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In this paper, a time-dependent natural convective heat transfer in a closed square cavity filled with non- Newtonian fluid was considered in the presence of an isothermal energy source located on the lower wall of the region under consideration. The vertical boundaries were kept at constant low temperature, while the horizontal walls were completely insulated. The behavior of a non-Newtonian fluid was described by the Ostwald de Ville power law. The process under study was described by transient partial differential equations using dimensionless non-primitive variables “stream function – vorticity – temperature”. This method allows excluding the pressure field from the number of unknown parameters, while the non-dimensionalization allows generalizing the obtained results to a variety of physical formulations. The considered mathematical model with the corresponding boundary conditions was solved on the basis of the finite difference method. The algebraic equation for the stream function was solved by the method of successive lower relaxation. Discrete analogs of the vorticity equation and energy equation were solved by the Thomas algorithm. The developed numerical algorithm was tested in detail on a class of model problems and good agreement with other authors was achieved. Also during the study, the mesh sensitivity analysis was performed that allows choosing the optimal mesh.

As a result of numerical simulation of unsteady natural convection of a non-Newtonian power-law fluid in a closed square cavity with a local isothermal energy source, the influence of governing parameters was analyzed including the impact of the Rayleigh number in the range 104–106, power-law index $n = 0.6–1.4$, and also the position of the heating element on the flow structure and heat transfer performance inside the cavity. The analysis was carried out on the basis of the obtained distributions of streamlines and isotherms in the cavity, as well as on the basis of the dependences of the average Nusselt number. As a result, it was established that pseudoplastic fluids $(n < 1)$ intensify heat removal from the heater surface. The increase in the Rayleigh number and the central location of the heating element also correspond to the effective cooling of the heat source.

This article studies a method of constructing a predictive neural network model of a time series based on determining the composition of input variables, constructing a training sample and training itself using the back propagation method. Traditional methods of constructing predictive models of the time series are: the autoregressive model, the moving average model or the autoregressive model — the moving average allows us to approximate the time series by a linear dependence of the current value of the output variable on a number of its previous values. Such a limitation as linearity of dependence leads to significant errors in forecasting.

Mining Technologies using neural network modeling make it possible to approximate the time series by a nonlinear dependence. Moreover, the process of constructing of a neural network model (determining the composition of input variables, the number of layers and the number of neurons in the layers, choosing the activation functions of neurons, determining the optimal values of the neuron link weights) allows us to obtain a predictive model in the form of an analytical nonlinear dependence.

The determination of the composition of input variables of neural network models is one of the key points in the construction of neural network models in various application areas that affect its adequacy. The composition of the input variables is traditionally selected from some physical considerations or by the selection method. In this work it is proposed to use the behavior of the autocorrelation and private autocorrelation functions for the task of determining the composition of the input variables of the predictive neural network model of the time series.

In this work is proposed a method for determining the composition of input variables of neural network models for stationary and non-stationary time series, based on the construction and analysis of autocorrelation functions. Based on the proposed method in the Python programming environment are developed an algorithm and a program, determining the composition of the input variables of the predictive neural network model — the perceptron, as well as building the model itself. The proposed method was experimentally tested using the example of constructing a predictive neural network model of a time series that reflects energy consumption in different regions of the United States, openly published by PJM Interconnection LLC (PJM) — a regional network organization in the United States. This time series is non-stationary and is characterized by the presence of both a trend and seasonality. Prediction of the next values of the time series based on previous values and the constructed neural network model showed high approximation accuracy, which proves the effectiveness of the proposed method.

The propagation of stable coherent entities of an electromagnetic field in nonlinear media with parameters varying in space can be described in the framework of iterations of nonlinear integral transformations. It is shown that for a set of geometries relevant to typical problems of nonlinear optics, numerical modeling by reducing to dynamical systems with discrete time and continuous spatial variables to iterates of local nonlinear Feigenbaum and Ikeda mappings and nonlocal diffusion-dispersion linear integral transforms is equivalent to partial differential equations of the Ginzburg–Landau type in a fairly wide range of parameters. Such nonlocal mappings, which are the products of matrix operators in the numerical implementation, turn out to be stable numerical- difference schemes, provide fast convergence and an adequate approximation of solutions. The realism of this approach allows one to take into account the effect of noise on nonlinear dynamics by superimposing a spatial noise specified in the form of a multimode random process at each iteration and selecting the stable wave configurations. The nonlinear wave formations described by this method include optical phase singularities, spatial solitons, and turbulent states with fast decay of correlations. The particular interest is in the periodic configurations of the electromagnetic field obtained by this numerical method that arise as a result of phase synchronization, such as optical lattices and self-organized vortex clusters.

