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In the present paper, we discuss the possibility of a simplified three-dimensional unsteady simulation of plasma-assisted combustion of gaseous fuel in a supersonic airflow. Simulation was performed by using FlowVision CFD software. Analysis of experimental geometry show that it has essentially 3D nature that conditioned by the discrete fuel injection into the flow as well as by the presence of the localized plasma filaments. Study proposes a variant of modeling geometry simplification based on symmetry of the aerodynamic duct and periodicity of the spatial inhomogeneities. Testing of modified FlowVision $k–\varepsilon$ turbulence model named «KEFV» was performed for supersonic flow conditions. Based on that detailed grid without wall functions was used the field of heat and near fuel injection area and surfaces remote from the key area was modeled with using of wall functions, that allowed us to significantly reduce the number of cells of the computational grid. Two steps significantly simplified a complex problem of the hydrocarbon fuel ignition by means of plasma generation. First, plasma formations were simulated by volumetric heat sources and secondly, fuel combustion is reduced to one brutto reaction. Calibration and parametric optimization of the fuel injection into the supersonic flow for IADT-50 JIHT RAS wind tunnel is made by means of simulation using FlowVision CFD software. Study demonstrates a rather good agreement between the experimental schlieren photo of the flow with fuel injection and synthetical one. Modeling of the flow with fuel injection and plasma generation for the facility T131 TSAGI combustion chamber geometry demonstrates a combustion mode for the set of experimental parameters. Study emphasizes the importance of the computational mesh adaptation and spatial resolution increasing for the volumetric heat sources that model electric discharge area. A reasonable qualitative agreement between experimental pressure distribution and modeling one confirms the possibility of limited application of such simplified modeling for the combustion in high-speed flow.

This paper is devoted to the analysis of the effect of additive and parametric noise on the processes occurring in the nerve cell. This study is carried out on the example of the well-known Morris – Lecar model described by the two-dimensional system of ordinary differential equations. One of the main properties of the neuron is the excitability, i.e., the ability to respond to external stimuli with an abrupt change of the electric potential on the cell membrane. This article considers a set of parameters, wherein the model exhibits the class 2 excitability. The dynamics of the system is studied under variation of the external current parameter. We consider two parametric zones: the monostability zone, where a stable equilibrium is the only attractor of the deterministic system, and the bistability zone, characterized by the coexistence of a stable equilibrium and a limit cycle. We show that in both cases random disturbances result in the phenomenon of the stochastic generation of mixed-mode oscillations (i. e., alternating oscillations of small and large amplitudes). In the monostability zone this phenomenon is associated with a high excitability of the system, while in the bistability zone, it occurs due to noise-induced transitions between attractors. This phenomenon is confirmed by changes of probability density functions for distribution of random trajectories, power spectral densities and interspike intervals statistics. The action of additive and parametric noise is compared. We show that under the parametric noise, the stochastic generation of mixed-mode oscillations is observed at lower intensities than under the additive noise. For the quantitative analysis of these stochastic phenomena we propose and apply an approach based on the stochastic sensitivity function technique and the method of confidence domains. In the case of a stable equilibrium, this confidence domain is an ellipse. For the stable limit cycle, this domain is a confidence band. The study of the mutual location of confidence bands and the boundary separating the basins of attraction for different noise intensities allows us to predict the emergence of noise-induced transitions. The effectiveness of this analytical approach is confirmed by the good agreement of theoretical estimations with results of direct numerical simulations.

This article solves the problem of constructing a neuro-fuzzy model of fuzzy rules formation and using them for objects state evaluation in conditions of uncertainty. Traditional mathematical statistics or simulation modeling methods do not allow building adequate models of objects in the specified conditions. Therefore, at present, the solution of many problems is based on the use of intelligent modeling technologies applying fuzzy logic methods. The traditional approach of fuzzy systems construction is associated with an expert attraction need to formulate fuzzy rules and specify the membership functions used in them. To eliminate this drawback, the automation of fuzzy rules formation, based on the machine learning methods and algorithms, is relevant. One of the approaches to solve this problem is to build a fuzzy neural network and train it on the data characterizing the object under study. This approach implementation required fuzzy rules type choice, taking into account the processed data specificity. In addition, it required logical inference algorithm development on the rules of the selected type. The algorithm steps determine the number and functionality of layers in the fuzzy neural network structure. The fuzzy neural network training algorithm developed. After network training the formation fuzzyproduction rules system is carried out. Based on developed mathematical tool, a software package has been implemented. On its basis, studies to assess the classifying ability of the fuzzy rules being formed have been conducted using the data analysis example from the UCI Machine Learning Repository. The research results showed that the formed fuzzy rules classifying ability is not inferior in accuracy to other classification methods. In addition, the logic inference algorithm on fuzzy rules allows successful classification in the absence of a part of the initial data. In order to test, to solve the problem of assessing oil industry water lines state fuzzy rules were generated. Based on the 303 water lines initial data, the base of 342 fuzzy rules was formed. Their practical approbation has shown high efficiency in solving the problem.

