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The paper presents the methodology of «line-by-line» calculations of the Earth and atmosphere thermal radiation. Intensity of radiation is computed by numerical integration of the radiative transfer kinetic equation and the system of the angular momentum equations using quasi-diffusion method. Data from HITRAN molecular spectroscopic database [Rothman et al., 2009] are used to calculate the atmosphere optical parameters.

The problem of accumulation and the statistical analysis of computer experiment results are solved. The main experiment program is considered as the data source. The results of main experiment are collected on specially prepared sheet Excel with pre-organized structure for the accumulation, statistical processing and visualization of the data. The created method and the program are used at efficiency research of the scientific researches which are carried out by authors.

The review is based on some of the authors ’early works of particular scientific, methodological and practical interest and the greatest attention is paid to recent works, where quite detailed numerical studies of not only single, but also double and multiple explosions in a wide range of heights and environmental conditions have been performed . Since the shock wave of a powerful explosion is one of the main damaging factors in the lower atmosphere, the review focuses on both the physical analysis of their propagation and their interaction. Using the three-dimensional algorithms developed by the authors, the effects of interference and diffraction of several shock waves, which are interesting from a physical point of view, in the absence and presence of an underlying surface of various structures are considered. Quantitative characteristics are determined in the region of their maximum values, which is of known practical interest. For explosions in a dense atmosphere, some new analytical solutions based on the small perturbation method have been found that are convenient for approximate calculations. For a number of conditions, the possibility of using the self-similar properties of equations of the first and second kind to solve problems on the development of an explosion has been shown.

Based on numerical analysis, a fundamental change in the structure of the development of the perturbed region with a change in the height of the explosion in the range of 100–120 km is shown. At altitudes of more than 120 km, the geomagnetic field begins to influence the development of the explosion; therefore, even for a single explosion, the picture of the plasma flow after a few seconds becomes substantially three-dimensional. For the calculation of explosions at altitudes of 120–1000 km under the guidance of academician A. Kholodov. A special three-dimensional numerical algorithm based on the MHD approximation was developed. Numerous calculations were performed and for the first time a quite detailed picture of the three-dimensional flow of the explosion plasma was obtained with the formation of an upward jet in 5–10 s directed in the meridional plane approximately along the geomagnetic field. After some modification, this algorithm was used to calculate double explosions in the ionosphere, spaced a certain distance. The interaction between them was carried out both by plasma flows and through a geomagnetic field. Some results are given in this review and are described in detail in the original articles.

A cluster method of mathematical modeling of interval-stochastic thermal processes in complex electronic systems (ES), is developed. In the cluster method, the construction of a complex ES is represented in the form of a thermal model, which is a system of clusters, each of which contains a core that combines the heat-generating elements falling into a given cluster, the cluster shell and a medium flow through the cluster. The state of the thermal process in each cluster and every moment of time is characterized by three interval-stochastic state variables, namely, the temperatures of the core, shell, and medium flow. The elements of each cluster, namely, the core, shell, and medium flow, are in thermal interaction between themselves and elements of neighboring clusters. In contrast to existing methods, the cluster method allows you to simulate thermal processes in complex ESs, taking into account the uneven distribution of temperature in the medium flow pumped into the ES, the conjugate nature of heat exchange between the medium flow in the ES, core and shells of clusters, and the intervalstochastic nature of thermal processes in the ES, caused by statistical technological variation in the manufacture and installation of electronic elements in ES and random fluctuations in the thermal parameters of the environment. The mathematical model describing the state of thermal processes in a cluster thermal model is a system of interval-stochastic matrix-block equations with matrix and vector blocks corresponding to the clusters of the thermal model. The solution to the interval-stochastic equations are statistical measures of the state variables of thermal processes in clusters - mathematical expectations, covariances between state variables and variance. The methodology for applying the cluster method is shown on the example of a real ES.