Under increasing loading and at a constant temperature shape memory solids become deformed in an ideal elastic plastic way as other metals, and the maximum elastic strains are much less than the ultimate plastic ones. The shape is restored at the elevated temperature and low stress level. Phenomenologically, the «reverse» deformation is equivalent to the change in shape under active loading up to sign. Plastic deformation plays a leading role in a non-elastic process; thus, the mechanical behavior should be analyzed within the ideal rigid-plastic model with two loading surfaces. In this model two physical states of the material correspond to the loading surfaces: plastic flow under high stresses and melting at a relatively low temperature. The second section poses a problem of deformation of rigid-plastic bodies at the constant temperature in two forms: as a principle of virtual velocities with the von Mises yield condition and as a requirement of the minimum dissipative functionаl. The equivalence of the accepted definitions and the existence of the generalized solutions is proved for both principles. The third section studies the rigid-plastic model of the solid at the variable temperature with two loading surfaces. For the assumed model two optimal principles are defined that link the external loads and the displacement velocities of the solid points both under active loading and in the process of shape restoration under heating. The existence of generalized velocities is proved for the wide variety of 3D domains. The connection between the variational principles and the variable temperature is ensured by inclusion of the first and second principles of thermodynamics in the calculation model. It is essential that only the phenomenological description of the phenomenon is used in the proving process. The austenite-tomartensite transformations of alloys, which are often the key elements in explanations of the mechanical behavior of shape memory materials, are not used here. The fourth section includes the definition of the shape memory materials as solids with two loading surfaces and proves the existence of solutions within the accepted restrictions. The adequacy of the model and the experiments on deformation of shape memory materials is demonstrated. In the conclusion mathematical problems that could be interesting for future research are defined.

The paper is devoted to convex-concave saddle point problems where the objective is a sum of a large number of functions. Such problems attract considerable attention of the mathematical community due to the variety of applications in machine learning, including adversarial learning, adversarial attacks and robust reinforcement learning, to name a few. The individual functions in the sum usually represent losses related to examples from a data set. Additionally, the formulation admits a possibly nonsmooth composite term. Such terms often reflect regularization in machine learning problems. We assume that the dimension of one of the variable groups is relatively small (about a hundred or less), and the other one is large. This case arises, for example, when one considers the dual formulation for a minimization problem with a moderate number of constraints. The proposed approach is based on using Vaidya’s cutting plane method to minimize with respect to the outer block of variables. This optimization algorithm is especially effective when the dimension of the problem is not very large. An inexact oracle for Vaidya’s method is calculated via an approximate solution of the inner maximization problem, which is solved by the accelerated variance reduced algorithm Katyusha. Thus, we leverage the structure of the problem to achieve fast convergence. Separate complexity bounds for gradients of different components with respect to different variables are obtained in the study. The proposed approach is imposing very mild assumptions about the objective. In particular, neither strong convexity nor smoothness is required with respect to the low-dimensional variable group. The number of steps of the proposed algorithm as well as the arithmetic complexity of each step explicitly depend on the dimensionality of the outer variable, hence the assumption that it is relatively small.

A model of combustion of premixed mixture of gases with one global chemical reaction is considered, the model includes equations of the second order for temperature of mixture and concentrations of fuel and oxidizer, and the right-hand sides of these equations contain the reaction rate function. This function depends on five unknown parameters of the global reaction and serves as approximation to multistep reaction mechanism. The model is reduced, after replacement of variables, to one equation of the second order for temperature of mixture that transforms to a first-order equation for temperature derivative depending on temperature that contains a parameter of flame propagation velocity. Thus, for computing the parameter of burning velocity, one has to solve Dirichlet problem for first-order equation, and after that a model dependence of burning velocity on mixture equivalence ratio at specified reaction rate parameters will be obtained. Given the experimental data of dependence of burning velocity on mixture equivalence ratio, the problem of optimal selection of reaction rate parameters is stated, based on minimization of the mean square deviation of model values of burning velocity on experimental ones. The aim of our study is analysis of uniqueness of this problem solution. To this end, we apply computational experiment during which the problem of global search of optima is solved using multistart of gradient descent. The computational experiment clarifies that the inverse problem in this statement is underdetermined, and every time, when running gradient descent from a selected starting point, it converges to a new limit point. The structure of the set of limit points in the five-dimensional space is analyzed, and it is shown that this set can be described with three linear equations. Therefore, it might be incorrect to tabulate all five parameters of reaction rate based on just one match criterion between model and experimental data of flame propagation velocity. The conclusion of our study is that in order to tabulate reaction rate parameters correctly, it is necessary to specify the values of two of them, based on additional optimality criteria.

