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The article contains results of modeling a meteor entering dense atmosphere layers using Galerkin’s method and smoother particle hydrodynamics. Numerical simulations were run using experimental data gathered for the Chelyabinsk meteor while varying the meteor material characteristics and its orientation when entering the atmosphere.

We study the influence of fishery on a structured fish population under random changes of habitat conditions. The population parameters correspond to dominant pelagic fish species of Far-Eastern seas of the northwestern part of the Pacific Ocean (pollack, herring, sardine). Similar species inhabit various parts of the Word Ocean. The species body size distribution was chosen as a main population feature. This characteristic is easy to measure and adequately defines main specimen qualities such as age, maturity and other morphological and physiological peculiarities. Environmental fluctuations have a great influence on the individuals in early stages of development and have little influence on the vital activity of mature individuals. The fishery revenue was chosen as an optimality criterion. The main control characteristic is fishing effort. We have chosen quadratic dependence of fishing revenue on the fishing effort according to accepted economic ideas stating that the expenses grow with the production volume. The model study shows that the population structure ensures the increased population stability. The growth and drop out of the individuals’ due to natural mortality smoothens the oscillations of population density arising from the strong influence of the fluctuations of environment on young individuals. The smoothing part is played by diffusion component of the growth processes. The fishery in its turn smooths the fluctuations (including random fluctuations) of the environment and has a substantial impact upon the abundance of fry and the subsequent population dynamics. The optimal time-dependent fishing effort strategy was compared to stationary fishing effort strategy. It is shown that in the case of quickly changing habitat conditions and stochastic dynamics of population replenishment there exists a stationary fishing effort having approximately the same efficiency as an optimal time-dependent fishing effort. This means that a constant or weakly varying fishing effort can be very efficient strategy in terms of revenue.

Solution of Dynamic Light Scattering problem makes it possible to determine particle size distribution (PSD) from the spectrum of the intensity of scattered light. As a result of experiment, an intensity curve is obtained. The experimentally obtained spectrum of intensity is compared with the theoretically expected spectrum, which is the Lorentzian line. The main task is to determine on the basis of these data the relative concentrations of particles of each class presented in the solution. The article presents a method for constructing and using a neural network trained on synthetic data to determine PSD in a solution in the range of 1–500 nm. The neural network has a fully connected layer of 60 neurons with the RELU activation function at the output, a layer of 45 neurons and the same activation function, a dropout layer and 2 layers with 15 and 1 neurons (network output). The article describes how the network has been trained and tested on synthetic and experimental data. On the synthetic data, the standard deviation metric (rmse) gave a value of 1.3157 nm. Experimental data were obtained for particle sizes of 200 nm, 400 nm and a solution with representatives of both sizes. The results of the neural network and the classical linear methods are compared. The disadvantages of the classical methods are that it is difficult to determine the degree of regularization: too much regularization leads to the particle size distribution curves are much smoothed out, and weak regularization gives oscillating curves and low reliability of the results. The paper shows that the neural network gives a good prediction for particles with a large size. For small sizes, the prediction is worse, but the error quickly decreases as the particle size increases.

We study the computational properties of a parametric class of finite-volume schemes with customizable dissipative properties with splitting by physical processes into Lagrangian, Eulerian, and the final stages (the hybrid large-particle method). The method has a second-order approximation in space and time on smooth solutions. The regularization of a numerical solution at the Lagrangian stage is performed by nonlinear correction of artificial viscosity. Regardless of the grid resolution, the artificial viscosity value tends to zero outside the zone of discontinuities and extremes in the solution. At Eulerian and final stages, primitive variables (density, velocity, and total energy) are first reconstructed by an additive combination of upwind and central approximations weighted by a flux limiter. Then numerical divergent fluxes are formed from them. In this case, discrete analogs of conservation laws are performed.

The analysis of dissipative properties of the method using known viscosity and flow limiters, as well as their linear combination, is performed. The resolution of the scheme and the quality of numerical solutions are demonstrated by examples of two-dimensional benchmarks: a gas flow around the step with Mach numbers 3, 10 and 20, the double Mach reflection of a strong shock wave, and the implosion problem. The influence of the scheme viscosity of the method on the numerical reproduction of a gases interface instability is studied. It is found that a decrease of the dissipation level in the implosion problem leads to the symmetric solution destruction and formation of a chaotic instability on the contact surface.

Numerical solutions are compared with the results of other authors obtained using higher-order approximation schemes: CABARET, HLLC (Harten Lax van Leer Contact), CFLFh (CFLF hybrid scheme), JT (centered scheme with limiter by Jiang and Tadmor), PPM (Piecewise Parabolic Method), WENO5 (weighted essentially non-oscillatory scheme), RKGD (Runge –Kutta Discontinuous Galerkin), hybrid weighted nonlinear schemes CCSSR-HW4 and CCSSR-HW6. The advantages of the hybrid large-particle method include extended possibilities for solving hyperbolic and mixed types of problems, a good ratio of dissipative and dispersive properties, a combination of algorithmic simplicity and high resolution in problems with complex shock-wave structure, both instability and vortex formation at interfaces.

