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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.

Despite numerous achievements of modern science the problem of lightning initiation in an electrodeless thundercloud, the maximum electric field strength inside which is approximately an order of magnitude lower than the dielectric strength of air, remains unsolved. Although there is no doubt that discharge activity begins with the appearance of positive streamers, which can develop under approximately half the threshold electric field as compared to negative ones, it remains unexplored how cold weakly conducting streamer systems unite in a joint hot well-conducting leader channel capable of self-propagation due to effective polarization in a relatively small external field. In this study, we present a self-organizing transport model which is applied to the case of electric discharge tree formation in a thundercloud. So, the model is aimed at numerical simulation of the initial stage of lightning discharge development. Among the innovative features of the model are the absence of grid spacing, high spatiotemporal resolution, and consideration of temporal evolution of electrical parameters of transport channels. The model takes into account the widely known asymmetry between threshold fields needed for positive and negative streamers development. In our model, the resulting well-conducting leader channel forms due to collective effect of combining the currents of tens of thousands of interacting streamer channels each of which initially has negligible conductivity and temperature that does not differ from the ambient one. The model bipolar tree is a directed graph (it has both positive and negative parts). It has morphological and electrodynamic characteristics which are intermediate between laboratory long spark and developed lightning. The model has universal character which allows to use it in other tasks related to the study of transport (in the broad sense of the word) networks.

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 aim of the work is to study a monotone finite-difference scheme of the second order of accuracy, created on the basis of a generalization of the one-dimensional Z-scheme. The study was carried out for model equations of the transfer of an incompressible medium. The paper describes a two-dimensional generalization of the Z-scheme with nonlinear correction, using instead of streams oblique differences containing values from different time layers. The monotonicity of the obtained nonlinear scheme is verified numerically for the limit functions of two types, both for smooth solutions and for nonsmooth solutions, and numerical estimates of the order of accuracy of the constructed scheme are obtained.

The constructed scheme is absolutely stable, but it loses the property of monotony when the Courant step is exceeded. A distinctive feature of the proposed finite-difference scheme is the minimality of its template. The constructed numerical scheme is intended for models of plasma instabilities of various scales in the low-latitude ionospheric plasma of the Earth. One of the real problems in the solution of which such equations arise is the numerical simulation of highly nonstationary medium-scale processes in the earth’s ionosphere under conditions of the appearance of the Rayleigh – Taylor instability and plasma structures with smaller scales, the generation mechanisms of which are instabilities of other types, which leads to the phenomenon F-scattering. Due to the fact that the transfer processes in the ionospheric plasma are controlled by the magnetic field, it is assumed that the plasma incompressibility condition is fulfilled in the direction transverse to the magnetic field.

The background social strain of a society can be quantitatively estimated using various statistical indicators. Mathematical models, allowing to forecast the dynamics of social strain, are successful in describing various social processes. If the number of interacting groups is small, the dynamics of the corresponding indicators can be modelled with a system of ordinary differential equations. The increase in the number of interacting components leads to the growth of complexity, which makes the analysis of such models a challenging task. A continuous social stratification model can be considered as a result of the transition from a discrete number of interacting social groups to their continuous distribution in some finite interval. In such a model, social strain naturally spreads locally between neighbouring groups, while in reality, the social elite influences the whole society via news media, and the Internet allows non-local interaction between social groups. These factors, however, can be taken into account to some extent using the term of the model, describing negative external influence on the society. In this paper, we develop a continuous social stratification model, describing the dynamics of two societies connected through migration. We assume that people migrate from the social group of donor society with the highest strain level to poorer social layers of the acceptor society, transferring the social strain at the same time. We assume that all model parameters are constants, which is a realistic assumption for small societies only. By using the finite volume method, we construct the spatial discretization for the problem, capable of reproducing finite propagation speed of social strain. We verify the discretization by comparing the results of numerical simulations with the exact solutions of the auxiliary non-linear diffusion equation. We perform the numerical analysis of the proposed model for different values of model parameters, study the impact of migration intensity on the stability of acceptor society, and find the destabilization conditions. The results, obtained in this work, can be used in further analysis of the model in the more realistic case of inhomogeneous coefficients.

A promising method of increasing precipitation in arid climates is the method of creating a vertical high-temperature jet seeded by hygroscopic aerosol. Such an installation makes it possible to create artificial clouds with the possibility of precipitation formation in a cloudless atmosphere, unlike traditional methods of artificial precipitation enhancement, which provide for increasing the efficiency of precipitation formation only in natural clouds by seeding them with nuclei of crystallization and condensation. To increase the power of the jet, calcium chloride, carbamide, salt in the form of a coarse aerosol, as well as NaCl/TiO₂ core/shell novel nanopowder, which is capable of condensing much more water vapor than the listed types of aerosols, are added. Dispersed inclusions in the jet are also centers of crystallization and condensation in the created cloud to increase the possibility of precipitation. To simulate convective flows in the atmosphere, a mathematical model of FlowVision large-scale atmospheric flows is used, the solution of the equations of motion, energy and mass transfer is carried out in relative variables. The statement of the problem is divided into two parts: the initial jet model and the FlowVision large-scale atmospheric model. The lower region, where the initial high-speed jet flows, is calculated using a compressible formulation with the solution of the energy equation with respect to the total enthalpy. This division of the problem into two separate subdomains is necessary in order to correctly carry out the numerical calculation of the initial turbulent jet at high velocity (M > 0.3). The main mathematical dependencies of the model are given. Numerical experiments were carried out using the presented model, experimental data from field tests of the installation for creating artificial clouds were taken for the initial data. A good agreement with the experiment is obtained: in 55% of the calculations carried out, the value of the vertical velocity at a height of 400 m (more than 2 m/s) and the height of the jet rise (more than 600 m) is within an deviation of 30% of the experimental characteristics, and in 30% of the calculations it is completely consistent with the experiment. The results of numerical simulation allow evaluating the possibility of using the high-speed jet method to stimulate artificial updrafts and to create precipitation. The calculations were carried out using FlowVision CFD software on SUSU Tornado supercomputer.