The study of the development of π-periodic perturbations of a converging spherical shock wave leading to cumulation limitation is performed. The study is based on 3D hydrodynamic calculations with the Carnahan – Starling equation of state for hard sphere fluid. The method of solving the Euler equations on moving (compressing) grids allows one to trace the evolution of the converging shock wave front with high accuracy in a wide range of its radius. The compression rate of the computational grid is adapted to the motion of the shock wave front, while the motion of the boundaries of the computational domain satisfy the condition of its supersonic velocity relative to the medium. This leads to the fact that the solution is determined only by the initial data at the grid compression stage. The second order TVD scheme is used to reconstruct the vector of conservative variables at the boundaries of the computational cells in combination with the Rusanov scheme for calculating the numerical vector of flows. The choice is due to a strong tendency for the manifestation of carbuncle-type numerical instability in the calculations, which is known for other classes of flows. In the three-dimensional case of the observed force, the carbuncle effect was obtained for the first time, which is explained by the specific nature of the flow: the concavity of the shock wave front in the direction of motion, the unlimited (in the symmetric case) growth of the Mach number, and the stationarity of the front on the computational grid. The applied numerical method made it possible to study the detailed flow pattern on the scale of cumulation termination, which is impossible within the framework of the Whitham method of geometric shock wave dynamics, which was previously used to calculate converging shock waves. The study showed that the limitation of cumulation is associated with the transition from the Mach interaction of converging shock wave segments to a regular one due to the progressive increase in the ratio of the azimuthal velocity at the shock wave front to the radial velocity with a decrease in its radius. It was found that this ratio is represented as a product of a limited oscillating function of the radius and a power function of the radius with an exponent depending on the initial packing density in the hard sphere model. It is shown that increasing the packing density parameter in the hard sphere model leads to a significant increase in the pressures achieved in a shock wave with broken symmetry. For the first time in the calculation, it is shown that at the scale of cumulation termination, the flow is accompanied by the formation of high-energy vortices, which involve the substance that has undergone the greatest shock-wave compression. Influencing heat and mass transfer in the region of greatest compression, this circumstance is important for current practical applications of converging shock waves for the purpose of initiating reactions (detonation, phase transitions, controlled thermonuclear fusion).

This study develops a previously proposed Method of Computer Analogy (MCA) based on formalization of digital computer operations. The paper discusses the position of the proposed approach among other well-known methods. It is emphasized that the primary objective is to derive analytical solutions, although in some cases they have to resort to semianalytical approximations. The paper focuses on constructing solutions for systems which, for certain parameter values, demonstrate the deterministic chaos behavior, namely Lorenz, Marioka – Shimitsu and R¨ossler systems. The paper also considers obtaining solution for Van der Pol equation (reduced to a nonlinear system). The aim of the study is to construct semi-analytical solutions represented as a segment of a power series in a step size of approximating difference scheme. To prevent overflow, authors formalize rank transfer operation. The authors apply a convergent difference scheme, referred to as the “guiding” scheme, to advance to the next step of the independent variable. The resulting approximation by a sum with only a few terms provides an approximation to the solution with any accuracy in accordance with the accuracy of the governing difference scheme. The senior digits in the resulting approximation exhibit probabilistic properties that can be modeled by known distributions, thereby enabling the derivation of analytical and semi-analytical approximations. The paper presents linear approximations that are the base for a complete approximations of solutions and provide important qualitative as well as some quantitative properties of solutions of considered systems. This work describes approximations of various orders, including those that do not guarantee convergence to the exact solution, but simplify the analysis of certain properties of nonlinear equations and systems. In particular, for the Van der Pol equation, authors demonstrate that its corresponding system has a cyclic solution and provide an estimate of its scale. A modification of the MCA that has features of the Monte Carlo method makes it possible to remove recurrent sequences and construct complete solutions in simple situations. The authors mention a promising approach for representing the solution using branched continued fractions.

Solving the problem of stationary stream distribution for an arbitrary volume-free hydrosystem with a free level can be reduced to determining the extremes of a multi-variable function. Rayleigh function expressed in terms of the hydraulic characteristics of the parts of the system in question is used as such a function. The same function is Lyapunov function when analyzing the stability of the determined stationary operational modes of a hydrosystem using the direct Lyapunov method.

Mathematical simulation on unsteady natural convection in a closed porous cylindrical cavity having finite thickness heat-conducting solid walls in conditions of convective heat exchange with an environment has been carried out. A boundary-value problem of mathematical physics formulated in dimensionless variables such as stream function and temperature on the basis of Darcy–Boussinesq model has been solved by finite difference method. Effect of a porous medium permeability 10^–5≤Da<∞, ratio between a solid wall thickness and the inner radius of a cylinder 0.1≤h/L≤0.3, a thermal conductivity ratio 1≤λ_1,2≤20 and a dimensionless time on both local distributions of isolines and isotherms and integral complexes reflecting an intensity of convective flow and heat transfer has been analyzed in detail.