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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.

The paper describes an analytical study of hydrodynamic processes taking place in the course of coolant flow through a fuel assembly of the core of a fast neutron sodium-cooled reactor. Within the framework of the study, a procedure and an analytical model were developed based on program complex FlowVision of computational fluid dynamics, which, using proved simplifications, permits to obtain a coefficient of rod lifting margin of a fuel assembly and to study hydrodynamic characteristics of processes taking place in the course of simulation of different initial events influencing motion of a reactor core fuel assembly.

For analytical justification a fuel assembly model was developed, which is equivalent by hydraulic resistance values and permits not to simulate explicitly a complicated full-scale fuel assembly design, thus, decreasing a number of computational cells in the model and, as a result, reducing computational and time resources.

Hydraulic parameters of the equivalent fuel assembly model in program complex FlowVision were analyzed in two stages. At the first stage, to determine the minimum rod lifting margin coefficient of a fuel assembly, steady-state analyses were performed, where various flowrate values were assigned at the model inlet and forces acting upon the assembly were analyzed. A series of dynamic mode analyses was performed at the second stage. Jump-like pressure increase being the initial event which could occur hypothetically in the fast neutron sodium cooled reactor plant was assigned in these modes. Hydrodynamic parameters and forces acting upon the fuel assembly were determined.

The results of the first stage of the analytical study proved the minimum coefficient of rod lifting margin of a fuel assembly of the fast neutron reactor justified in reactor plant design documentation. As a result of the second stage of the study, conclusions were made on impossibility for the fuel assembly to move at the initial event associated with jump-like pressure increase in the reactor pressure chamber.

We present a simulation methodology for the prediction of ЃgunexpectedЃh bottlenecks, i.e., the bottlenecks that occur suddenly and unexpectedly for drivers on a highway. Such unexpected bottlenecks can be either a moving bottleneck (MB) caused by a slow moving vehicle or a motionless bottleneck caused by a stopped vehicle (SV). Based on simulations of a stochastic microscopic traffic flow model in the framework of KernerЃfs three-phase traffic theory, we show that through the use of a small share of probe vehicles (FCD) randomly distributed in traffic flow the reliable prediction of ЃgunexpectedЃh bottlenecks is possible. We have found that the time dependence of the probability of MB and SV prediction as well as the accuracy of the estimation of MB and SV location depend considerably on sequences of phase transitions from free flow (F) to synchronized flow (S) (F→S transition) and back from synchronized flow to free flow (S→F transition) as well as on speed oscillations in synchronized flow at the bottleneck. In the simulation approach, the identification of F→S and S→F transitions at an unexpected bottleneck has been made in accordance with Kerner's three-phase traffic theory. The presented simulation methodology allows us both the prediction of the unexpected bottleneck that suddenly occurs on a highway and the distinguishing of the origin of the unexpected bottleneck, i.e., whether the unexpected bottleneck has occurred due to a MB or a SV.

The work is devoted to the development of a computational algorithm of the Cartesian grid method for studying the interaction of a shock wave with moving bodies with a piecewise linear boundary. The interest in such problems is connected with direct numerical simulation of two-phase media flows. The effect of the particle shape can be important in the problem of dust layer dispersion behind a passing shock wave. Experimental data on the coefficient of aerodynamic drag of non-spherical particles are practically absent.

Mathematical model is based on the two-dimensional Euler equations, which are solved in a region with varying boundaries. The defining system of equations is integrated using an explicit scheme and the Cartesian grid method. The computational algorithm at the time integration step includes: determining the step value, calculating the dynamics of the body movement (determining the force and moment acting on the body; determining the linear and angular velocities of the body; calculating the new coordinates of the body), calculating the gas parameters. At each time step, all cells are divided into two classes – external (inside the body or intersected by its boundaries) and internal (completely filled with gas). The solution of the Euler equations is constructed only in the internal ones. The main difficulty is the calculation of the numerical flux through the edges common to the internal and external cells intersected by the moving boundaries of the bodies. To calculate this flux, we use a two-wave approximation for solving the Riemann problem and the Steger-Warming scheme. A detailed description of the numerical algorithm is presented.

