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The paper contains numerical simulation of nonisothermal nonlinear flow in a porous medium. Twodimensional unsteady problem of heavy oil, water and steam flow is considered. Oil phase consists of two pseudocomponents: light and heavy fractions, which like the water component, can vaporize. Oil exhibits viscoplastic rheology, its filtration does not obey Darcy's classical linear law. Simulation considers not only the dependence of fluids density and viscosity on temperature, but also improvement of oil rheological properties with temperature increasing.

To solve this problem numerically we use streamline method with splitting by physical processes, which consists in separating the convective heat transfer directed along filtration from thermal conductivity and gravitation. The article proposes a new approach to streamline methods application, which allows correctly simulate nonlinear flow problems with temperature-dependent rheology. The core of this algorithm is to consider the integration process as a set of quasi-equilibrium states that are results of solving system on a global grid. Between these states system solved on a streamline grid. Usage of the streamline method allows not only to accelerate calculations, but also to obtain a physically reliable solution, since integration takes place on a grid that coincides with the fluid flow direction.

In addition to the streamline method, the paper presents an algorithm for nonsmooth coefficients accounting, which arise during simulation of viscoplastic oil flow. Applying this algorithm allows keeping sufficiently large time steps and does not change the physical structure of the solution.

Obtained results are compared with known analytical solutions, as well as with the results of commercial package simulation. The analysis of convergence tests on the number of streamlines, as well as on different streamlines grids, justifies the applicability of the proposed algorithm. In addition, the reduction of calculation time in comparison with traditional methods demonstrates practical significance of the approach.

Rugose spiraling whitefly (RSW) is one of the major pests which affects the coconut trees. It feeds on the tree by sucking up the water content as well as the essential nutrients from leaves. It also forms sooty mold in leaves due to which the process of photosynthesis is inhibited. Biocontrol of pest is harmless for trees and crops. The experimental results in literature reveal that Pseudomallada astur is a potential predator for this pest. We investigate the dynamics of predator, Pseudomallada astur’s interaction with rugose spiralling whitefly, Aleurodicus rugioperculatus in coconut trees using a mathematical model. In this system of ordinary differential equation, the pest-predator interaction is modeled using Holling type III functional response. The parametric values are calculated from the experimental results and are tabulated. An approximate analytical solution for the system has been derived. The homotopy analysis method proves to be a suitable method for creating solutions that are valid even for moderate to large parameter values, hence we employ the same to solve this nonlinear model. The $\hbar$-curves, which give the admissible region of $\hbar$, are provided to validate the region of convergence. We have derived the approximate solution at fifth order and stopped at this order since we obtain a more approximate solution in this iteration. Numerical simulation is obtained through MATLAB. The analytical results are compared with numerical simulation and are found to be in good agreement. The biological interpretation of figures implies that the use of a predator reduces the whitefly’s growth to a greater extent.

In this paper, we consider two variants of the concept of sharp minimum for mathematical programming problems with quasiconvex objective function and inequality constraints. It investigated the problem of describing a variant of a simple subgradient method with switching along productive and non-productive steps, for which, on a class of problems with Lipschitz functions, it would be possible to guarantee convergence with the rate of geometric progression to the set of exact solutions or its vicinity. It is important that to implement the proposed method there is no need to know the sharp minimum parameter, which is usually difficult to estimate in practice. To overcome this problem, the authors propose to use a step adjustment procedure similar to that previously proposed by B. T. Polyak. However, in this case, in comparison with the class of problems without constraints, it arises the problem of knowing the exact minimal value of the objective function. The paper describes the conditions for the inexactness of this information, which make it possible to preserve convergence with the rate of geometric progression in the vicinity of the set of minimum points of the problem. Two analogs of the concept of a sharp minimum for problems with inequality constraints are considered. In the first one, the problem of approximation to the exact solution arises only to a pre-selected level of accuracy, for this, it is considered the case when the minimal value of the objective function is unknown; instead, it is given some approximation of this value. We describe conditions on the inexact minimal value of the objective function, under which convergence to the vicinity of the desired set of points with a rate of geometric progression is still preserved. The second considered variant of the sharp minimum does not depend on the desired accuracy of the problem. For this, we propose a slightly different way of checking whether the step is productive, which allows us to guarantee the convergence of the method to the exact solution with the rate of geometric progression in the case of exact information. Convergence estimates are proved under conditions of weak convexity of the constraints and some restrictions on the choice of the initial point, and a corollary is formulated for the convex case when the need for an additional assumption on the choice of the initial point disappears. For both approaches, it has been proven that the distance from the current point to the set of solutions decreases with increasing number of iterations. This, in particular, makes it possible to limit the requirements for the properties of the used functions (Lipschitz-continuous, sharp minimum) only for a bounded set. Some computational experiments are performed, including for the truss topology design problem.

