All issues

Magnetic systems, in which due to competition between the direct Heisenberg exchange and the Dzyaloshinskii –Moriya interaction, magnetic vortex structures — skyrmions appear, were studied using the Metropolis algorithm.

The conditions for the nucleation and stable existence of magnetic skyrmions in two-dimensional magnetic films in the frame of the classical Heisenberg model were considered in the article. A thermal stability of skyrmions in a magnetic film was studied. The processes of the formation of various states in the system at different values of external magnetic fields were considered, various phases into which the Heisenberg spin system passes were recognized. The authors identified seven phases: paramagnetic, spiral, labyrinth, spiralskyrmion, skyrmion, skyrmion-ferromagnetic and ferromagnetic phases, a detailed analysis of the configurations is given in the article.

Two phase diagrams were plotted: the first diagram shows the behavior of the system at a constant $D$ depending on the values of the external magnetic field and temperature $(T, B)$, the second one shows the change of the system configurations at a constant temperature $T$ depending on the magnitude of the Dzyaloshinskii – Moriya interaction and external magnetic field: $(D, B)$.

The data from these numerical experiments will be used in further studies to determine the model parameters of the system for the formation of a stable skyrmion state and to develop methods for controlling skyrmions in a magnetic film.

The numerical methods used to simulate the evolution of hydrodynamic systems require the considerable use of computational resources thus limiting the number of possible simulations. The data-driven simulation technique is one promising approach to the development of heuristic models, which may speed up the study of such models. In this approach, machine learning methods are used to tune the weights of an artificial neural network that predicts the state of a physical system at a given point in time based on initial conditions. This article describes an original neural network architecture and a novel multi-stage training procedure which create a heuristic model of a two-phase flow in a heterogeneous porous medium. The neural network-based model predicts the states of the grid cells at an arbitrary timestep (within the known constraints), taking in only the initial conditions: the properties of the heterogeneous permeability of the medium and the location of sources and sinks. The proposed model requires orders of magnitude less processor time in comparison with the classical numerical method, which served as a criterion for evaluating the effectiveness of the trained model. The proposed architecture includes a number of subnets trained in various combinations on several datasets. The techniques of adversarial training and weight transfer are utilized.

The influence of the process of initiating a rapid local heat release near surface streamlined by supersonic gas (air) flow on the separation region that occurs during a fast turn of the flow was investigated. This surface consists of two planes that form obtuse angle when crossing, so that when flowing around the formed surface, the supersonic gas flow turns by a positive angle, which forms an oblique shock wave that interacts with the boundary layer and causes flow separation. Rapid local heating of the gas above the streamlined surface simulates long spark discharge of submicrosecond duration that crosses the flow. The gas heated in the discharge zone interacts with the separation region. The flow can be considered two-dimensional, so the numerical simulation is carried out in a two-dimensional formulation. Numerical simulation was carried out for laminar regime of flow using the sonicFoam solver of the OpenFOAM software package.

The paper describes a method for constructing a two-dimensional computational grid using hexagonal cells. A study of grid convergence has been carried out. A technique is given for setting the initial profiles of the flow parameters at the entrance to the computational domain, which makes it possible to reduce the computation time by reducing the number of computational cells. A method for non-stationary simulation of the process of rapid local heating of a gas is described, which consists in superimposing additional fields of increased pressure and temperature values calculated from the amount of energy deposited in oncoming supersonic gas flow on the corresponding fields of values obtained in the stationary case. The parameters of the energy input into the flow corresponding to the parameters of the electric discharge process, as well as the parameters of the oncoming flow, are close to the experimental values.

During analyzing numerical simulation data it was found that the initiation of rapid local heating leads to the appearance of a gas-dynamic perturbation (a quasi-cylindrical shock wave and an unsteady swirling flow), which, when interacting with the separation region, leads to a displacement of the separation point downstream. The paper considers the question of the influence of the energy spent on local heating of the gas, and of the position on the streamlined surface of the place of heating relative to the separation point, on the value of its maximum displacement.

When modeling turbulent flows in practical applications, it is often necessary to carry out a series of calculations of bodies of similar topology. For example, bodies that differ in the shape of the fairing. The use of convolutional neural networks allows to reduce the number of calculations in a series, restoring some of them based on calculations already performed. The paper proposes a method that allows to apply a convolutional neural network regardless of the method of constructing a computational mesh. To do this, the flow field is reinterpolated to a uniform mesh along with the body itself. The geometry of the body is set using the signed distance function and masking. The restoration of the flow field based on part of the calculations for similar geometries is carried out using a neural network of the UNet type with a spatial attention mechanism. The resolution of the nearwall region, which is a critical condition for turbulent modeling, is based on the equations obtained in the nearwall domain decomposition method.

