International Journal of Modern Physics C

In this work, a dynamically adaptive curvelet technique has been developed for solving nonlinear Schrödinger equation (NLS). Central finite difference method is used for approximating the one- and two-dimensional differential operators and radial-basis functions (RBFs) are utilized for approximating the differential operators on the sphere. The grid on which the equation is solved, is obtained using curvelets. For test problems 1 and $2$ (1d & 2d problems) considered in this paper, the computational time carried out by the proposed technique is analyzed with the computational time carried out by the finite difference technique. Moreover, the problem on the sphere has been considered, for which the computational time carried out by the RBF collocation technique is analyzed with the computational time carried out by the proposed technique. It is found that the developed technique performs better in terms of computational time, for example, on sphere, computational effort reduces by four times using the proposed method.

In this paper, a meshless spectral radial point interpolation (MSRPI) method using weighted $\theta$-scheme is formulated for the numerical solutions of a class of nonlinear Kawahara-type evolutionary equations. The formulated method is applied for simulation of single and double solitary waves motion, wave generation and oscillatory shock waves propagation. Quality of approximation is measured via discrete $L_{\infty }$, $L_2$ and $L_{\mathrm{\text{rms}}}$ error norms. Three invariant quantities corresponding to mass, momentum and energy are also computed for the method validation. Stability analysis of the proposed method is briefly discussed and verified computationally. Comparison of the obtained results are made with other existing results in the literature revealing the method superiority.

This paper explores the interaction of different flow paths in a porous medium by observing the effect of having more than one drain in a simple model domain with a single source. The work is based on three-dimensional numerical simulations of the flow of injected water in a sandbox domain with porous volume completely filled by water and oil. The calculation uses the OpenFOAM library to solve Darcy’s equations for the dynamics of a two-phase flow: water as the wetting, oil as the nonwetting fluid. We observe the interactions of flows in different paths under changes of number of drains and their relative positions.

Communities in networks expose some intrinsic properties, each of them involves some influential nodes as its cores, around which the entire community grows gradually; the more the common neighbors that exist between a pair of nodes, the larger the possibility of belonging to the same community; the more the neighbors of any one node belong to a community, the larger the possibility that node belongs to that community too. In this paper, we present a novel method, which makes full utilization of these intrinsic properties to detect communities from networks. We iteratively select the node with the largest degree from the remainder of the network as the first seed of a community, then consider its first- and second-order neighbors to identify other seeds of the community, then expand the community by attracting nodes whose large proportion of neighbors have been in the community to join. In this way, we obtain a series of communities. However, some of them might be too small to make sense. Therefore, we merge some of the initial communities into larger ones to acquire the final community structure. In the entire procedure, we try to keep nodes in every community to be consistent with the properties as possible as we can, this leads to a high-quality result. Moreover, the proposed method works with a higher efficiency, it does not need any prior knowledge about communities (such as the number or the size of communities), and does not need to optimize any objective function either. We carry out extensive experiments on both some artificial networks and some real-world networks to testify the proposed method, the experimental results demonstrate that both the efficiency and the community-structure quality of the proposed method are promising, our method outperforms the competitors significantly.

Lattice Boltzmann method (LBM) was used to simulate two-dimensional MHD Al₂O₃/water nanofluid flow and heat transfer in an enclosure with a semicircular wall and a triangular heating obstacle. The effects of nanoparticle volume fraction ($0\le \varphi \le 0.05$), Rayleigh number $(10^4\le Ra\le 10^6)$, Hartmann number $(0\le Ha\le 60)$ and heating obstacle position (Cases 1–7) on flow pattern, temperature distribution and rate of heat transfer were investigated. The results show that with the enhancing Rayleigh number, the increasing nanoparticle volume fraction and the reducing Hartmann number, an enhancement in the average Nusselt number and the heat transfer appeared. The effect of Ha on the average Nu increases by increasing the Ra. It can also be found that the action of changing the heating obstacle position on the convection heat transfer is more important than that on the conduction heat transfer. The higher obstacle position in Cases 6 and 7 leads to the small value of the average Nusselt number. Moreover, the effect of Ha on average Nu in Case 1 at $Ra=10^6$ is more significant than other cases because the flow pattern in Case 1 is changed as increasing Ha.

Gas–liquid cylindrical cyclone (GLCC) separator is attractive for subsea gas–water separation due to its simplicity and small footprint. In this study, we designed a multi-channel conductance probe and conducted an experiment of gas–liquid separation in a GLCC separator. Local information of the lower swirling liquid film in the GLCC is collected by the multi-channel conductance probe. To uncover the hydrodynamics of swirl liquid film in the lower part of the GLCC, multi-scale distribution entropy (MSDE) in coupled phase space is employed to analyze the multi-channel conductance signals. The results of the MSDE enable to indicate the spatial dynamics of the swirling film, which can provide beneficial insights for uncovering the generation mechanism of gas bubble in the process of gas carry-under.

