International Journal of Modern Physics C

This paper provides the impact of roughness on the hydrodynamic behavior of the fluid over a convex-shape grooved cylinder by examining the shape and amplitude of the groove influence on the average flow rate. Direct numerical investigations of two-dimensional flow around a convex-shape grooved cylinder are performed at Reynolds number up to 100. A Reynolds number up to 40 corresponds to laminar flow in a stable state presenting the creeping and symmetry regimes and Re of 100 corresponds to laminar unstable flow presenting pure Karman vortex street flow regime. The number of grooves is set at 10, 20 and 30, uniformly spread around the periphery of the cylinder, with three different wave amplitudes of 1/50, 1/25 and 1/12.5, for each geometry. The numerical algorithm applied in this investigation is based on the finite volume method. The obtained results are compared with the smooth cylinder at the same Reynolds number, that latter shows excellent agreement with the available data in the literature. The forces acting on the cylinder are seen to be reduced by the presence of the grooves; this reduction is more significant with increasing groove amplitude, especially at a high Reynolds number. At Reynolds number equal 100, when the groove number and wave amplitude are set to 10, 1/12.5, respectively, the drag coefficient is lowered by about 10%, while the lift coefficient is reduced by around 25%.

The mixed traffic of conventional vehicles (CVs) and connected and autonomous vehicles (CAVs) will dominate a long period in the popularization of CAVs and its dynamics are still not clear, especially in some complicated traffic scenarios. In this paper, an improved cellular automaton model was applied to investigate the CVs–CAVs mixed traffic dynamics around freeway merging area. The lane-changing rules were improved to consider the difference between CAVs and CVs and also the variation of drivers’ aggressive driving during the merging process. The simulation has been conducted with different traffic demands and percentages of CAVs while the phase diagram, spatial-temporal diagram, characteristics of traffic volume, velocity profiles, stability and traffic conflict around the merging area were obtained. The results show that CAVs contribute to improving the traffic operation of freeway merging area in most conditions. However, more CAVs in the merging area without proper management will have a negative impact on traffic if the traffic demand for the main road is great. For example, the stability can decrease and particularly, the on-ramp traffic will be completely blocked when the percentage of CAVs is one. The findings can provide a basis for further research on the CVs–CAVs mixed traffic.

The dynamical responses of a rodlike anisotropic Heisenberg ferromagnet, irradiated by the propagating magnetic field wave, have been studied by Monte Carlo simulation using Metropolis single-spin-flip algorithm. A nonequilibrium dynamical phase transition has been observed. The transition temperature has been obtained from the peak position of the variance of the dynamic order parameter plotted as a function of the temperature. The transition has been found to occur at a lower temperature for the higher value of the amplitude of the propagating magnetic field wave. A comprehensive phase boundary was drawn. The phase boundary has been found to be insensitive to the wavelength of the propagating field wave. The boundary encloses more area of the region of the ordered phase for stronger anisotropy.

The Laplacian spectra are often used to characterize topological properties and dynamical characteristics of the networks. In this paper, we analytically calculate the network coherence quantified by the Laplacian spectra, which is a measurement of the extent of consensus in the coupled systems with the noise. Due to complex interactions between two coupled networks, we choose three types of weighted duplex networks with an aim of obtaining their exact solutions of the network coherence. Based on the network structures and matrix theories, we provide analytical expressions of the coherence regarding the network size and weights and further study the robustness of the coherence under network parameters. We finally show that smaller network sizes and larger weights will lead to better coherence and the best coherence happens in a duplex network because there are more connections between the layers.

In recent years, the interaction of electrons with the electromagnetic field in plasma at low pressure has received significant attention because of its high application. In low-pressure plasma, electrons can move long distances without any collisions in physical dimensions of the problem. This causes nonlocal dynamics, which leads to strong time and space dependence on and anisotropy of the distribution function. In this paper, a hybrid Particle-in-Cell (PIC) method with Direct Monte Carlo Simulation (DSMC) was proposed to investigate electron interactions with the electrostatic field. A capacitively coupled plasma (CCP) reactor at low pressure (0.01–10Pa) was also investigated. The effect of electric field was studied on electrons and field distribution in the computational domain at different times. Also, the amplitude current was investigated in a complete radio frequency (RF) cycle. As a result, this work proposed tools to examine the discharge flow for accurate knowledge of CCP discharge for optimization of reactors and its related processes.

The numerical method based on Sinc function is applied for the solution of Rosenau–KdV equation in this paper. The equation is fully-discretized by using the Sinc collocation method for spatial discretization and the forward finite difference for time discretization. The difference scheme is indicated to be conditionally stable using error analysis. The validity and accuracy of this method are verified by serval numerical experiments using single solitary waves, double solitary waves and multiple solitary waves.

This paper proposed a novel routing strategy with two tunable parameters, ${\alpha }_1$, $\beta$, based on the dynamic local information in a finite buffer network. The system behaves differently from that with a local routing strategy based on the degree with an adjustable parameter ${\alpha }_2$. Simulations show that, under dynamic local strategy, the maximal capacity of the network system increases with $\beta$ whereas it increases with the decrement of ${\alpha }_1$ in the case of all the nodes having identical delivering ability. The dynamic local routing performs much better than the local routing, which is demonstrated by a larger value of the critical packet generation rate. We found that buffer coefficient has a limited impact on the performance of network system. We also demonstrate that a smaller ${\alpha }_1$ would be better if we want to have an excellent traffic capacity considering the travel time, average path length and waiting time. Our study will be helpful to improve traffic performance in finite buffer networks.

