International Journal of Modern Physics C最新文献

Studies have indicated that focusing solely on pairwise interactions between two nodes disregards the associativity among multi-nodes in the network’s local structure. This associativity can be seen as dependencies among nodes, where certain edges’ presence depends on the path leading to it. Examinations on diverse datasets have approved that the variable order of chained dependencies allows for the preservation of structure information, which enables the reconstruction of the original network into a Higher-Order Network (HON) with improved quality of network representation. This paper proposes a Density-based Higher-Order Network Embedding (DHONE) algorithm, which integrates the concept of higher-order density into the network-embedding process in order to classify the contribution of different orders of dependencies. Through the construction of a novel and effective higher-order adjacency matrix, DHONE steadily improves the accuracy of network representation learning. Experimental results demonstrate DHONEs proficiency in improving embedding accuracy and overall algorithm robustness. Furthermore, grounded in the concept of higher-order density proposed herein, numerous dependencies have been discerned within the network generated from trajectories, potentially indicating the role of multi-node structures in networks.

This paper presents a numerical procedure for handling delay fractional differential problems where the derivative is defined using the M-fractional approach. The proposed scheme modus operandi is based on the shifted Legendre–Galerkin procedure, which is a powerful tool for solving complex differential models of generalized fractional derivatives. The method involves constructing a series of Legendre polynomials that form the basis functions for approximating the solution of the required problem. The coefficients of the series are obtained after solving an algebraic system of linear types that results from the application of the Galerkin practice. The numerical accuracy and convergence assessment are also presented together with various results. Simulations-based analyses are realized to validate the truthfulness and exactness of the process. The results manifest that the M-derivatives and the Galerkin practice provide alternative innovative approaches for handling M-delay fractional problems. Several keynotes and future recommendations are exhibited at the last with some selected references.

Birds have traits that can induce better aerodynamic efficiency along with high manoeuvring capability during its flight, which could be shared with unmanned aerial vehicles for improving their aerodynamic performances. One such feature of the wing tip, i.e. the primary feathers of the birds could be an effective geometrical feature to reduce the wing tip vortices. This paper presents the bio-inspired wing tip devices, i.e. three-and four-tipped multiple winglets in reducing the strength of vortices emanating from the wing tip of the wing operating in the Reynolds number (Re) of $0.9794\times 10^5$ and $0.9794\times 10^6$. Different combinations of both three- and four-tipped multiple winglets have been designed by varying the cant angle of each tip. Numerical simulations were carried out using Ansys-Fluent by solving three-dimensional Reynolds averaged Navier–Stokes formulations coupled with k-$ϵ$ turbulence model to resolve the features of tip vortices. The simulation clearly indicates that there is a strong correlation between the size of the vortices and the aerodynamic performance parameters such as $C_L/C_D$, ${(C_L)}_{max,}$ $C_L{}^{0.5}/C_D$, $C_L{}^{1.5}/{\mathrm{\text{C}}}_D$. The three- and four-tipped multiple winglets are effective in reducing vortex drag by disintegrating large strength vortex which occurs in the tip of s

The utilization of latent heat storage units with their high energy density and isothermal heat transfer behavior can enhance the performance of thermal energy systems. This study aims to investigate the effect of fin placement on the melting time of a wire and tube-based Phase Change Material (PCM) heat exchanger using numerical simulations. The study introduces a new complex geometry for the heat exchanger, and numerical analysis of heat transfer was conducted in Ansys Fluent software using an established solidification and melting model. The numerical results were validated against experimental data, and it was found that the position of the tubes and fins had a significant impact on heat transfer within the PCM. The model was able to predict temperature data with a maximum discrepancy of 3%.

Optimal transport aims to learn a mapping of sources to targets by minimizing the cost, which is typically defined as a function of distance. The solution to this problem consists of straight line segments optimally connecting sources to targets, and it does not exhibit branching. These optimal solutions are in stark contrast with both natural, and man-made transportation networks, where branching structures are prevalent. Here, we discuss a fast heuristic branching method for optimal transport in networks. We also provide several numerical applications to synthetic examples, a simplified cardiovascular network, and the “Santa Claus” distribution network which includes 141$$182 cities around the world, with known location and population.

We present a mapping between a Schrödinger equation with a shifted nonlinear potential and the Navier–Stokes equation. Following a generalization of the Madelung transformations, we show that the inclusion of the Bohm quantum potential plus the laplacian of the phase field in the nonlinear term leads to continuity and momentum equations for a dissipative incompressible Navier–Stokes fluid. An alternative solution, built using a complex quantum diffusion, is also discussed. The present models may capture dissipative effects in quantum fluids, such as Bose–Einstein condensates, as well as facilitate the formulation of quantum algorithms for classical dissipative fluids.