Modeling Methods Research Articles

High precision aerodynamic heat prediction method based on data augmentation and transfer learning

Data-driven modeling methods have become one of the main technologies for predicting aerodynamic heat in hypersonic conditions. However, due to the limitations of wind tunnel experimental conditions, the spatial distribution of aerothermal wind tunnel experimental data is often sparse, and the sample size is relatively small. Furthermore, there is a lack of direct correlation in the aerodynamic heat distribution data among different shapes of vehicles, which poses challenges for constructing high-performance data-driven aerodynamic heat prediction models. To address these issues, this paper proposes a high-precision aerodynamic heat modeling and prediction method based on data augmentation and transfer learning. First, integrating the concept of data fusion, we propose to enhance the sparse aerothermal wind tunnel experimental data by using deep neural networks and introducing low-precision numerical computation results. Next, based on the close physical correlation between boundary layer outer edge information and wall surface aerodynamic heat, we construct the aerodynamic heat prediction model ED-ResNet using a double-series residual neural network. Finally, by fine-tuning the ED-ResNet model for transfer learning, high-precision predictions of aerothermal wind tunnel experimental results for different shaped vehicles are achieved under small sample conditions. Verification using hypersonic double-ellipsoid, blunt cone, and blunt bicone shows that after data augmentation, the prediction error of the aerodynamic heat prediction model is significantly reduced to 1/3 of that when data augmentation is not used. Moreover, through transfer learning, the model effectively leverages existing hypersonic double-ellipsoid aerothermal wind tunnel experimental data to achieve high-precision predictions of aerodynamic heat distribution for blunt cone and blunt double cone under different incoming flow conditions, with normalized root mean square error(NRMSE) maintained below 10%.

A framework for digital twin integration in biofabrication and a scaffold 3D bioplotting case study

Biofabrication, which integrates biological sciences with advanced manufacturing, is vital for innovations in tissue engineering and regenerative medicine. One primary consideration in this domain is ensuring consistent, scalable, and adaptable processes that are amenable to clinical translation. Toward this, this paper introduces a new framework for digital twin integration in biofabrication. Digital twins, which are real-time virtual replicas of physical systems, can facilitate comprehensive monitoring, accurate prediction, and effective optimization to enable robust biofabrication processes and systems. The proposed framework incorporates major building blocks for implementing digital twins for biofabrication, including comprehensive data acquisition and analysis using sophisticated sensors to integrate biological, material, and process data; advanced modeling methods utilizing traditional and machine learning algorithms to refine process parameters; and feedback loop for real-time process adjustments. The first phase of the framework is demonstrated through a case study highlighting process optimization of 3D bioplotting of polycaprolactone (PCL) scaffolds. The digital twin’s insights streamline decisions on bioplotting parameters and post-processing, ensuring that the printed scaffold meets the desired mechanical and biological properties, thereby significantly enhancing cell proliferation and printing resolution. Key benefits of this approach include optimized biofabrication outcomes by integrating physical and biological models, promoting scalability and reproducibility with an accelerated research and development phase. This work underlines the promise of digital convergence in biofabrication, which can accelerate advancements in regenerative medicine and tissue engineering.

Dynamic Prediction for Pollutant Emissions of Coal-fired Power Plant Based on CNN-LSTM-Attention

Abstract The prediction accuracy of pollutant emissions by using traditional modeling methods is unsatisfactory in dynamic conditions. To overcome the problem, data-driven modeling was introduced to build the dynamic model of pollutant emissions of power plants in this paper. Combining with the running data of a 300MW circulating fluidized bed (CFB) unit, the dynamic prediction models of SO2 and NO x emissions were established respectively by using conventional neural network-long short-term memory and attention mechanism (CNN-LSTM-Attention). Moreover, LSSVM, LSTM and CNN-LSTM were introduced for comparison to demonstrate the superiority of CNN-LSTM-Attention model respectively. Simulation results indicate that model can imitate change trend of actual data with high accuracy over a long period of time. Compared with LSSVM, LSTM and CNN-LSTM, the proposed model has better modeling performance under different load conditions. This work provides certain guidance for the application of deep learning in the industrial field.