The determination of the physical and chemical properties of small nanoparticles plays a fundamental role in homogeneous and heterogeneous catalysis since a large number of the surface atoms of these nanoparticles can be exposed to chemical reactions, as well as in chemisorption, solid state physics, laser physics, crystal growth, epitaxy and surface science in general as most recent experimental and theoretical investigations have been disclosed. An empirical many-body Potential Energy Function (PEF) incorporating two-plus three-body atomistic potential derived by fitting experimental data pertaining to bulk Iridium has been applied to study the structural stability and energetics of Iridium nanoparticles of Ir_n ($n=3$–13). A constant temperature Molecular Dynamics (MD) technique is employed in the simulations. It is found that the energetically most stable structures of Iridium nanoparticles are in three-dimensional distorted compact forms close to symmetric geometries with the MD technique. The theoretical predictions are compared to the available theoretical and experimental literature data for binding energies, bond lengths and the most stable nanocluster geometry.

This paper formulates a new hyperchaotic system for particle motion. The continuous dependence on initial conditions of the system’s solution and the equilibrium stability, bifurcation, energy function of the system are analyzed. The hyperchaotic behaviors in the motion of the particle on a horizontal smooth plane are also investigated. It shows that the rich dynamic behaviors of the system, including the degenerate Hopf bifurcations and nondegenerate Hopf bifurcations at multiple equilibrium points, the irregular variation of Hamiltonian energy, and the hyperchaotic attractors. These results generalize and improve some known results about the particle motion system. Furthermore, the constraint of hyperchaos control is obtained by applying Lagrange’s method and the constraint change the system from a hyperchaotic state to asymptotically state. The numerical simulations are carried out to verify theoretical analyses and to exhibit the rich hyperchaotic behaviors.

This paper aims at the phenomenon of information selective propagation based on historical memory. A network model with memory strength and edge strength is established. The information propagation model with memory-clustering ability is designed with SIR model. And unsupervised learning is introduced to modify the performance. Based on the new network model, the core network and critical path that play a key role in the information propagation are found through the K-shell decomposition method. The research shows that the memory network contains an inertial channel for information propagation, it makes information propagation smooth. And information is selectively propagated in the new network, information is more inclined to propagate between nodes with powerful memory strength and close connections, in other words, people are more willing to propagate information to old friends who have been in contact for a long time instead of new friends.

In this paper, a dimensionless numerical study of the flow-field and heat transfer characteristics of an incompressible turbulent slot jet impinging obliquely over a moving surface of finite thickness is presented. Simulations were performed using $k-ϵ$ eddy viscosity turbulence model. The temperature field was solved simultaneously in the solid and the fluid domain. For a fixed impingement distance and a fixed Reynolds number, the impingement angle ($\varphi$) and plate velocity ($U_p$) were varied in the range of 30–75^∘ and 0–0.3, respectively. In the results, the length of the potential core depends on the jet inclination, which increases with increase in jet angle. The jet angle and plate velocity have more influence on the uphill side compared to the downhill side. The location of stagnation displaces toward the uphill side as the inclination angle decreases, and the drifting of stagnation point is noted with the variation in plate velocity. The average skin-friction coefficient increases with increase in $\varphi$ and $U_p$, and the influence of $U_p$ on the skin-friction coefficient is reduced as $\varphi$ increases. The maximum Nusselt number ($N{u}_{max}$) increases with increase in $\varphi$, and the drifting of $N{u}_{max}$ is observed with increase in plate velocity. It is found that the average Nusselt number increases quickly with increase in plate velocity for lower angles of impingement. The distribution of local heat flux follows the same trend as the local Nusselt number.

In social networks, resource sharing behaviors always take place in groups of individuals and rely on voluntary cooperation. In this work, first, a multi-player donor recipient game in which strategies describe individuals’ varying degrees of willingness to share resources is formulated, instead of using the limited binary decisions (e.g. share or not share) in a classical donor-recipient game. Second, the evolutionary dynamics of individual strategies are explored under the influence of two contribution-based resource allocation mechanisms: the total contribution-based allocation mechanism (TCAM) and the direct contribution-based allocation mechanism (DCAM). The results indicate that the network is dominated by the full-cooperation strategy when the cost-to-benefit ratio of resources is not too large and the DCAM is more effective than TCAM. Furthermore, the underlying reason why some strategies with higher sharing willingness can coexist in specific situations, is also explained in detail by leveraging macroscopic and microscopic perspective analysis. Finally, the influences of slandering and whitewashing behaviors conducted by a few malicious individuals on the allocation mechanisms are also studied. Current research will offer new insights into understanding the influence and optimizing the resource allocation policies in social networks.