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An algorithm is proposed to identify parameters of a 2D vortex structure used on information about the flow velocity at a finite (small) set of reference points. The approach is based on using a set of point vortices as a model system and minimizing a functional that compares the model and known sets of velocity vectors in the space of model parameters. For numerical implementation, the method of gradient descent with step size control, approximation of derivatives by finite differences, and the analytical expression of the velocity field induced by the point vortex model are used. An experimental analysis of the operation of the algorithm on test flows is carried out: one and a system of several point vortices, a Rankine vortex, and a Lamb dipole. According to the velocity fields of test flows, the velocity vectors utilized for identification were arranged in a randomly distributed set of reference points (from 3 to 200 pieces). Using the computations, it was determined that: the algorithm converges to the minimum from a wide range of initial approximations; the algorithm converges in all cases when the reference points are located in areas where the streamlines of the test and model systems are topologically equivalent; if the streamlines of the systems are not topologically equivalent, then the percentage of successful calculations decreases, but convergence can also take place; when the method converges, the coordinates of the vortices of the model system are close to the centers of the vortices of the test configurations, and in many cases, the values of their circulations also; con-vergence depends more on location than on the number of vectors used for identification. The results of the study allow us to recommend the proposed algorithm for identifying 2D vortex structures whose streamlines are topologically close to systems of point vortices.

The work investigates the process of self-consistent relaxation of the region of disturbances created in a rarefied binary low-temperature plasma by a stationary charged ball or cylinder with an absorbing surface. A feature of such problems is their self-consistent kinetic nature, in which it is impossible to separate the processes of transfer in phase space and the formation of an electromagnetic field. A mathematical model is presented that makes it possible to describe and analyze the state of the gas, electric and thermal fields in the vicinity of the body. The multidimensionality of the kinetic formulation creates certain problems in the numerical solution, therefore a curvilinear system of nonholonomic coordinates was selected for the problem, which minimizes its phase space, which contributes to increasing the efficiency of numerical methods. For such coordinates, the form of the Vlasov kinetic equation has been justified and analyzed. To solve it, a variant of the large particle method with a constant form factor was used. The calculations used a moving grid that tracks the displacement of the distribution function carrier in the phase space, which further reduced the volume of the controlled region of the phase space. Key details of the model and numerical method are revealed. The model and the method are implemented as code in the Matlab language. Using the example of solving a problem for a ball, the presence of significant disequilibrium and anisotropy in the particle velocity distribution in the disturbed zone is shown. Based on the calculation results, pictures of the evolution of the structure of the particle distribution function, profiles of the main macroscopic characteristics of the gas — concentration, current, temperature and heat flow, and characteristics of the electric field in the disturbed region are presented. The mechanism of heating of attracted particles in the disturbed zone is established and some important features of the process of formation of heat flow are shown. The results obtained are well explainable from a physical point of view, which confirms the adequacy of the model and the correct operation of the software tool. The creation and testing of a basis for the development in the future of tools for solving more complex problems of modeling the behavior of ionized gases near charged bodies is noted.

The work will be useful to specialists in the field of mathematical modeling, heat and mass transfer processes, lowtemperature plasma physics, postgraduate students and senior students specializing in the indicated areas.

Currently, a significant development has been observed in the direction of distributed computing theory, where computational tasks are solved collectively by resource-constrained devices. In practice, this scenario is implemented when processing data in Internet of Things systems, with the aim of reducing system latency and network infrastructure load, as data is processed on edge network computing devices. However, the rapid growth and widespread adoption of IoT systems raise questions about the need to develop methods for reducing the resource intensity of computations. The resource constraints of computing devices pose the following issues regarding the distribution of computational resources: firstly, the necessity to account for the transit cost between different devices solving various tasks; secondly, the necessity to consider the resource cost associated directly with the process of distributing computational resources, which is particularly relevant for groups of autonomous devices such as drones or robots. An analysis of modern publications available in open access demonstrated the absence of proposed models or methods for distributing computational resources that would simultaneously take into account all these factors, making the creation of a new mathematical model for organizing distributed computing in IoT systems and its solution methods topical. This article proposes a novel mathematical model for distributing computational resources along with heuristic optimization methods, providing an integrated approach to implementing distributed computing in IoT systems. A scenario is considered where there exists a leader device within a group that makes decisions concerning the allocation of computational resources, including its own, for distributed task resolution involving information exchanges. It is also assumed that no prior knowledge exists regarding which device will assume the role of leader or the migration paths of computational tasks across devices. Experimental results have shown the effectiveness of using the proposed models and heuristics: achieving up to a 52% reduction in resource costs for solving computational problems while accounting for data transit costs, saving up to 73% of resources through supplementary criteria optimizing task distribution based on minimizing fragment migrations and distances, and decreasing the resource cost of resolving the computational resource distribution problem by up to 28 times with reductions in distribution quality up to 10%.