The paper is devoted to the secondary use of spectrum in telecommunication networks. It is emphasized that one of the solutions to this problem is the use of cognitive radio technologies and dynamic spectrum access for the successful functioning of which a large amount of information is required, including the parameters of base stations and network subscribers. Storage and processing of information should be carried out using a radio environment map, which is a spatio-temporal database of all activity in the network and allows you to determine the frequencies available for use at a given time. The paper presents a two-level model for forming a map of the radio environment of a cellular communication system LTE, in which the local and global levels are highlighted, which is described by the following parameters: a set of frequencies, signal attenuation, signal propagation map, grid step, current time count. The key objects of the model are the base station and the subscriber unit. The main parameters of the base station include: name, identifier, cell coordinates, range number, radiation power, numbers of connected subscriber devices, dedicated resource blocks. For subscriber devices, the following parameters are used: name, identifier, location, current coordinates of the device cell, base station identifier, frequency range, numbers of resource blocks for communication with the station, radiation power, data transmission status, list of numbers of the nearest stations, schedules movement and communication sessions of devices. An algorithm for the implementation of the model is presented, taking into account the scenarios of movement and communication sessions of subscriber devices. A method for calculating a map of the radio environment at a point on a coordinate grid, taking into account losses during the propagation of radio signals from emitting devices, is presented. The software implementation of the model is performed using the MatLab package. The approaches are described that allow to increase the speed of its work. In the simulation, the choice of parameters was carried out taking into account the data of the existing communication systems and the economy of computing resources. The experimental results of the algorithm for the formation of a radio environment map are demonstrated, confirming the correctness of the developed model.

This work is devoted to the study of the problem of modeling and analyzing complex oscillatory modes, both regular and chaotic, in systems of interacting populations in the presence of random perturbations. As an initial conceptual deterministic model, a Volterra system of three differential equations is considered, which describes the dynamics of prey populations of two competing species and a predator. This model takes into account the following key biological factors: the natural increase in prey, their intraspecific and interspecific competition, the extinction of predators in the absence of prey, the rate of predation by predators, the growth of the predator population due to predation, and the intensity of intraspecific competition in the predator population. The growth rate of the second prey population is used as a bifurcation parameter. At a certain interval of variation of this parameter, the system demonstrates a wide variety of dynamic modes: equilibrium, oscillatory, and chaotic. An important feature of this model is multistability. In this paper, we focus on the study of the parametric zone of tristability, when a stable equilibrium and two limit cycles coexist in the system. Such birhythmicity in the presence of random perturbations generates new dynamic modes that have no analogues in the deterministic case. The aim of the paper is a detailed study of stochastic phenomena caused by random fluctuations in the growth rate of the second population of prey. As a mathematical model of such fluctuations, we consider white Gaussian noise. Using methods of direct numerical modeling of solutions of the corresponding system of stochastic differential equations, the following phenomena have been identified and described: unidirectional stochastic transitions from one cycle to another, trigger mode caused by transitions between cycles, noise-induced transitions from cycles to the equilibrium, corresponding to the extinction of the predator and the second prey population. The paper presents the results of the analysis of these phenomena using the Lyapunov exponents, and identifies the parametric conditions for transitions from order to chaos and from chaos to order. For the analytical study of such noise-induced multi-stage transitions, the technique of stochastic sensitivity functions and the method of confidence regions were applied. The paper shows how this mathematical apparatus allows predicting the intensity of noise, leading to qualitative transformations of the modes of stochastic population dynamics.

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.

This work provides a new approach for constructing high-precision routes based on data from transport detectors inside the SUMO traffic modeling package. Existing tools such as flowrouter and routeSampler have a number of disadvantages, such as the lack of interaction with the network in the process of building routes. Our rlRouter uses multi-agent reinforcement learning (MARL), where the agents are incoming lanes and the environment is the road network. By performing actions to launch vehicles, agents receive a reward for matching data from transport detectors. Parameter Sharing DQN with the LSTM backbone of the Q-function was used as an algorithm for multi-agent reinforcement learning.