This paper presents a methodology for modeling centrifugal pumps using the example of the NM 1250 260 main oil centrifugal pump. We use FlowVision CFD software as the numerical modeling instrument. Bench tests and numerical modeling use water as a working fluid. The geometrical model of the pump is fully three-dimensional and includes the pump housing to account for leakages. In order to reduce the required computational resources, the methodology specifies leakages using flow rate rather than directly modeling them. Surface roughness influences flow through the wall function model. The wall function model uses an equivalent sand roughness, and a formula for converting real roughness into equivalent sand roughness is applied in this work. FlowVision uses the sliding mesh method for simulation of the rotation of the impeller. This approach takes into account the nonstationary interaction between the rotor and diffuser of the pump, allowing for accurate resolution of recirculation vortices that occur at low flow rates.

The developed methodology has achieved high consistency between numerical simulations results and experiments at all pump operating conditions. The deviation in efficiency at nominal conditions is 0.42%, and in head is 1.9%. The deviation of calculated characteristics from experimental ones increases as the flow rate increases and reaches a maximum at the far-right point of the characteristic curve (up to 4.8% in head). This phenomenon occurs due to a slight mismatch between the geometric model of the impeller used in the calculation and the real pump model from the experiment. However, the average arithmetic relative deviation between numerical modeling and experiment for pump efficiency at 6 points is 0.39%, with an experimental efficiency measurement error of 0.72%. This meets the accuracy requirements for calculations. In the future, this methodology can be used for a series of optimization and strength calculations, as modeling does not require significant computational resources and takes into account the non-stationary nature of flow in the pump.

An approach to numerical analysis of thermo-hydraulic processes proceeding in a fast-neutron reactor is described in the given article. The description covers physical models, numerical schemes and geometry simplifications accepted in the computational model. Steady-state and dynamic regimes of reactor operation are considered. The steady-state regimes simulate the reactor operation at nominal power. The dynamic regimes simulate the shutdown reactor cooling by means of the heat-removal system.

Simulation of thermo-hydraulic processes is carried out in the FlowVision CFD software. A mathematical model describing the coolant flow in the first loop of the fast-neutron reactor was developed on the basis of the available geometrical model. The flow of the working fluid in the reactor simulator is calculated under the assumption that the fluid density does not depend on pressure, with use a $k–\varepsilon$ turbulence model, with use of a model of dispersed medium, and with account of conjugate heat exchange. The model of dispersed medium implemented in the FlowVision software allowed taking into account heat exchange between the heat-exchanger lops. Due to geometric complexity of the core region, the zones occupied by the two heat exchangers were modeled by hydraulic resistances and heat sources.

Numerical simulation of the coolant flow in the FlowVision software enabled obtaining the distributions of temperature, velocity and pressure in the entire computational domain. Using the model of dispersed medium allowed calculation of the temperature distributions in the second loops of the heat exchangers. Besides that, the variation of the coolant temperature along the two thermal probes is determined. The probes were located in the cool and hot chambers of the fast-neutron reactor simulator. Comparative analysis of the numerical and experimental data has shown that the developed mathematical model is correct and, therefore, it can be used for simulation of thermo-hydraulic processes proceeding in fast-neutron reactors with sodium coolant.

Migration has a significant impact on the shaping of the demographic structure of the territories population, the state of regional and local labour markets. As a rule, rapid change in the working-age population of any territory due to migration processes results in an imbalance in supply and demand on labour markets and a change in the demographic structure of the population. Migration is also to a large extent a reflection of socio-economic processes taking place in the society. Hence, the issues related to the study of migration factors, the direction, intensity and structure of migration flows, and the prediction of their magnitude are becoming topical issues these days.

Mathematical tools are often used to analyze, predict migration processes and assess their consequences, allowing for essentially accurate modelling of migration processes for different territories on the basis of the available statistical data. In recent years, quite a number of scientific papers on modelling internal and external migration flows using mathematical methods have appeared both in Russia and in foreign countries in recent years. Consequently, there has been a need to systematize the currently most commonly used methods and tools applied in migration modelling to form a coherent picture of the main trends and research directions in this field.