In this paper we consider the controlled motion of a helical body with three blades in an ideal fluid, which is executed by rotating three internal rotors. We set the problem of selecting control actions, which ensure the motion of the body near the predetermined trajectory. To determine controls that guarantee motion near the given curve, we propose methods based on the application of hybrid genetic algorithms (genetic algorithms with real encoding and with additional learning of the leader of the population by a gradient method) and artificial neural networks. The correctness of the operation of the proposed numerical methods is estimated using previously obtained differential equations, which define the law of changing the control actions for the predetermined trajectory.

In the approach based on hybrid genetic algorithms, the initial problem of minimizing the integral functional reduces to minimizing the function of many variables. The given time interval is broken up into small elements, on each of which the control actions are approximated by Lagrangian polynomials of order 2 and 3. When appropriately adjusted, the hybrid genetic algorithms reproduce a solution close to exact. However, the cost of calculation of 1 second of the physical process is about 300 seconds of processor time.

To increase the speed of calculation of control actions, we propose an algorithm based on artificial neural networks. As the input signal the neural network takes the components of the required displacement vector. The node values of the Lagrangian polynomials which approximately describe the control actions return as output signals . The neural network is taught by the well-known back-propagation method. The learning sample is generated using the approach based on hybrid genetic algorithms. The calculation of 1 second of the physical process by means of the neural network requires about 0.004 seconds of processor time, that is, 6 orders faster than the hybrid genetic algorithm. The control calculated by means of the artificial neural network differs from exact control. However, in spite of this difference, it ensures that the predetermined trajectory is followed exactly.

In the paper, it is represented a linearization method for the stress-strain curves of nonlinearly deformable beams and circular plates in order to generalize the pure bending vibration equations. It is considered composite, on average isotropic prismatic beams of a constant rectangular cross-section and circular plates of a constant thickness made of nonlinearly elastic materials. The technique consists in determining the approximate Young’s moduli from the initial stress-strain state of beam and plate subjected to the action of the bending moment.

The paper proposes two criteria for linearization: the equality of the specific potential energy of deformation and the minimization of the standard deviation in the state equation approximation. The method allows obtaining in the closed form the estimated value of the natural frequencies of layered and structurally heterogeneous, on average isotropic nonlinearly elastic beams and circular plates. This makes it possible to significantly reduce the resources in the vibration analysis and modeling of these structural elements. In addition, the paper shows that the proposed linearization criteria allow to estimate the natural frequencies with the same accuracy.

Since in the general case even isotropic materials exhibit different resistance to tension and compression, it is considered the piecewise-linear Prandtl’s diagrams with proportionality limits and tangential Young’s moduli that differ under tension and compression as the stress-strain curves of the composite material components. As parameters of the stress-strain curve, it is considered the effective Voigt’s characteristics (under the hypothesis of strain homogeneity) for a longitudinally layered material structure; the effective Reuss’ characteristics (under the hypothesis of strain homogeneity) for a transversely layered beam and an axially laminated plate. In addition, the effective Young’s moduli and the proportionality limits, obtained by the author’s homogenization method, are given for a structurally heterogeneous, on average isotropic material. As an example, it is calculated the natural frequencies of two-phase beams depending on the component concentrations.

We consider one of the problems of transport modelling — searching the equilibrium distribution of traffic flows in the network. We use the classic Beckman’s model to describe time costs and flow distribution in the network represented by directed graph. Meanwhile agents’ behavior is not completely rational, what is described by the introduction of Markov logit dynamics: any driver selects a route randomly according to the Gibbs’ distribution taking into account current time costs on the edges of the graph. Thus, the problem is reduced to searching of the stationary distribution for this dynamics which is a stochastic Nash – Wardrope equilibrium in the corresponding population congestion game in the transport network. Since the game is potential, this problem is equivalent to the problem of minimization of some functional over flows distribution. The stochasticity is reflected in the appearance of the entropy regularization, in contrast to non-stochastic case. The dual problem is constructed to obtain a solution of the optimization problem. The universal primal-dual gradient method is applied. A major specificity of this method lies in an adaptive adjustment to the local smoothness of the problem, what is most important in case of the complex structure of the objective function and an inability to obtain a prior smoothness bound with acceptable accuracy. Such a situation occurs in the considered problem since the properties of the function strongly depend on the transport graph, on which we do not impose strong restrictions. The article describes the algorithm including the numerical differentiation for calculation of the objective function value and gradient. In addition, the paper represents a theoretical estimate of time complexity of the algorithm and the results of numerical experiments conducted on a small American town.