A data-driven maximum entropy method for probability uncertainty analysis based on the B-spline theory

The probability density function (PDF) is quite important for structural reliability analysis; thus, accurate PDF modeling methods draw increasing attention. This paper proposes a novel metaheuristic data-driven paradigm of the maximum entropy method (MEM) based on the B-spline function theory. Firstly, a B-spline proxy of the MEM PDF is proposed for probability uncertainty analysis. We derive the parameter calculation formulation and calculate the undetermined parameters via the raw data of structural responses. Then, to determine the knots of the B-spline functions, we propose a novel data-driven approach with the aid of a powerful metaheuristic algorithm and the response data information. Different from the traditional MEM, the proposed method is a complete data-driven solution approach and does not involve the statistical moment calculation and the nonlinear equations composed of statistical moments. Combining the advantages of the B-spline theory and the MEM, the proposed method can reconstruct the response PDF with a complex shape, such as the PDF with multiple peaks or heavy tails. For verification, two numerical examples and one engineering example are analyzed, and compared with some classical PDF modeling methods. The results show that the proposed method is superior to the compared methods in terms of computational accuracy, when the same sample data is used.

An automatic generation approach of process model based on feature knowledge and geometric modeling

Traditional process planning applies knowledge to convert design information within the part model into process information. This information is then displayed by manually drawing two-dimensional (2D) process cards from multiple views, illustrating the geometric design and the process details. However, this method has revealed several issues, including poor efficiency, information misinterpretation, and the potential for manual drawing errors. This traditional approach hampers the integration of manufacturing information across engineering software. It exacerbates the digital divide between computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM). To address these issues, this study proposes an automated approach for creating three-dimensional (3D) process models based on feature knowledge and geometric modeling. The 3-axis milling features are recognized as the machining objects from the boundary representation (B-Rep) part model. Geometric and parameter knowledge for modeling is derived from the machining step. In the proposed approach, two geometric modeling methods are investigated to construct feature state volumes (FSVs). The first method utilizes FSV modeling to simulate the shape of unmachined features before rough machining, while the second method utilizes FSV modeling to construct the shape of machined features before finishing machining. The case studies illustrate that the proposed approach can construct FSVs for various machining states and autonomously generate process models. In digital manufacturing, this approach assists process planners in intuitively evaluating the reasonableness of process routes. Additionally, it provides the essential driving geometry required for the autonomous generation of tool paths.

Data-Driven Modeling of DC–DC Power Converters

This article presents a data-driven methodology for modeling DC–DC power electronic converters. Using the proposed methodology, the dynamics of a converter can be captured, thereby eliminating the need for explicit theoretical modeling methods. This approach only requires the acquisition of fundamental measurements: currents through inductors and voltages across capacitors. The acquired data are used to construct a linear difference system, which is algebraically manipulated to form a state–space representation of the converter under analysis. Three DC–DC converter topologies were analyzed, and their resulting models were tested and compared with simulation data, yielding an average error deviation of approximately 2% for current signals and 4% for voltage signals, demonstrating precise tracking of the actual dynamics. The proposed data-driven methodology could simplify the implementation of adaptive control strategies in larger-scale solutions or in the interconnection of multiple converters.

Exploring Cross-site User Modeling Without Cross-site User Identity Linkage: A Case Study of Content Preference Prediction

Performing user modeling on two or more social media platforms collaboratively and complementing each other (cross-site user modeling) has been a significant problem in the area of social media mining in recent years. The core of this problem is to get to know a person’s identities on multiple platforms and then train user models collaboratively among these platforms. However, for privacy protection, many people do not want their identities on different platforms to be linked and disclosed. For this problem, we set cross-site Content Preference Prediction as a task and propose a cross-site user modeling method without cross-site User Identity Linkage (UIL). The core thought borrowed from privacy-preserving recommender system research is to organize social media identities into groups to hide the identity linkage among platforms. Experiments on real-world datasets suggest that our method outperforms the existing cross-site user modeling methods with cross-site UIL regarding several metrics.