Link prediction is a fundamental study with a variety of applications in complex network, which has attracted increased attention. Link prediction often can be used to recommend new friends in social networks, as well as recommend new products based on earlier shopping records in recommender systems, which brings considerable benefits for companies. In this work, we propose a new link prediction algorithm Local Neighbor Gravity Model (LNGM) algorithm, which is based on gravity and neighbors (1-hop and 2-hop), to suggest the formation of new links in complex networks. Extensive experiments on nine real-world datasets validate the superiority of LNGM on eight different benchmark algorithms. The results further validate the improved performance of LNGM.

The Haar wavelet collocation approach (HWCM) is an impressive numerical method for solving linear initial value problems when compared to the existing numerical methods (Adomian decomposition method (ADM) & Runge–Kutta method (RK4)). The objective of this study is to use the Haar-wavelet technique, Adomian decomposition technique (ADM) and Runge–Kutta (RK4) method to achieve the numerical solution of second-order ordinary differential equations. The proposed methods are applied to three different problems and the numerical results show that the HWCM has better agreement with analytic solutions than the other numerical methods.

In this paper, an efficient numerical treatment for magnetohydrodynamic flow and heat transfer calculations is presented, based on the differential transformation method (DTM), and the finite element method (FEM). This numerical treatment is obtained for the ordinary differential equation (ODE) which describes physically the boundary layer flow due to a permeable shrinking sheet. The DTM and FEM are utilized in this study because of their capacity to solve linear and nonlinear systems of ODEs, as well as their accuracy and convenience of use. These methods are approximate analytical that can usually get the solution in a series form. These numerical procedures are effective for this type of physical problem of varying degrees of complexity. The numerical calculations yield that the dimensionless velocity enhances when the wall mass suction for the flow is increased after the magnetic field is imposed. Also, the dimensionless velocity was found to increase for the large value of the magnetic parameter.

Accurate traffic flow forecasting is a prerequisite guarantee for the realization of intelligent transportation, but it is a challenging task due to the complex spatial-temporal dependence and uncertainty of traffic flow. In most existing approaches, spatial correlation is captured using graph convolution networks in a pre-determined graph structure. However, some nodes in such a graph structure have spatial correlations between them but are missing a connection, so the hidden spatial correlations between these nodes cannot be captured. Traffic flow has dynamic characteristics, showing different characteristics over time. Most methods ignore the dynamics of traffic flow when modeling the spatio–temporal correlation of traffic flow. We proposed a new network model (MSTA-GCN) to solve the above problem. The model presents a gated adaptive graph convolutional network that captures the hidden spatial correlations between graph nodes from the adaptive. In addition, the model introduces a multi-head spatial-temporal attention mechanism to pay attention to spatial-temporal information of different historical moments and different spatial dimensions to effectively capture the dynamics of spatial-temporal correlation of traffic flow. Extensive experiments were conducted on four datasets of PEMS. The experimental results show that the MSTA-GCN model has better forecasting performance compared with the baseline methods.

In this paper, a whale optimization algorithm based on Levy flight and memory (WOALFM) is proposed to solve the curvature discontinuity problem in the global path planning of unmanned vehicles. Levy flight and chaotic mapping are introduced to disturb the solutions of each generation and enhance the diversity of solutions. A memory strategy based on fractional-order expansion is presented to remember the influence of the positions of individuals in previous generations on the positions of current generation. This strategy based on Levey flight and memory can enhance searching ability and avoid falling into local optimum. Furthermore, the ratio of global searching to local searching can be adjusted to achieve desired results in WOALFM algorithm. The proposed WOALFM algorithm is tested and compared with five algorithms including whale optimization algorithm (WOA), Moth-Flame Optimization (MFO), particle swarm optimization (PSO), Fractional-Order Velocity based Particle Swarm Optimization (FOPSO) and Grey Wolf Optimizer (GWO) on 23 standard benchmark functions. The experimental results show the effectiveness of WOALFM. The proposed algorithm is applied to smooth path planning problem of unmanned vehicles. Three factors, including the length, curvature and curvature derivative of a path are considered in order to obtain the shortest smooth path without collisions. The experimental results show that more collision-free paths can be obtained in lower computational cost by the presented method.

In computational fluid dynamics (CFD), there is a transformation of methods over the years for building commercially coded software. Each method has predicted its own set of importance, but the exportation and prediction of data are some of the crucial elements for post-processing and validating results. In the present investigation, a detailed comparative analysis is performed over finite difference method (FDM) and finite volume method (FVM) method for the 1D steady-state heat conduction problem over a 1-m-long plate. The comparison was made between solution creation and validation between FDM and FVM for the analytical and computational scheme. The convergence-dependent study is performed as multi-objective optimization to predict how artificial neural network (ANN) can be used to verify and validate the solution of CFD.

In this paper, we present a numerical meshless method, which is based on the radial basis functions (RBF), to solve the morphology-dependent resonance (MDR) modes of a dielectric micro-sphere. To check the accuracy of the considered RBF method, we calculated the transverse electric (TE) and transverse magnetic (TM) modes of a perfect sphere that has an analytical characteristic equation. Besides, we employed the traditional mesh-dependent finite difference (FD) method to study the same problem. When we compared the results obtained by the three methods, we observe that the RBF method’s results are in very good agreement with the analytical and FD method’s results. We conclude that the presented meshless method may also be applicable to prolate and oblate spheroidal geometries, which do not have an analytical equation to calculate the modes.