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The present article describes the authors’ model of construction of the distributed computer network and realization in it of the distributed calculations which are carried out within the limits of the software-information environment providing management of the information, automated and engineering systems of intellectual buildings. The presented model is based on the functional approach with encapsulation of the non-determined calculations and various side effects in monadic calculations that allows to apply all advantages of functional programming to a choice and execution of scenarios of management of various aspects of life activity of buildings and constructions. Besides, the described model can be used together with process of intellectualization of technical and sociotechnical systems for increase of level of independence of decision-making on management of values of parameters of the internal environment of a building, and also for realization of methods of adaptive management, in particular application of various techniques and approaches of an artificial intellect. An important part of the model is a directed acyclic graph, which is an extension of the blockchain with the ability to categorically reduce the cost of transactions taking into account the execution of smart contracts. According to the authors it will allow one to realize new technologies and methods — the distributed register on the basis of the directed acyclic graph, calculation on edge and the hybrid scheme of construction of artificial intellectual systems — and all this together can be used for increase of efficiency of management of intellectual buildings. Actuality of the presented model is based on necessity and importance of translation of processes of management of life cycle of buildings and constructions in paradigm of Industry 4.0 and application for management of methods of an artificial intellect with universal introduction of independent artificial cognitive agents. Model novelty follows from cumulative consideration of the distributed calculations within the limits of the functional approach and hybrid paradigm of construction of artificial intellectual agents for management of intellectual buildings. The work is theoretical. The article will be interesting to scientists and engineers working in the field of automation of technological and industrial processes both within the limits of intellectual buildings, and concerning management of complex technical and social and technical systems as a whole.

In this work, parallel method for solving single-phase flow problems in a fractured porous media is considered. Method is based on the representation of fractures by surfaces embedded into the computational mesh, and known as the embedded discrete fracture model. Porous medium and fractures are represented as two independent continua within the model framework. A distinctive feature of the considered approach is that fractures do not modify the computational grid, while an additional degree of freedom is introduced for each cell intersected by the fracture. Discretization of fluxes between fractures and porous medium continua uses the pre-calculated intersection characteristics of fracture surfaces with a three-dimensional computational grid. The discretization of fluxes inside a porous medium does not depend on flows between continua. This allows the model to be integrated into existing multiphase flow simulators in porous reservoirs, while accurately describing flow behaviour near fractures.

Previously, the author proposed monotonic modifications of the model using nonlinear finite-volume schemes for the discretization of the fluxes inside the porous medium: a monotonic two-point scheme or a compact multi-point scheme with a discrete maximum principle. It was proved that the discrete solution of the obtained nonlinear problem preserves non-negativity or satisfies the discrete maximum principle, depending on the choice of the discretization scheme.

This work is a continuation of previous studies. The previously proposed monotonic modification of the model was parallelized using the INMOST open-source software platform for parallel numerical modelling. We used such features of the INMOST as a balanced grid distribution among processors, scalable methods for solving sparse distributed systems of linear equations, and others. Parallel efficiency was demonstrated experimentally.

Mathematical models of gas dynamics and its computational industry, in our opinion, are far from perfect. We will look at this problem from the point of view of a clear probabilistic micro-model of a gas from hard spheres, relying on both the theory of random processes and the classical kinetic theory in terms of densities of distribution functions in phase space, namely, we will first construct a system of nonlinear stochastic differential equations (SDE), and then a generalized random and nonrandom integro-differential Boltzmann equation taking into account correlations and fluctuations. The key feature of the initial model is the random nature of the intensity of the jump measure and its dependence on the process itself.