Since the rlRouter is trained inside the SUMO simulation, it can restore routes better by taking into account the interaction of vehicles within the network with each other and with the network infrastructure. We have modeled diverse traffic situations on three different junctions in order to compare the performance of SUMO’s routers with the rlRouter. We used Mean Absoluter Error (MAE) as the measure of the deviation from both cumulative detectors and routes data. The rlRouter achieved the highest compliance with the data from the detectors. We also found that by maximizing the reward for matching detectors, the resulting routes also get closer to the real ones. Despite the fact that the routes recovered using rlRouter are superior to the routes obtained using SUMO tools, they do not fully correspond to the real ones, due to the natural limitations of induction-loop detectors. To achieve more plausible routes, it is necessary to equip junctions with other types of transport counters, for example, camera detectors.

We consider “predator – prey” model taking into account the competition of prey, predator for different from the prey resources, and their interaction described by the second type Holling trophic function. An analysis of the attractors is carried out depending on the coefficient of competition of predators. In the deterministic case, this model demonstrates the complex behavior associated with the local (Andronov –Hopf and saddlenode) and global (birth of a cycle from a separatrix loop) bifurcations. An important feature of this model is the disappearance of a stable cycle due to a saddle-node bifurcation. As a result of the presence of competition in both populations, parametric zones of mono- and bistability are observed. In parametric zones of bistability the system has either coexisting two equilibria or a cycle and equilibrium. Here, we investigate the geometrical arrangement of attractors and separatrices, which is the boundary of basins of attraction. Such a study is an important component in understanding of stochastic phenomena. In this model, the combination of the nonlinearity and random perturbations leads to the appearance of new phenomena with no analogues in the deterministic case, such as noise-induced transitions through the separatrix, stochastic excitability, and generation of mixed-mode oscillations. For the parametric study of these phenomena, we use the stochastic sensitivity function technique and the confidence domain method. In the bistability zones, we study the deformations of the equilibrium or oscillation regimes under stochastic perturbation. The geometric criterion for the occurrence of such qualitative changes is the intersection of confidence domains and the separatrix of the deterministic model. In the zone of monostability, we evolve the phenomena of explosive change in the size of population as well as extinction of one or both populations with minor changes in external conditions. With the help of the confidence domains method, we solve the problem of estimating the proximity of a stochastic population to dangerous boundaries, upon reaching which the coexistence of populations is destroyed and their extinction is observed.

Represented study considers numerical simulation of corium cooling driven by natural convection within a horizontal hemicylindrical cavity, boundaries of which are assumed isothermal. Corium is a melt of ceramic fuel of a nuclear reactor and oxides of construction materials.

Corium cooling is a process occurring during severe accident associated with core melt. According to invessel retention conception, the accident may be restrained and localized, if the corium is contained within the vessel, only if it is cooled externally. This conception has a clear advantage over the melt trap, it can be implemented at already operating nuclear power plants. Thereby proper numerical analysis of the corium cooling has become such a relevant area of studies.

In the research, we assume the corium is contained within a horizontal semitube. The corium initially has temperature of the walls. In spite of reactor shutdown, the corium still generates heat owing to radioactive decays, and the amount of heat released decreases with time accordingly to Way–Wigner formula. The system of equations in Boussinesq approximation including momentum equation, continuity equation and energy equation, describes the natural convection within the cavity. Convective flows are taken to be laminar and two-dimensional.

The boundary-value problem of mathematical physics is formulated using the non-dimensional nonprimitive variables «stream function – vorticity». The obtained differential equations are solved numerically using the finite difference method and locally one-dimensional Samarskii scheme for the equations of parabolic type.

As a result of the present research, we have obtained the time behavior of mean Nusselt number at top and bottom walls for Rayleigh number ranged from 10³ to 10⁶. These mentioned dependences have been analyzed for various dimensionless operation periods before the accident. Investigations have been performed using streamlines and isotherms as well as time dependences for convective flow and heat transfer rates.

The early embryos developing in vitro to the blastocyst stage have low implantation potential. In the current work the microinjection was used to evaluate the most viable blastocysts with high implantation ability on the basis of morphology changing. The recovery rate of the embryo volume allows assessing the functional activity of trophoblast cells that involved in implantation. The predictive model is suggested to forecast the development effectiveness of blastocysts in vitro. It’s shown the recovery rate of the blastocyst volume after microinjection is the most important feature of implantation potential of early embryos. The maximal recovery rate of blastocyst volume (35.7 % of initial volume per 1 h) correlates with the embryos ability to generate the colonies 72 h after microinjection. By the area under receiver operator curve (AUC) it was shown that combination of such characteristics as blastocyst stage (middle and late) and recovery rate after microinjection allowed to predict the blastocyst development.