Image denoising is one of the fundamental problems in digital image processing. This problem usually refers to the reconstruction of an image from an observed image degraded by noise. There are many factors that cause this degradation such as transceiver equipment, or environmental influences, etc. In order to obtain higher quality images, many methods have been proposed for image denoising problem. Most image denoising method are based on total variation (TV) regularization to develop efficient algorithms for solving the related optimization problem. TV-based models have become a standard technique in image restoration with the ability to preserve image sharpness.

In this paper, we focus on Poisson noise usually appearing in photon-counting devices. We propose an effective regularization model based on combination of first-order and fractional-order total variation for image reconstruction corrupted by Poisson noise. The proposed model allows us to eliminate noise while edge preserving. An efficient alternating minimization algorithm is employed to solve the optimization problem. Finally, provided numerical results show that our proposed model can preserve more details and get higher image visual quality than recent state-of-the-art methods.

The work is devoted to the study of the theoretical value of destructive influence on normal tissues of an organism by infrared radiation that goes beyond the treated pathological focus. This situation is possible if the direct laser radiation on the tissues is extremely long-acting. The solution to this problem can be the uniform distribution of heat inside the volume through indirect heating of the liquid, which contributes to minimal damage to the perifocal structures. A non-stationary thermophysical model of the process of heat propagation in biological tissues is presented, allowing to carry out studies of energy transfer from internal liquid contents of Baker's cyst heated by infrared laser radiation of a given specific power through a certain thickness of its wall to surrounding biological tissues. Calculation of the spacetime temperature distribution in the cyst wall and surrounding fat tissue is carried out by the finite-difference method. The time of effective exposure to temperature on the entire thickness of the cyst wall was estimated to be 55 ° C on its outer surface. The safety procedure ensures the exposure duration of this value is not more than 10 seconds.

As a result of the calculations carried out, it is established that there are several operating modes of a surgical laser that meet all the safety requirements with a simultaneous effective procedure. Local one-sided hyperthermia of the synovial membrane and subsequent coagulation of the entire wall thickness due to heat transfer contributes to the elimination of the cavity neoplasm of the popliteal region. With a thickness of 3 mm, the heating mode is satisfactory, under which the exposure time lasts about 200 seconds, and the specific power of the laser radiation in the internal medium of the liquid contents of the Baker cyst is approximately 1.

In this paper, we develop a new first-order method for composite nonconvex minimization problems with simple constraints and inexact oracle. The objective function is given as a sum of «hard», possibly nonconvex part, and «simple» convex part. Informally speaking, oracle inexactness means that, for the «hard» part, at any point we can approximately calculate the value of the function and construct a quadratic function, which approximately bounds this function from above. We give several examples of such inexactness: smooth nonconvex functions with inexact H¨older-continuous gradient, functions given by the auxiliary uniformly concave maximization problem, which can be solved only approximately. For the introduced class of problems, we propose a gradient-type method, which allows one to use a different proximal setup to adapt to the geometry of the feasible set, adaptively chooses controlled oracle error, allows for inexact proximal mapping. We provide a convergence rate for our method in terms of the norm of generalized gradient mapping and show that, in the case of an inexact Hölder-continuous gradient, our method is universal with respect to Hölder parameters of the problem. Finally, in a particular case, we show that the small value of the norm of generalized gradient mapping at a point means that a necessary condition of local minimum approximately holds at that point.

Analysis and practical aspects of implementing developed in the control theory robust control methods in studying economic systems is carried out. The main emphasis is placed on studying results obtained for dynamical systems with structured uncertainty. Practical aspects of implementing such results in control of economic systems on the basis of dynamical models with uncertain parameters and perturbations (stabilization of price on the oil market and inflation in macroeconomic systems) are discussed. With the help of specially constructed aggregate model of oil price dynamics studied the problem of finding control which provides minimal deviation of price from desired levels over middle range period. The second real problem considered in the article consists in determination of stabilizing control providing minimal deviation of inflation from desired levels (on the basis of constructed aggregate macroeconomic model of the USA over middle range period).

Upper levels of parameters uncertainty and control laws guaranteeing stabilizability of the real considered economic systems have been found using the robust method of control with structured uncertainty. At the same time we have come to the conclusion that received estimates of parameters uncertainty upper levels are conservative. Monte-Carlo experiments carried out for the article made it possible to analyze dynamics of oil price and inflation under received limit levels of models parameters uncertainty and under implementing found robust control laws for the worst and the best scenarios. Results of these experiments show that received robust control laws may be successfully used under less stringent uncertainty constraints than it is guaranteed by sufficient conditions of stabilization.