Briefly recall the transition to increasingly coarse meso-macro approximations in accordance with a decrease in the dimensionalization parameter, the Knudsen number. We obtain stochastic and non-random equations, first in phase space (meso-model in terms of the Wiener — measure SDE and the Kolmogorov – Fokker – Planck equations), and then — in coordinate space (macro-equations that differ from the Navier – Stokes system of equations and quasi-gas dynamics systems). The main difference of this derivation is a more accurate averaging by velocity due to the analytical solution of stochastic differential equations with respect to the Wiener measure, in the form of which an intermediate meso-model in phase space is presented. This approach differs significantly from the traditional one, which uses not the random process itself, but its distribution function. The emphasis is placed on the transparency of assumptions during the transition from one level of detail to another, and not on numerical experiments, which contain additional approximation errors.

The theoretical power of the microscopic representation of macroscopic phenomena is also important as an ideological support for particle methods alternative to difference and finite element methods.

This article discusses the mechanical properties of carbon nanotube (CNT)-reinforced platinum under uniaxial tensile loading using the molecular dynamics method. A review of current computational and experimental studies on the use of carbon nanotube-reinforced composites from a structural point of view. However, quantitative and qualitative studies of CNTs to improve the properties of composites are still rare. Composite selection is a promising application for platinum alloys in many cases where they may be subjected to mechanical stress, including in biocompatibility sources. Pt-reinforced with CNTs may have additional possibilities for implantation of the implant and at the same time obtain the required mechanical characteristics.

The structure of the composite is composed of a Pt crystal with a face-centered cubic lattice with a constant of 3.92 Å and a carbon nanotube. The Pt matrix has the shape of a cube with dimensions of $43.1541 Å \times 43.1541 Å \times 43.1541 Å$. The hole size in the average platinum dimension is the radius of the carbon nanotube of the «zigzag» type (8,0), which is 2.6 Å. A carbon nanotube is placed in a hole with a radius of 4.2 Å. At such parameters, the maximum energy level was mutually observed. The model under consideration is contained in 320 atomic bombs and 5181 atomic platinum. The volume fraction of deaths in the Pt-C composite is 5.8%. At the first stage of the study, the strain rate was analyzed for stress-strain and energy change during uniaxial action on the Pt-C composite.

Analysis of the strain rate study showed that the consumption yield strength increases with high strain rate, and the elasticity has increased density with decreasing strain rate. This work also increased by 40% for Pt-C, the elasticity of the composite decreased by 42.3%. In general, fracture processes are considered in detail, including plastic deformation on an atomistic scale.

The development of a high-temperature gas-cooled reactor (HTGR) constituting a part of nuclear power-and-process station and intended for large-scale hydrogen production is now in progress in the Russian Federation. One of the key objectives in development of the high-temperature gas-cooled reactor is the computational justification of the accepted design.

The article gives the procedure for the computational analysis of thermal and physical characteristics of the high-temperature gas-cooled reactor. The procedure is based on the use of the state-of-the-art codes for personal computer (PC).

The objective of thermal and physical analysis of the reactor as a whole and of the core in particular was achieved in three stages. The idea of the first stage is to justify the neutron physical characteristics of the block-type core during burn-up with the use of the MCU-HTR code based on the Monte Carlo method. The second and the third stages are intended to study the coolant flow and the temperature condition of the reactor and the core in 3D with the required degree of detailing using the FlowVision and the ANSYS codes.

For the purpose of carrying out the analytical studies the computational models of the reactor flow path and the fuel assembly column were developed.

As per the results of the computational modeling the design of the support columns and the neutron physical characteristics of the fuel assembly were optimized. This results in the reduction of the total hydraulic resistance of the reactor and decrease of the maximum temperature of the fuel elements.

The dependency of the maximum fuel temperature on the value of the power peaking factors determined by the arrangement of the absorber rods and of the compacts of burnable absorber in the fuel assembly is demonstrated.

The paper is devoted to the study of wave and relaxation effects during the pulsed outflow of a gas mixture with a high content of solid particles from a cylindrical channel during its initial partial filling. The problem is formulated in a two-speed two-temperature formulation and was solved numerically by the hybrid large-particle method of the second order of approximation. The numerical algorithm is implemented in the form of parallel computing using basic Free Pascal language tools. The applicability and accuracy of the method for wave flows of concentrated gas-particles mixtures is confirmed by comparison with test asymptotically accurate solutions. The calculation error on a grid of low detail in the characteristic flow zones of a two-phase medium was 10^-6 . . . 10^-5.