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.

This paper demonstrates the advantages of using artificial intelligence algorithms for the design of experiment theory, which makes possible to improve the accuracy of parameter identification for an elastostatic robot model. Design of experiment for a robot consists of the optimal configuration-external force pairs for the identification algorithms and can be described by several main stages. At the first stage, an elastostatic model of the robot is created, taking into account all possible mechanical compliances. The second stage selects the objective function, which can be represented by both classical optimality criteria and criteria defined by the desired application of the robot. At the third stage the optimal measurement configurations are found using numerical optimization. The fourth stage measures the position of the robot body in the obtained configurations under the influence of an external force. At the last, fifth stage, the elastostatic parameters of the manipulator are identified based on the measured data.

The objective function required to finding the optimal configurations for industrial robot calibration is constrained by mechanical limits both on the part of the possible angles of rotation of the robot’s joints and on the part of the possible applied forces. The solution of this multidimensional and constrained problem is not simple, therefore it is proposed to use approaches based on artificial intelligence. To find the minimum of the objective function, the following methods, also sometimes called heuristics, were used: genetic algorithms, particle swarm optimization, simulated annealing algorithm, etc. The obtained results were analyzed in terms of the time required to obtain the configurations, the optimal value, as well as the final accuracy after applying the calibration. The comparison showed the advantages of the considered optimization techniques based on artificial intelligence over the classical methods of finding the optimal value. The results of this work allow us to reduce the time spent on calibration and increase the positioning accuracy of the robot’s end-effector after calibration for contact operations with high loads, such as machining and incremental forming.

Coffee berry disease (CBD), resulting from the Colletotrichum kahawae fungal pathogen, poses a severe risk to coffee crops worldwide. Focused on coffee berries, it triggers substantial economic losses in regions relying heavily on coffee cultivation. The devastating impact extends beyond agricultural losses, affecting livelihoods and trade economies. Experimental insights into coffee berry disease provide crucial information on its pathogenesis, progression, and potential mitigation strategies for control, offering valuable knowledge to safeguard the global coffee industry. In this paper, we investigated the mathematical model of coffee berry disease, with a focus on the dynamics of the coffee plant and Colletotrichum kahawae pathogen populations, categorized as susceptible, exposed, infected, pathogenic, and recovered (SEIPR) individuals. To address the system of nonlinear differential equations and obtain semi-analytical solution for the coffee berry disease model, a novel analytical approach combining the Shehu transformation, Akbari – Ganji, and Pade approximation method (SAGPM) was utilized. A comparison of analytical results with numerical simulations demonstrates that the novel SAGPM is excellent efficiency and accuracy. Furthermore, the sensitivity analysis of the coffee berry disease model examines the effects of all parameters on the basic reproduction number $R_0$. Moreover, in order to examine the behavior of the model individuals, we varied some parameters in CBD. Through this analysis, we obtained valuable insights into the responses of the coffee berry disease model under various conditions and scenarios. This research offers valuable insights into the utilization of SAGPM and sensitivity analysis for analyzing epidemiological models, providing significant utility for researchers in the field.

This paper focuses on the problem of modeling and analyzing dynamic regimes, both regular and chaotic, in systems of coupled populations in the presence of random disturbances. The discrete Ricker model is used as the initial deterministic population model. The paper examines the dynamics of two populations coupled by migration. Migration is proportional to the difference between the densities of two populations with a coupling coefficient responsible for the strength of the migration flow. Isolated population subsystems, modeled by the Ricker map, exhibit various dynamic modes, including equilibrium, periodic, and chaotic ones. In this study, the coupling coefficient is treated as a bifurcation parameter and the parameters of natural population growth rate remain fixed. Under these conditions, one subsystem is in the equilibrium mode, while the other exhibits chaotic behavior. The coupling of two populations through migration creates new dynamic regimes, which were not observed in the isolated model. This article aims to analyze the dynamics of corporate systems with variations in the flow intensity between population subsystems. The article presents a bifurcation analysis of the attractors in a deterministic model of two coupled populations, identifies zones of monostability and bistability, and gives examples of regular and chaotic attractors. The main focus of the work is in comparing the stability of dynamic regimes against random disturbances in the migration intensity. Noise-induced transitions from a periodic attractor to a chaotic attractor are identified and described using direct numerical simulation methods. The Lyapunov exponents are used to analyze stochastic phenomena. It has been shown that in this model, there is a region of change in the bifurcation parameter in which, even with an increase in the intensity of random perturbations, there is no transition from order to chaos. For the analytical study of noise-induced transitions, the stochastic sensitivity function technique and the confidence domain method are used. The paper demonstrates how this mathematical tool can be employed to predict the critical noise intensity that causes a periodic regime to transform into a chaotic one.