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The paper simulates a mixture of gases in a multi-stage micro-pump and evaluates its effectiveness at separating the components of the mixture. A device in the form of a long channel with a series of transverse plates is considered. A temperature difference between the sides of the plates induces a radiometric gas flow within the device, and the differences in masses of the gases lead to differences in flow velocities and to the separation of the mixture. Modeling is based on the numerical solution of the Boltzmann kinetic equation, for which a splitting scheme is used, i. e., the advection equation and the relaxation problem are solved separately in alternation. The calculation of the collision integral is performed using the conservative projection method. This method ensures the strict fulfillment of the laws of conservation of mass, momentum, and energy, as well as the important asymptotic property of the equality of the integral of the Maxwell function to zero. Explicit first-order and second-order TVD-schemes are used to solve the advection equation. The calculations were performed for a neon-argon mixture using a model of solid spheres with real molecular diameters and masses. Software has been developed to allow calculations on personal computers and cluster systems. The use of parallelization leads to faster computation and constant time per iteration for devices of different sizes, enabling the modeling of large particle systems. It was found that the value of mixture separation, i. e. the ratio of densities at the ends of the device linearly depends on the number of cascades in the device, which makes it possible to estimate separation for multicascade systems, computer modeling of which is impossible. Flows and distributions of gas inside the device during its operation were analyzed. It was demonstrated that devices of this kind with a sufficiently large number of plates are suitable for the separation of gas mixtures, given that they have no moving parts and are quite simple in manufacture and less subject to wear.

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%.

This paper proposes an architectural solution for organizing a convolutional neural network (CNN) oriented towards hardware implementation on edge devices under limited resources. To this goal, an approach to compressing spectrograms to a given size (28 × 28) is proposed using discretization, monoconversion, windowed Fourier transform, and two-dimensional interpolation. A balanced convolution procedure is developed based on compact convolutional filters, the size of which provides the balance between computational complexity and accuracy required for edge devices. An algorithm that enables convolution operations and calculation of the error function gradient in the convolutional layer in a single cycle ensuring increased performance in both inference and training modes of the CNN is proposed. The tradeoff between network trainability and its resistance to overfitting is optimized by applying the Dropout regularization method with a dropout coefficient of 0.5 for the fully connected layer.

The effectiveness of the proposed solution was demonstrated using the example of recognizing audio spectrograms of car and airplane engine sounds. The CNN was trained on a balanced dataset consisting of 7160 audio recordings. The trained network demonstrated high recognition accuracy (95%), low loss values (< 0.2), and balanced precision/recall/F-metric, demonstrating the effectiveness of the developed CNN model.

An approach to evaluation of a parametric portrait of integral equations of the theory of liquids in the RISM approximation was proposed. To obtain all associated solutions the continuation method was used. The equations reduced to a two-centered molecule model for symmetry reasons were deduced for molecular liquids. For molecular liquids, some equations were obtained which could be reduced, for symmetry reasons, to a two-center molecular model. To avoid critical points we changed the dependence of RISM-equations on reverse compressibility. The suggested method was used to perform numerical computations of methane reverse compressibility isotherms with three closures. No bifurcation of solutions was observed in the case of the partially linearized hypernetted chain closure. For other closures bifurcations of solutions were obtained and the model behavior nontypical for simple liquids was observed. In the case of Percus-Yevick closure nonphysical solutions were obtained at low temperature and density. Additional solution branch with a kink in the bifurcation point was obtained in the case of hypernetted chain closure at temperature above the critical point.

Results obtained during practical training session on MPI during high perfomance computing summer school MIPT-2010 are discussed. MPI technology were one of technologies proposed to participants for realization of project. 3D version of Conway’s Game of Life was proposed as a project. Algorithms used in the development, theoretical and practical assessment of their scalability is analyzed.

The method of modelling of a compact bone tissue microstructure is presented. The modelling sample is considered as set of the structural elements containing reinforcing element – osteon and a matrix. The form of structural elements is defined by distances to next osteons and directions of next osteons arrangement. Calculation of the stress and strain state of the modelling sample is carried out at tension in program complex ANSYS. Results of calculation have shown, that haversian canals are stress concentrators.

The computation model of the interlayer magnetostatic energy and its contribution into the total magnetic energy of multilayer films has been proposed. The model has been used for the energy computation of three-layer Co/Cu/Co structure. The comparison of obtained theoretical results with experimental data has been allowed made an estimation of magnetostatic interaction influence on a value and a character of saturation field behavior for such structures.

The computer code NINE (Newtonian Iteration for Nonlinear Equation) for numerical solution of the boundary problems for nonlinear differential equations on the basis of continuous analogue of the Newton method (CANM) is presented. Numerov’s finite-difference appproximation is applied to provide the fourth accuracy order with respect to the discretization stepsize. Algorithms of calculating the Newtonian iterative parameter are discussed. A convergence of iteration process in dependence on choice of the iteration parameter has been studied. Results of numerical investigation of the particle-like solutions of the scalar field equation are given.

The problems of radiative-conductive heat transfer in the scattering layer are considered. They consist in finding the temperature profile and improving the heat transfer from boundaries. For their solution the Monte Carlo method is used. The different approaches of parallelization of proposed algorithm are analyzed.

The paper presents a new dense material modeling method based on spherepolyhedra packing algorithm, describes mathematical model of spherepolyhedra and discuss the results of computation experiments on different spherepolyhedra packs. The results of experiments show convergence of proposed method. Experiments include investigations of spherepolyhedra packs with different shapes, polydisperse and oriented structures. Presented method would be applied to virtual design of dense materials composed of non-spherical particles.