Based on the wave diagram, the analysis of the physical pattern of the outflow of a gas suspension partially filling a cylindrical channel is performed. It is established that, depending on the degree of initial filling of the channel, various outflow modes are formed. The first mode is implemented with a small degree of loading of the high-pressure chamber, at which the left boundary of the gas-particles mixture crosses the outlet section before the arrival of the rarefaction wave reflected from the bottom of the channel. At the same time, the maximum value of the mass flow rate of the mixture is achieved. Other modes are formed in cases of a larger initial filling of the channel, when the rarefaction waves reflected from the bottom of the channel interact with the gas suspension layer and reduce the intensity of its outflow.

The influence of relaxation properties with changing particle size on the dynamics of a limited layer of a gas-dispersed medium is studied. Comparison of the outflow of a limited gas suspension layer with different particle sizes shows that for small particles (the Stokes number is less than 0.001), an anomalous phenomenon of the simultaneous existence of shock wave structures in the supersonic and subsonic flow of gas and suspension is observed. With an increase in the size of dispersed inclusions, the compaction jumps in the region of the two-phase mixture are smoothed out, and for particles (the Stokes number is greater than 0.1), they practically disappear. At the same time, the shock-wave configuration of the supersonic gas flow at the outlet of the channel is preserved, and the positions and boundaries of the energy-carrying volumes of the gas suspension are close when the particle sizes change.

The work is devoted to the description of the author’s formal statements of the clustering problem for a given number of clusters, algorithms for their solution, as well as the results of using this toolkit in medicine.

The solution of the formulated problems by exact algorithms of implementations of even relatively low dimensions before proving optimality is impossible in a finite time due to their belonging to the NP class.

In this regard, we have proposed a hybrid algorithm that combines the advantages of precise methods based on clustering in paired distances at the initial stage with the speed of methods for solving simplified problems of splitting by cluster centers at the final stage. In the development of this direction, a sequential hybrid clustering algorithm using random search in the paradigm of swarm intelligence has been developed. The article describes it and presents the results of calculations of applied clustering problems.

To determine the effectiveness of the developed tools for optimal clustering of multidimensional objects according to a variety of heterogeneous indicators, a number of computational experiments were performed using data sets including socio-demographic, clinical anamnestic, electroencephalographic and psychometric data on the cognitive status of patients of the cardiology clinic. An experimental proof of the effectiveness of using local search algorithms in the paradigm of swarm intelligence within the framework of a hybrid algorithm for solving optimal clustering problems has been obtained.

The results of the calculations indicate the actual resolution of the main problem of using the discrete optimization apparatus — limiting the available dimensions of task implementations. We have shown that this problem is eliminated while maintaining an acceptable proximity of the clustering results to the optimal ones. The applied significance of the obtained clustering results is also due to the fact that the developed optimal clustering toolkit is supplemented by an assessment of the stability of the formed clusters, which allows for known factors (the presence of stenosis or older age) to additionally identify those patients whose cognitive resources are insufficient to overcome the influence of surgical anesthesia, as a result of which there is a unidirectional effect of postoperative deterioration of complex visual-motor reaction, attention and memory. This effect indicates the possibility of differentiating the classification of patients using the proposed tools.

