydrodynamics(MHD) dynamo depends on many factors such as viscosity ${\gamma}$, magnetic diffusivity ${\eta}$, magnetic Reynolds number $Re_M$, external driving source, or magnetic Prandtl number $Pr_M$. $Pr_M$, the ratio of ${\gamma}$ to ${\eta}$ (for example, galaxy ${\sim}10^{14}$), plays an important role in small scale dynamo. With the high PrM, conductivity effect becomes very important in small scale regime between the viscous scale ($k_{\gamma}{\sim}Re^{3/4}k_fk_f$:forcing scale) and resistivity scale ($k_{\eta}{\sim}PrM^{1/2}k_{\gamma}$). Since ${\eta}$ is very small, the balance of local energy transport due to the advection term and nonlocal energy transfer decides the magnetic energy spectra. Beyond the viscous scale, the stretched magnetic field (magnetic tension in Lorentz force) transfers the magnetic energy, which is originally from the kinetic energy, back to the kinetic eddies leading to the extension of the viscous scale. This repeated process eventually decides the energy spectrum of the coupled momentum and magnetic induction equation. However, the evolving profile does not follow Kolmogorov's -3/5 law. The spectra of EV (${\sim}k^{-4}$) and EM (${\sim}k^0$ or $k^{-1}$) in high $Pr_M$ have been reported, but our recent simulation results show a little different scaling law ($E_V{\sim}k^{-3}-k^{-4}$, $EM{\sim}k^{-1/2}-k^{-1}$). We show the results and explain the reason.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.102-110
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• 2015

The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.