The efficiency of the algorithm is demonstrated on the problem of lifting a cylinder with a base in the form of a circle, ellipse and rectangle behind a passing shock wave. A circular cylinder test was considered in many papers devoted to the immersed boundary methods development. A qualitative and quantitative analysis of the trajectory of the cylinder center mass is carried out on the basis of comparison with the results of simulations presented in eight other works. For a cylinder with a base in the form of an ellipse and a rectangle, a satisfactory agreement was obtained on the dynamics of its movement and rotation in comparison with the available few literary sources. Grid convergence of the results is investigated for the rectangle. It is shown that the relative error of mass conservation law fulfillment decreases with a linear rate.

In this paper, we introduce a new version of Gauss–Newton method for solving a system of nonlinear equations based on ideas of the residual upper bound for a system of nonlinear equations and a quadratic regularization term. The introduced Gauss–Newton method in practice virtually forms the whole parameterized family of the methods solving systems of nonlinear equations and regression problems. The developed family of Gauss–Newton methods completely consists of iterative methods with generalization for cases of non-euclidean normed spaces, including special forms of Levenberg–Marquardt algorithms. The developed methods use the local model based on a parameterized proximal mapping allowing us to use an inexact oracle of «black–box» form with restrictions for the computational precision and computational complexity. We perform an efficiency analysis including global and local convergence for the developed family of methods with an arbitrary oracle in terms of iteration complexity, precision and complexity of both local model and oracle, problem dimensionality. We present global sublinear convergence rates for methods of the proposed family for solving a system of nonlinear equations, consisting of Lipschitz smooth functions. We prove local superlinear convergence under extra natural non-degeneracy assumptions for system of nonlinear functions. We prove both local and global linear convergence for a system of nonlinear equations under Polyak–Lojasiewicz condition for proposed Gauss– Newton methods. Besides theoretical justifications of methods we also consider practical implementation issues. In particular, for conducted experiments we present effective computational schemes for the exact oracle regarding to the dimensionality of a problem. The proposed family of methods unites several existing and frequent in practice Gauss–Newton method modifications, allowing us to construct a flexible and convenient method implementable using standard convex optimization and computational linear algebra techniques.

Modern methods of complex planning the execution of task packages in multistage systems are characterized by the presence of restrictions on the dimension of the problem being solved, the impossibility of guaranteed obtaining effective solutions for various values of its input parameters, as well as the impossibility of registration the conditions for the formation of sets from the result and the restriction on the interval duration of time of the system operating. The decomposition of the generalized function of the system into a set of hierarchically interconnected subfunctions is implemented to solve the problem of scheduling the execution of task packages with generating sets of results and the restriction on the interval duration of time for the functioning of the system. The use of decomposition made it possible to employ the hierarchical approach for planning the execution of task packages in multistage systems, which provides the determination of decisions by the composition of task groups at the first level of the hierarchy decisions by the composition of task packages groups executed during time intervals of limited duration at the second level and schedules for executing packages at the third level the hierarchy. In order to evaluate decisions on the composition of packages, the results of their execution, obtained during the specified time intervals, are distributed among the packages. The apparatus of the theory of hierarchical games is used to determine complex solutions. A model of a hierarchical game for making decisions by the compositions of packages, groups of packages and schedules of executing packages is built, which is a system of hierarchically interconnected criteria for optimizing decisions. The model registers the condition for the formation of sets from the results of the execution of task packages and restriction on duration of time intervals of its operating. The problem of determining the compositions of task packages and groups of task packages is NP-hard; therefore, its solution requires the use of approximate optimization methods. In order to optimize groups of task packages, the construction of a method for formulating initial solutions by their compositions has been implemented, which are further optimized. Moreover, a algorithm for distributing the results of executing task packages obtained during time intervals of limited duration by sets is formulated. The method of local solutions optimization by composition of packages groups, in accordance with which packages are excluded from groups, the results of which are not included in sets, and packages, that aren’t included in any group, is proposed. The software implementation of the considered method of complex optimization of the compositions of task packages, groups of task packages, and schedules for executing task packages from groups (including the implementation of the method for optimizing the compositions of groups of task packages) has been performed. With its use, studies of the features of the considered planning task are carried out. Conclusion are formulated concerning the dependence of the efficiency of scheduling the execution of task packages in multistage system under the introduced conditions from the input parameters of the problem. The use of the method of local optimization of the compositions of groups of task packages allows to increase the number of formed sets from the results of task execution in packages from groups by 60% in comparison with fixed groups (which do not imply optimization).