We examine a phenomenological algorithm for generating morphology of astrocytes, a major class of glial brain cells, based on morphometric data of rat brain protoplasmic astrocytes and observations of general cell development trends in vivo, based on current literature. We adapted the Space Colonization Algorithm (SCA) for procedural generation of astrocytic morphology from scratch. Attractor points used in generation were spatially distributed in the model volume according to the synapse distribution density in the rat hippocampus tissue during the first week of postnatal brain development. We analyzed and compared astrocytic morphology reconstructions at different brain development stages using morphometry estimation techniques such as Sholl analysis, number of bifurcations, number of terminals, total tree length, and maximum branching order. Using morphometric data from protoplasmic astrocytes of rats at different ages, we selected the necessary generation parameters to obtain the most realistic three-dimensional cell morphology models. We demonstrate that our proposed algorithm allows not only to obtain individual cell geometry but also recreate the phenomenon of tiling domain organization in the cell populations. In our algorithm tiling emerges due to the cell competition for territory and the assignment of unique attractor points to their processes, which then become unavailable to other cells and their processes. We further extend the original algorithm by splitting morphology generation in two phases, thereby simulating astrocyte tree structure development during the first and third-fourth weeks of rat postnatal brain development: rapid space exploration at the first stage and extensive branching at the second stage. To this end, we introduce two attractor types to separate two different growth strategies in time. We hypothesize that the extended algorithm with dynamic attractor generation can explain the formation process of fine astrocyte cell structures and maturation of astrocytic arborizations.

The article considers the mathematical aspects of the problem of identifying a multicomponent scattering medium based on pulsed multienergy X-ray irradiation data. X-ray diagnostics problems are of considerable interest from both theoretical and practical points of view, and radiographic methods are indispensable in non-destructive testing of products.

Within the framework of a mathematical model based on a non-stationary integro-differential equation of radiation transfer, the inverse problem of finding the attenuation coefficient for radiation known at the boundary of the region and the problem of identifying a substance based on the found values of the attenuation coefficient on a discrete set of irradiation energies of the medium are formulated.

A preliminary processing of a wide list of substances of interest in computed tomography was carried out to determine the possibility of their identification by an approximately specified radiation attenuation coefficient characterizing the medium. When analyzing the degree of proximity of substances in a certain norm, it was found that the set of all possible substances potentially contained in the medium is divided into a finite number of non-intersecting clusters. For a sufficiently short duration of the probing signal, the scattering component of the radiation leaving the medium is asymptotically small. This circumstance allows us to reduce the inverse problem for the radiation transfer equation to the problem of inverting the Radon transform from the attenuation coefficient. The possibility of unambiguous or partial identification of a substance by varying the duration of the probing pulse and the number of energy levels of irradiation of the medium is analyzed using numerical modeling methods on a specially developed digital phantom.

The key problems of remote forest pathology monitoring for coniferous forests affected by insect pests have been analyzed. It has been demonstrated that addressing these tasks requires the use of multiclass classification results for coniferous trees in high- and ultra-high-resolution images, which are promptly obtained through monitoring via satellites or unmanned aerial vehicles (UAVs). An analytical review of modern models and methods for multiclass classification of coniferous forest images was conducted, leading to the development of three fully convolutional neural network models: Mo-U-Net, At-Mo-U-Net, and Res-Mo-U-Net, all based on the classical U-Net architecture. Additionally, the Segformer transformer model was modified to suit the task. For RGB images of fir trees Abies sibirica affected by the four-eyed bark beetle Polygraphus proximus, captured using a UAV-mounted camera, two datasets were created: the first dataset contains image fragments and their corresponding reference segmentation masks sized 256 × 256 × 3 pixels, while the second dataset contains fragments sized 480 × 480 × 3 pixels. Comprehensive studies were conducted on each trained neural network model to evaluate both classification accuracy for assessing the degree of damage (health status) of Abies sibirica trees and computation speed using test datasets from each set. The results revealed that for fragments sized 256 × 256 × 3 pixels, the At-Mo-U-Net model with an attention mechanism is preferred alongside the Modified Segformer model. For fragments sized 480 × 480 × 3 pixels, the Res-Mo-U-Net hybrid model with residual blocks demonstrated superior performance. Based on classification accuracy and computation speed results for each developed model, it was concluded that, for production-scale multiclass classification of affected fir trees, the Res-Mo-U-Net model is the most suitable choice. This model strikes a balance between high classification accuracy and fast computation speed, meeting conflicting requirements effectively.