In this paper, the turbulent Taylor – Couette flow is investigated using two-dimensional modeling based on the averaged Navier – Stokes (RANS) equations and a new two-fluid approach to turbulence at Reynolds numbers in the range from 1000 to 8000. The flow due to a rotating internal and stationary external cylinders. The case of ratio of cylinder diameters 1:2 is considered. It is known that the emerging circular flow is characterized by anisotropic turbulence and mathematical modeling of such flows is a difficult task. To describe such flows, either direct modeling methods are used, which require large computational costs, or rather laborious Reynolds stress methods, or linear RANS models with special corrections for rotation, which are able to describe anisotropic turbulence. In order to compare different approaches to turbulence modeling, the paper presents the numerical results of linear RANS models SARC, SST-RC, Reynolds stress method SSG/LRR-RSM-w2012, DNS direct turbulence modeling, as well as a new two-fluid model. It is shown that the recently developed twofluid model adequately describes the considered flow. In addition, the two-fluid model is easy to implement numerically and has good convergence.

The aim of the paper is to obtain a numerical solution for linear and higher-order delay differential equations (DDEs) using the coded differential transform method (CDTM). The CDTM is developed and applied to delay problems to show the efficiency of the proposed method. The coded differential transform method is a combination of the differential transform method and Mathematica software. We construct recursive relations for a few delay problems, which results in simultaneous equations, and solve them to obtain various series solution terms using the coded differential transform method. The numerical solution obtained by CDTM is compared with an exact solution. Numerical results and error analysis are presented for delay differential equations to show that the proposed method is suitable for solving delay differential equations. It is established that the delay differential equations under discussion are solvable in a specific domain. The error between the CDTM solution and the exact solution becomes very small if more terms are included in the series solution. The coded differential transform method reduces complex calculations, avoids discretization, linearization, and saves calculation time. In addition, it is easy to implement and robust. Error analysis shows that CDTM is consistent and converges fast. We obtain more accurate results using the coded differential transform method as compared to other methods.

Finding PageRank vector is of great scientific and practical interest due to its applicability to modern search engines. Despite the fact that this problem is reduced to finding the eigenvector of the stochastic matrix $P$, the need for new algorithms is justified by a large size of the input data. To achieve no more than linear execution time, various randomized methods have been proposed, returning the expected result only with some probability close enough to one. We will consider two of them by reducing the problem of calculating the PageRank vector to the problem of finding equilibrium in an antagonistic matrix game, which is then solved using the Grigoriadis – Khachiyan algorithm. This implementation works effectively under the assumption of sparsity of the input matrix. As far as we know, there are no successful implementations of neither the Grigoriadis – Khachiyan algorithm nor its application to the task of calculating the PageRank vector. The purpose of this paper is to fill this gap. The article describes an algorithm giving pseudocode and some details of the implementation. In addition, it discusses another randomized method of calculating the PageRank vector, namely, Markov chain Monte Carlo (MCMC), in order to compare the results of these algorithms on matrices with different values of the spectral gap. The latter is of particular interest, since the magnitude of the spectral gap strongly affects the convergence rate of MCMC and does not affect the other two approaches at all. The comparison was carried out on two types of generated graphs: chains and $d$-dimensional cubes. The experiments, as predicted by the theory, demonstrated the effectiveness of the Grigoriadis – Khachiyan algorithm in comparison with MCMC for sparse graphs with a small spectral gap value. The written code is publicly available, so everyone can reproduce the results themselves or use this implementation for their own needs. The work has a purely practical orientation, no theoretical results were obtained.

The influence of the process of initiating a rapid local heat release near surface streamlined by supersonic gas (air) flow on the separation region that occurs during a fast turn of the flow was investigated. This surface consists of two planes that form obtuse angle when crossing, so that when flowing around the formed surface, the supersonic gas flow turns by a positive angle, which forms an oblique shock wave that interacts with the boundary layer and causes flow separation. Rapid local heating of the gas above the streamlined surface simulates long spark discharge of submicrosecond duration that crosses the flow. The gas heated in the discharge zone interacts with the separation region. The flow can be considered two-dimensional, so the numerical simulation is carried out in a two-dimensional formulation. Numerical simulation was carried out for laminar regime of flow using the sonicFoam solver of the OpenFOAM software package.

The paper describes a method for constructing a two-dimensional computational grid using hexagonal cells. A study of grid convergence has been carried out. A technique is given for setting the initial profiles of the flow parameters at the entrance to the computational domain, which makes it possible to reduce the computation time by reducing the number of computational cells. A method for non-stationary simulation of the process of rapid local heating of a gas is described, which consists in superimposing additional fields of increased pressure and temperature values calculated from the amount of energy deposited in oncoming supersonic gas flow on the corresponding fields of values obtained in the stationary case. The parameters of the energy input into the flow corresponding to the parameters of the electric discharge process, as well as the parameters of the oncoming flow, are close to the experimental values.

During analyzing numerical simulation data it was found that the initiation of rapid local heating leads to the appearance of a gas-dynamic perturbation (a quasi-cylindrical shock wave and an unsteady swirling flow), which, when interacting with the separation region, leads to a displacement of the separation point downstream. The paper considers the question of the influence of the energy spent on local heating of the gas, and of the position on the streamlined surface of the place of heating relative to the separation point, on the value of its maximum displacement.