The presented review considers the main approaches to migration modelling and the main components of migration modelling methodology, i. e. stages, methods, models and model classification. Their comparative analysis was also conducted and general recommendations on the choice of mathematical tools for modelling were developed. The review contains two sections: migration modelling methods and migration models. The first section describes the main methods used in the model development process — econometric, cellular automata, system-dynamic, probabilistic, balance, optimization and cluster analysis. Based on the analysis of modern domestic and foreign publications on migration, the most common classes of models — regression, agent-based, simulation, optimization, probabilistic, balance, dynamic and combined — were identified and described. The features, advantages and disadvantages of different types of migration process models were considered.

The work discusses the methodological basis and problems of modeling of world dynamics. Outlines approaches to the construction of a new simulation model of global development and the results of the simulation. The basis of the model building is laid empirical approach which based on the statistical analysis of the main socio-economic indicators. On the basis of this analysis identified the main variables. Dynamic equations (in continuous differential form) were written for these variables. Dependencies between variables were selected based on the dynamics of indicators in the past and on the basis of expert assessments, while econometric techniques were used, based on regression analysis. Calculations have been performed for the resulting dynamic equations system, the results are presented in the form of a trajectories beam for those indicators that are directly observable, and for which statistics are available. Thus, it is possible to assess the scatter of the trajectories and understand the predictive capability of this model.

The proteins of the Major Histocompatibility Complex (MHC) play a key role in the functioning of the adaptive immune system, and the identification of peptides that bind to them is an important step in the development of vaccines and understanding the mechanisms of autoimmune diseases. Today, there are a number of methods for predicting the binding of a particular MHC allele to a peptide. One of the best such methods is NetMHCpan-4.0, which is based on an ensemble of artificial neural networks. This paper presents a methodology for qualitatively improving the underlying neural network underlying NetMHCpan-4.0. The proposed method uses the ensemble construction technique and adds as input an estimate of the Potts model taken from static mechanics, which is a generalization of the Ising model. In the general case, the model reflects the interaction of spins in the crystal lattice. Within the framework of the proposed method, the model is used to better represent the physical nature of the interaction of proteins included in the complex. To assess the interaction of the MHC + peptide complex, we use a two-dimensional Potts model with 20 states (corresponding to basic amino acids). Solving the inverse problem using data on experimentally confirmed interacting pairs, we obtain the values of the parameters of the Potts model, which we then use to evaluate a new pair of MHC + peptide, and supplement this value with the input data of the neural network. This approach, combined with the ensemble construction technique, allows for improved prediction accuracy, in terms of the positive predictive value (PPV) metric, compared to the baseline model.

The article deals with the development of a methodological approach to forecasting and modeling the socioeconomic consequences of viral epidemics in conditions of heterogeneous economic development of territorial systems. The relevance of the research stems from the need for rapid mechanisms of public management and stabilization of adverse epidemiological situation, taking into account the spatial heterogeneity of the spread of COVID-19, accompanied by a concentration of infection in large metropolitan areas and territories with high economic activity. The aim of the work is to substantiate a methodology to assess the spatial heterogeneity of the spread of coronavirus infection, find poles of its growth, emerging spatial clusters and zones of their influence with the assessment of inter-territorial relationships, as well as simulate the effects of worsening epidemiological situation on the dynamics of economic development of regional systems. The peculiarity of the developed approach is the spatial clustering of regional systems by the level of COVID-19 incidence, conducted using global and local spatial autocorrelation indices, various spatial weight matrices, and L.Anselin mutual influence matrix based on the statistical information of the Russian Federal State Statistics Service. The study revealed a spatial cluster characterized by high levels of infection with COVID-19 with a strong zone of influence and stable interregional relationships with surrounding regions, as well as formed growth poles which are potential poles of further spread of coronavirus infection. Regression analysis using panel data not only confirmed the impact of COVID-19 incidence on the average number of employees in enterprises, the level of average monthly nominal wages, but also allowed to form a model for scenario prediction of the consequences of the spread of coronavirus infection. The results of this study can be used to form mechanisms to contain the coronavirus infection and stabilize socio-economic at macroeconomic and regional level and restore the economy of territorial systems, depending on the depth of the spread of infection and the level of economic damage caused.