Authors describe a two-stage traffic assignment model. It contains of two blocks. The first block consists of a model for calculating a correspondence (demand) matrix, whereas the second block is a traffic assignment model. The first model calculates a matrix of correspondences using a matrix of transport costs (it characterizes the required volumes of movement from one area to another, it is time in this case). To solve this problem, authors propose to use one of the most popular methods of calculating the correspondence matrix in urban studies — the entropy model. The second model describes exactly how the needs for displacement specified by the correspondence matrix are distributed along the possible paths. Knowing the ways of the flows distribution along the paths, it is possible to calculate the cost matrix. Equilibrium in a two-stage model is a fixed point in the sequence of these two models. In practice the problem of finding a fixed point can be solved by the fixed-point iteration method. Unfortunately, at the moment the issue of convergence and estimations of the convergence rate for this method has not been studied quite thoroughly. In addition, the numerical implementation of the algorithm results in many problems. In particular, if the starting point is incorrect, situations may arise where the algorithm requires extremely large numbers to be computed and exceeds the available memory even on the most modern computers. Therefore the article proposes a method for reducing the problem of finding the equilibrium to the problem of the convex non-smooth optimization. Also a numerical method for solving the obtained optimization problem is proposed. Numerical experiments were carried out for both methods of solving the problem. The authors used data for Vladivostok (for this city information from various sources was processed and collected in a new dataset) and two smaller cities in the USA. It was not possible to achieve convergence by the method of fixed-point iteration, whereas the second model for the same dataset demonstrated convergence rate $k^{-1.67}$.

The work is devoted to the construction of efficient and applicable to real tasks first-order methods of convex optimization, that is, using only values of the target function and its derivatives. Construction uses OGMG, fast gradient method which is optimal by complexity, but requires to know the Lipschitz constant for gradient and the strong convexity constant to determine the number of steps and step length. This requirement makes practical usage very hard. An adaptive on the constant for strong convexity algorithm ACGM is proposed, based on restarts of the OGM-G with update of the strong convexity constant estimate, and an adaptive on the Lipschitz constant for gradient ALGM, in which the use of OGM-G restarts is supplemented by the selection of the Lipschitz constant with verification of the smoothness conditions used in the universal gradient descent method. This eliminates the disadvantages of the original method associated with the need to know these constants, which makes practical usage possible. Optimality of estimates for the complexity of the constructed algorithms is proved. To verify the results obtained, experiments on model functions and real tasks from machine learning are carried out.

The problem discrete statement by the smoothed particle hydrodynamics method (SPH) include a discretization constants parameters set. Of them particular note is the model sound speed $c_0$, which relates the SPH-particle instantaneous density to the resulting pressure through the equation of state.

The paper describes an approach to the exact determination of the model sound speed required value. It is on the analysis based, how SPH-particle density changes with their relative shift. An example of the continuous medium motion taken the plane shear flow problem; the analysis object is the relative compaction function $\varepsilon_\rho$ in the SPH-particle. For various smoothing kernels was research the functions of $\varepsilon_\rho$, that allowed the pulsating nature of the pressures occurrence in particles to establish. Also the neighbors uniform distribution in the smoothing domain was determined, at which shaping the maximum of compaction in the particle.

Through comparison the function $\varepsilon_\rho$ with the SPH-approximation of motion equation is defined associate the discretization parameter $c_0$ with the smoothing kernel shape and other problem parameters. As a result, an equation is formulated that the necessary and sufficient model sound speed value provides finding. For such equation the expressions of root $c_0$ are given for three different smoothing kernels, that simplified from polynomials to numerical coefficients for the plane shear flow problem parameters.

In this work, we propose a method of the numerical solution of the magnetostatic problem for domains with boundaries containing corners. With the help of this numerical method, the magnetic systems of rectangular configuration were simulated with high accuracy. In particular, the calculations of some modifications of the magnetic system SP-40 used in the NIS JINR experimental installation, are presented. The basic feature of such a magnet is a rectangular aperture, hence, the area in which the boundary-value problem is solved, has a smooth border everywhere, except for a finite number of angular points in the vicinity of which the border is formed by crossing two smooth curves. In such cases the solution to the problem or derivatives of the solution can have a special feature. A behavior of the magnetic field in the vicinity of an angular point is investigated, and the configuration of the magnet was chosen numerically. The width of the area of homogeneity of the magnetic field increased from 0.5 m up to 1.0 m, i. e. twice.

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.

This work presents the model of heat wall functions FlowVision (WFFV), which allows simulation of nonisothermal flows of fluid and gas near solid surfaces on relatively coarse grids with use of turbulence models. The work follows the research on the development of wall functions applicable in wide range of the values of quantity y+. Model WFFV assumes smooth profiles of the tangential component of velocity, turbulent viscosity, temperature, and turbulent heat conductivity near a solid surface. Possibility of using a simple algebraic model for calculation of variable turbulent Prandtl number is investigated in this study (the turbulent Prandtl number enters model WFFV as parameter). The results are satisfactory. The details of implementation of model WFFV in the FlowVision software are explained. In particular, the boundary condition for the energy equation used in high-Reynolds number calculations of non-isothermal flows is considered. The boundary condition is deduced for the energy equation written via thermodynamic enthalpy and via full enthalpy. The capability of the model is demonstrated on two test problems: flow of incompressible fluid past a plate and supersonic flow of gas past a plate (M = 3).

Analysis of literature shows that there exists essential ambiguity in experimental data and, as a consequence, in empirical correlations for the Stanton number (that being a dimensionless heat flux). The calculations suggest that the default values of the model parameters, automatically specified in the program, allow calculations of heat fluxes at extended solid surfaces with engineering accuracy. At the same time, it is obvious that one cannot invent universal wall functions. For this reason, the controls of model WFFV are made accessible from the FlowVision interface. When it is necessary, a user can tune the model for simulation of the required type of flow.

The proposed model of wall functions is compatible with all the turbulence models implemented in the FlowVision software: the algebraic model of Smagorinsky, the Spalart-Allmaras model, the SST $k-\omega$ model, the standard $k-\varepsilon$ model, the $k-\varepsilon$ model of Abe, Kondoh, Nagano, the quadratic $k-\varepsilon$ model, and $k-\varepsilon$ model FlowVision.