International Journal of Modern Physics C

A numerical investigation is carried out to analyze the flow patterns, drag and lift coefficients, and vortex shedding around a square cylinder using a control circular bar upstream and downstream. Lattice Boltzmann method (LBM) was used to investigate flow over a square cylinder controlled by upstream and downstream circular bar, which is the main novelty of this study. Compared with those available results in the literature, the code for flow over a single square cylinder proves valid. The Reynolds number (Re) based on the width of the square cylinder ($D$) and diameter of circular bar ($d$) are 100 for square cylinder, 30 and 50 for different circular bars. Numerical simulations are performed in the ranges of $1\le L∕D\le 5$ and $1\le G∕D\le 5$, where $L$ and $G$ are the center-to-center distances between the bar and cylinder. Five distinct flow patterns are observed in the present study. It is found that the maximum percentage reduction in drag coefficient is 59.86% by upstream control bar, and the maximum percentage reduction in r.m.s. lift coefficient is 73.69% by downstream control bar. By varying the distance ratio for the downstream control bar, a critical value of distance ratio is found where there are two domain frequencies.

We study epidemic dynamics in weighted networks, where the susceptible nodes can change their infected neighbors by rewiring the edge. Two kinds of edges with different weights are considered and the effects of the edge density and the rewiring process on the epidemic properties are studied. The simulation results show that a fraction of infected nodes in the equilibrium state exhibit a nonmonotonic or monotonic behavior with the increase of the fraction of heavier weighted edges or the rewiring probability. Under the rewiring process, the system can evolve to a lower infected state in comparison with the result obtained in static network. The effect of rewiring different weighted edges on the epidemic behavior is also discussed.

During the past three decades, the advantageous concept of the Green’s function has been extended from linear systems to nonlinear ones. At that, there exists a rigorous and an approximate extension. The rigorous extension introduces the so-called backward and forward propagators, which play the same role for nonlinear systems as the Green’s function plays for linear systems. The approximate extension involves the Green’s formula for linear systems with a Green’s function satisfying the corresponding nonlinear equation. For the numerical evaluation of nonlinear ordinary differential equations, the second approach seems to be more convenient. In this article we study a hierarchy of nonlinear partial differential equations that can be approximated by the second approach. Green’s functions for particular nonlinearities are derived explicitly. Numerical error analysis in the case of exponential nonlinearity for different source functions supports the advantage of the approach.

The El Niño-Southern Oscillation (ENSO) is the most important driver of natural climate variability and is characterized by anomalies in the sea surface temperatures (SST) over the tropical Pacific ocean. It has three phases: neutral, a warming phase or El Niño, and a cooling phase called La Niña. In this research, we modeled the climate under the three phases as a network and characterized its properties. We utilized the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily surface temperature reanalysis data from January 1950 to December 2016. A network associated to a month was created using the temperature spanning from the previous month to the succeeding month, for a total of three months worth of data for each network. Each site of the included data was a potential node in the network and the existence of links were determined by the strength of their relationship, which was based on mutual information. Interestingly, we found that climate networks exhibit small-world properties and these are found to be more prominent from October to April, coinciding with observations that El Niño occurrences peak from December to March. During these months, the temperature of a relatively large part of the Pacific ocean and its surrounding areas increase and the anomaly values become synchronized. This synchronization in the temperature anomalies forms links around the Pacific, increasing the clustering in the region and in effect, that of the entire network.

In this paper, we propose a stochastic win-stay-lose-learn in which an individual is more likely to imitate one of its nearest neighbor’s strategy if its aspiration is not achieved. The results on the spatial public good game show that the cooperation can be greatly enhanced when the aspiration is moderate. Besides, we have studied the time evolution of the spatial distribution of strategies and the probability that cooperators and defectors choose to learn respectively.

Turbulent Schmidt number as an important parameter in computational fluid dynamic (CFD) simulations is strongly dependent on height, whereas it is mostly considered to be constant in the literature. This paper presents a new variable turbulent Schmidt number formulation which can calculate the relative concentrations (RCs) in neutral atmospheric conditions more accurately. To achieve this aim, RCs from continuous releases are calculated in different distances by the analytical Gaussian plume mode. CFD simulations are carried out for single stack dispersion on a flat terrain surface and an inverse procedure is then applied so that different turbulent Schmidt numbers are used as inputs to determine the RCs to select the “best-fit” turbulent Schmidt number value. This process is continued for different heights to fit a curve to obtain the new formulation for turbulent Schmidt number varying with height. The values are compared with experimental results. The comparison indicates that the new formulation for turbulent Schmidt number is more accurate and reliable than previous research works.

Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantical similarity and co-occurrence which indicates the behavior information from other users in web search. Regarding the current user’s preference to a query, we combine the user’s short-term and long-term search behavior in a linear fashion and deal with the data sparse problem with Bayesian probabilistic matrix factorization (BPMF). In particular, we also investigate the impact of different personalization strategies (the combination of the user’s short-term and long-term search behavior) on the performance of query suggestion reranking. We quantify the improvement of our proposed UB model against a state-of-the-art baseline using the public AOL query logs and show that it beats the baseline in terms of metrics used in query suggestion reranking. The experimental results show that: (i) for personalized ranking, users’ behavioral information helps to improve query suggestion effectiveness; and (ii) given a query, merging information inferred from the short-term and long-term search behavior of a particular user can result in a better performance than both plain approaches.

The steady-state dynamics of a two-channel TASEP coupled with Langmuir kinetics in the presence of a localized bottleneck are investigated under a fully asymmetric lane-changing rule. A hybrid mean-field approach is adopted to generate the density profiles as well as phase diagrams. The effect of lane-changing rate and strength of bottleneck on the stationary dynamics of the system has also been investigated. It is observed that an increase in lane-changing rate weakens the effect of bottleneck. As a part of studying the shock dynamics, we have identified and analyzed turning effect in the movement of shock with respect to lane-changing rate. Our theoretical arguments are in good agreement with extensively performed Monte-Carlo simulations.

A numerical approach based on the immersed boundary (IB), lattice Boltzmann and nonlinear finite element method (FEM) is proposed to simulate hydrodynamic interactions of very flexible objects. In the present simulation framework, the motion of fluid is obtained by solving the discrete lattice Boltzmann equations on Eulerian grid, the behaviors of flexible objects are calculated through nonlinear dynamic finite element method, and the interactive forces between them are implicitly obtained using velocity correction IB method which satisfies the no-slip conditions well at the boundary points. The efficiency and accuracy of the proposed Immersed Boundary-Lattice Boltzmann-Finite Element method is first validated by a fluid–structure interaction (F-SI) benchmark case, in which a flexible filament flaps behind a cylinder in channel flow, then the nonlinear vibration mechanism of the cylinder-filament system is investigated by altering the Reynolds number of flow and the material properties of filament. The interactions between two tandem and side-by-side identical objects in a uniform flow are also investigated, and the in-phase and out-of-phase flapping behaviors are captured by the proposed method.

The effective temperature configuration of parallel tempering (PT) in finite-time optimization is studied for the solution of the traveling salesman problem. An experimental analysis is conducted to decide the relative importance of the two characteristic temperatures, the specific-heat-peak temperature referred to in the general guidelines and the effective intermediate temperature identified in the recent study on simulated annealing (SA). The results show that the operation near the former has no notable significance contrary to the conventional belief but that the operation near the latter plays a crucial role in fulfilling the optimization function of PT. The method shares the same origin of effectiveness with the SA and SA-related algorithms.