The article presents the results of a numerical study of the flow structure in a two-dimensional flat diffuser. A feature of diffusers is that they have a complex anisotropic turbulent flow, which occurs due to recirculation flows. The turbulent RANS models, which are based on the Boussinesq hypothesis, are not able to describe the flow in diffusers with sufficient accuracy. Because the Boussinesq hypothesis is based on isotropic turbulence. Therefore, to calculate anisotropic turbulent flows, models are used that do not use this hypothesis. One of such directions in turbulence modeling is the methods of Reynolds stresses. These methods are complex and require rather large computational resources. In this work, a relatively recently developed two-fluid turbulence model was used to study the flow in a flat diffuser. This model is developed on the basis of a two-fluid approach to the problem of turbulence. In contrast to the Reynolds approach, the two-fluid approach allows one to obtain a closed system of turbulence equations using the dynamics of two fluids. Consequently, if empirical equations are used in RANS models for closure, then in the two-fluid model the equations used are exact equations of dynamics. One of the main advantages of the two-fluid model is that it is capable of describing complex anisotropic turbulent flows. In this work, the obtained numerical results for the profiles of the longitudinal velocity, turbulent stresses in various sections of the channel, as well as the friction coefficient are compared with the known experimental data. To demonstrate the advantages of the used turbulence model, the numerical results of the Reynolds stress method EARSM are also presented. For the numerical implementation of the systems of equations of the two-fluid model, a non-stationary system of equations was used, the solution of which asymptotically approached the stationary solution. For this purpose, a finite-difference scheme was used, where the viscosity terms were approximated by the central difference implicitly, and for the convective terms, an explicit scheme against the flow of the second order of accuracy was used. The results are obtained for the Reynolds number Re = 20 000. It is shown that the two-fluid model, despite the use of a uniform computational grid without thickening near the walls, is capable of giving a more accurate solution than the rather complex Reynolds stress method with a high resolution of computational grids.

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.

A mathematical model is formulated for the coastal slope erosion of sandy channel, which occurs under the action of a passing flood wave. The moving boundaries of the computational domain — the bottom surface and the free surface of the hydrodynamic flow — are determined from the solution of auxiliary differential equations. A change in the hydrodynamic flow section area for a given law of change in the flow rate requires a change in time of the turbulent viscosity averaged over the section. The bottom surface movement is determined from the Exner equation solution together with the equation of the bottom material avalanche movement. The Exner equation is closed by the original analytical model of traction loads movement. The model takes into account transit, gravitational and pressure mechanisms of bottom material movement and does not contain phenomenological parameters.

Based on the finite element method, a discrete analogue of the formulated problem is obtained and an algorithm for its solution is proposed. An algorithm feature is control of the free surface movement influence of the flow and the flow rate on the process of determining the flow turbulent viscosity. Numerical calculations have been carried out, demonstrating qualitative and quantitative influence of these features on the determining process of the flow turbulent viscosity and the channel bank slope erosion.

Data comparison on bank deformations obtained as a result of numerical calculations with known flume experimental data showed their agreement.