A demonstration of the method is given for the case of a flow around a rounded plate by a turbulent air flow with different rounding at fixed parameters of the incoming flow with the Reynolds number $Re = 10^5$ and the Mach number $M = 0.15$. Since flows with such parameters of the incoming flow can be considered incompressible, only the velocity components are studied directly. The flow fields, velocity and friction profiles obtained by the surrogate model and numerically are compared. The analysis is carried out both on the plate and on the rounding. The simulation results confirm the prospects of the proposed approach. In particular, it was shown that even if the model is used at the maximum permissible limits of its applicability, friction can be obtained with an accuracy of up to 90%. The work also analyzes the constructed architecture of the neural network. The obtained surrogate model is compared with alternative models based on a variational autoencoder or the principal component analysis using radial basis functions. Based on this comparison, the advantages of the proposed method are demonstrated.

The paper considers an earlier proposed by the author discrete modification of the A. P. Mikhailov “power – society” model. The modification is based on a stochastic cellular automaton, it’s microdynamics being completely different from the c continuous model based on differential equations. However, the macrodynamics of the discrete modification is shown in previous works to be equivalent to one of the continuous model. This is important, but at the same time raises the question why use the discrete model. The answer lies in its flexibility, which allows adding a variety of factors, the consideration of which in a continuous model either leads to a significant increase in computational complexity or is simply impossible.

This paper considers several examples of such applicability expansion of the model, with the help of which a number of applied problems are solved.

One of the modifications of the model takes into account economic ties between regions and municipalities, which could not be studied in the basic model. Computational experiments confirmed the improvement of the socio-economic indicators of the system under the influence of the ties.

The second modification allows internal migration in the system. Using it we studied the socio-economic development of a more prosperous region that attracts migrants.

Next we studied the dynamics of the system while the number of regions and municipalities changes. The negative impact of this process on the socio-economic indicators of the system was shown and possible control was found to overcome this negative impact.

The results of this study, therefore, include both the solution of some applied problems and the demonstration of the broader applicability of the discrete model compared with the continuous one.

In single-photon emission computed tomography (SPECT), patients with bone disorders receive a radiopharmaceutical (RP) that accumulates selectively in pathological lesions. Accurate quantification of RP uptake plays a critical role in disease staging, prognosis, and the development of personalized treatment strategies. Traditionally, the accuracy of quantitative assessment is evaluated through in vitro clinical trials using the standardized physical NEMA IEC phantom, which contains six spheres simulating lesions of various sizes. However, such experiments are limited by high costs and radiation exposure to researchers. This study proposes an alternative in silico approach based on numerical simulation using a digital twin of the NEMA IEC phantom. The computational framework allows for extensive testing under varying conditions without physical constraints. Analogous to clinical protocols, we calculated the recovery coefficient (RC_max), defined as the ratio of the maximum activity in a lesion to its known true value. The simulation settings were tailored to clinical SPECT/CT protocols involving ^99mTc for patients with bone-related diseases. For the first time, we systematically analyzed the impact of lesion-to-background ratios and post-reconstruction filtering on RC_max values. Numerical experiments revealed the presence of edge artifacts in reconstructed lesion images, consistent with those observed in both real NEMA IEC phantom studies and patient scans. These artifacts introduce instability into the iterative reconstruction process and lead to errors in activity quantification. Our results demonstrate that post-filtering helps suppress edge artifacts and stabilizes the solution. However, it also significantly underestimates activity in small lesions. To address this issue, we introduce post-reconstruction correction factors derived from our simulations to improve the accuracy of quantification in lesions smaller than 20 mm in diameter.

Population dynamics is considered in a modified chemostat model including diffusion, chemotaxis, and nonlocal competitive losses. To account for influence of the external environment on the population of the ecosystem, a random parameter is included into the model equations. Computer simulations reveal three dynamic modes depending on system parameters: the transition from initial state to a spatially homogeneous steady state, to a spatially inhomogeneous distribution of population density, and elimination of population density.

In this paper particularities of the swirling turbulent flow of monodisperse suspension in the hydrocyclone with injector are investigated on the base of the numerical simulation. The 2D axisymmetric approximation of Reynolds Stresses Model and model of mixture is used to describe the swirling turbulent flow field of suspension and particles parameters in the hydrocyclone. Special attention is paid to the clarification of mechanisms of injection influence on the reorganization of hydrodynamic field and finally on classification mechanisms. It is shown that tangential injection method stronger effects separation curve compared to the radial one.

This work is devoted to creation of static atomic model of two surfaces in contact at electric diamond grinding: single-points diamond and material grinded of them. At the heart of the work there are issues of computer visualization of these surfaces at the molecular level, since traditional mathematical description does not possess sufficient visualization to demonstrate some aspects of the atomic tribology of metal cutting to simultaneously occurring the different, by their physical nature, processes. And in the electric diamond grinding blends effect of several processes simultaneously: mechanical, electrical and electrochemical. So the modeling technique proposed by authors is still the only way to see what is happening at the atomic level, cutting material of single-point diamond.

The article presents the results of computer simulations aimed to assess the influence of tree spatial locations (planting schemes) on the productivity and the dynamics of soil fertility in forest plantations. The growth of aspen (Populus tremula L.) in plantations with short rotation (30 years) was simulated in the EFIMOD system of models with the soil and climatic data matching forested lands in the Mari El Republic. The outcome reveals that higher biomass rates, increase in soil organic matter stocks, and the minimal loss of soil nitrogen can be obtained when the distance between trees in the row equals 1–4 m and 4–6 м in aisles.