In this paper we propose high-order (tensor) methods for two types of saddle point problems. Firstly, we consider the classic min-max saddle point problem. Secondly, we consider the search for a stationary point of the saddle point problem objective by its gradient norm minimization. Obviously, the stationary point does not always coincide with the optimal point. However, if we have a linear optimization problem with linear constraints, the algorithm for gradient norm minimization becomes useful. In this case we can reconstruct the solution of the optimization problem of a primal function from the solution of gradient norm minimization of dual function. In this paper we consider both types of problems with no constraints. Additionally, we assume that the objective function is $\mu$-strongly convex by the first argument, $\mu$-strongly concave by the second argument, and that the $p$-th derivative of the objective is Lipschitz-continous.

For min-max problems we propose two algorithms. Since we consider strongly convex a strongly concave problem, the first algorithm uses the existing tensor method for regular convex concave saddle point problems and accelerates it with the restarts technique. The complexity of such an algorithm is linear. If we additionally assume that our objective is first and second order Lipschitz, we can improve its performance even more. To do this, we can switch to another existing algorithm in its area of quadratic convergence. Thus, we get the second algorithm, which has a global linear convergence rate and a local quadratic convergence rate.

Finally, in convex optimization there exists a special methodology to solve gradient norm minimization problems by tensor methods. Its main idea is to use existing (near-)optimal algorithms inside a special framework. I want to emphasize that inside this framework we do not necessarily need the assumptions of strong convexity, because we can regularize the convex objective in a special way to make it strongly convex. In our article we transfer this framework on convex-concave objective functions and use it with our aforementioned algorithm with a global linear convergence and a local quadratic convergence rate.

Since the saddle point problem is a particular case of the monotone variation inequality problem, the proposed methods will also work in solving strongly monotone variational inequality problems.

The Lipschitz continuous property has been used for a long time to solve the global optimization problem and continues to be used. Here we can mention the work of Piyavskii, Yevtushenko, Strongin, Shubert, Sergeyev, Kvasov and others. Most papers assume a priori knowledge of the Lipschitz constant, but the derivation of this constant is a separate problem. Further still, we must prove that an objective function is really Lipschitz, and it is a complicated problem too. In the case where the Lipschitz continuity is established, Strongin proposed an algorithm for global optimization of a satisfying Lipschitz condition on a compact interval function without any a priori knowledge of the Lipschitz estimate. The algorithm not only finds a global extremum, but it determines the Lipschitz estimate too. It is known that every function that satisfies the Lipchitz condition on a compact convex set is uniformly continuous, but the reverse is not always true. However, there exist models (Arutyunova, Dulliev, Zabotin) whose study requires a minimization of the continuous but definitely not Lipschitz function. One of the algorithms for solving such a problem was proposed by R. J. Vanderbei. In his work he introduced some generalization of the Lipchitz property named $\varepsilon$-Lipchitz and proved that a function defined on a compact convex set is uniformly continuous if and only if it satisfies the $\varepsilon$-Lipchitz condition. The above-mentioned property allowed him to extend Piyavskii’s method. However, Vanderbei assumed that for a given value of $\varepsilon$ it is possible to obtain an associate Lipschitz $\varepsilon$-constant, which is a very difficult problem. Thus, there is a need to construct, for a function continuous on a compact convex domain, a global optimization algorithm which works in some way like Strongin’s algorithm, i.e., without any a priori knowledge of the Lipschitz $\varepsilon$-constant. In this paper we propose an extension of Strongin’s global optimization algorithm to a function continuous on a compact interval using the $\varepsilon$-Lipchitz conception, prove its convergence and solve some numerical examples using the software that implements the developed method.

The work is devoted to the study of subgradient methods with different variations of the Polyak stepsize for minimization functions from the class of weakly convex and relatively weakly convex functions that have the corresponding analogue of a sharp minimum. It turns out that, under certain assumptions about the starting point, such an approach can make it possible to justify the convergence of the subgradient method with the speed of a geometric progression. For the subgradient method with the Polyak stepsize, a refined estimate for the rate of convergence is proved for minimization problems for weakly convex functions with a sharp minimum. The feature of this estimate is an additional consideration of the decrease of the distance from the current point of the method to the set of solutions with the increase in the number of iterations. The results of numerical experiments for the phase reconstruction problem (which is weakly convex and has a sharp minimum) are presented, demonstrating the effectiveness of the proposed approach to estimating the rate of convergence compared to the known one. Next, we propose a variation of the subgradient method with switching over productive and non-productive steps for weakly convex problems with inequality constraints and obtain the corresponding analog of the result on convergence with the rate of geometric progression. For the subgradient method with the corresponding variation of the Polyak stepsize on the class of relatively Lipschitz and relatively weakly convex functions with a relative analogue of a sharp minimum, it was obtained conditions that guarantee the convergence of such a subgradient method at the rate of a geometric progression. Finally, a theoretical result is obtained that describes the influence of the error of the information about the (sub)gradient available by the subgradient method and the objective function on the estimation of the quality of the obtained approximate solution. It is proved that for a sufficiently small error $\delta > 0$, one can guarantee that the accuracy of the solution is comparable to $\delta$.