Subtractive manufacturing of composite materials with robotic manipulators: a comprehensive review

AbstractRobotic manipulators play an innovative role as a new method for high-precision, large-scale manufacturing of composite components. However, machining composite materials with these systems presents unique challenges. Unlike traditional monolithic materials, composites exhibit complex behaviour and inconsistent results during machining. Additionally, robotic manipulator as a machine tool often associates with stiffness and vibration issues which adds another layer of complexity to this approach. By employing a comprehensive analysis and a combination of quantitative and qualitative review methodology, this review paper aims to survey diverse properties of composite materials by different categories and their interaction with machining processes. Subsequently, a survey of manufacturing techniques for composite machining following with a review in various modeling practices to capture material machining behaviour under a systematic framework is presented. Thereafter, the reviewed literature examines the errors inherent in robotic systems, alongside ongoing research efforts in modeling to characterise robot behaviour and enhance its performance. Afterward, the paper explores the application of data-driven modelling methods, with a primary focus on digital twins, in enabling real-time monitoring and process optimisation. Finally, this paper aims to identify the gap in this field and suggests the potential routes for future research and application as well as their challenges.

Galaxy shapes in Magneticum

Context. Despite being one of the most fundamental properties of galaxies that dictate the form of the potential, the 3D shapes are intrinsically difficult to determine from observations. The improving quality of triaxial modeling methods in recent years has made it possible to measure these shapes more accurately. Aims. This study provides a comprehensive understanding of the stellar and dark matter (DM) shapes of galaxies and the connection between them. As these shapes are the result of the formation history of a galaxy, we investigate which galaxy properties they are correlated with, which will be especially useful for interpreting the results from dynamical modeling. Methods. Using the hydrodynamical cosmological simulation Magneticum Pathfinder Box4 (uhr), we computed the stellar and DM intrinsic shapes of 690 simulated galaxies with stellar masses above 2 × 1010 M⊙ at three different radii with an iterative unweighted method. We also determined their morphologies, their projected morphological and kinematic parameters, and their fractions of in situ formed stars. Results. The DM follows the stellar component in shape and orientation at three half-mass radii, indicating that DM is heavily influenced by the baryonic potential in the inner parts of the halo. The outer DM halo is independent of the inner properties such as the DM shape or galaxy morphology, however, and is more closely related to the large-scale anisotropy of the gas inflow. Overall, DM halo shapes are prolate, consistent with previous literature. The stellar shapes of galaxies are correlated with their morphology, with early-type galaxies featuring more spherical and prolate shapes than late-type galaxies out to 3 R1/2. Galaxies with more rotational support are flatter, and the stellar shapes are connected to the mass distribution, though not to the mass itself. In particular, more extended early-type galaxies have larger triaxialities at a given mass. Finally, the shapes can be used to better constrain the in situ fraction of stars when combined with the stellar mass. Conclusions. The relations between shape, mass distribution, and in situ formed star fraction of galaxies show that the shapes depend on the details of the accretion history through which the galaxies are formed. The similarities between DM and stellar shapes in the inner regions of galaxy halos signal the importance of baryonic matter for the behavior of DM in galaxies and will be of use for improving the underlying assumptions of dynamical models for galaxies in the future. However, at large radii the shapes of the DM are completely decoupled from the central galaxy, and their shapes and spin are coupled more to the large scale inflow than to the galaxy in the center.

Features of the formation of natural-technical systems within the injection areas of the Daldyn kimberlite field (Western Yakutia)

Relevance. Primary diamond deposits mined by PJSC ALROSA in Western Yakutia have widespread watering of varying intensity with sub-permafrost and inter-permafrost strong and very strong brines. Currently, the resulting drainage waters are pumped back into the depths of the permafrost, which leads to the formation of natural-technogenic aquifers that require constant control, monitoring and study. Aim. Identification of the features of the formation of natural-technogenic systems within the injection areas of the Daldyn kimberlite field, determination of the volume of permafrost waters involved in this process, as well as their characteristics to confirm the environmental friendliness of the method of handling drainage brines. Methods. Field work consisted of quarterly routine testing of observation and injection wells. Assessment of technogenic influence, as well as subsequent forecasting of the dynamics of changes in cryohydrogeological conditions and hydrodynamic regime, was carried out using modeling methods in Feflow software. An additional method was analytical balance calculation based on the parameter of changing mineralization. Results and conclusions. The interaction of drainage waters with permafrost waters leads to areal changes in mineralization without significant changes in the dominant anions and cations within the studied objects, forming an equilibrium natural-technogenic system in the peripheral parts relative to background temperature conditions. The volume of formed man-made aquifers will exceed 300 million m3. The process of involving cryogenic waters, despite the significant volumes, has a positive impact on the ecological state of the territory, because in the peripheral parts of the resulting aquifers, equilibrium cryohydrogeological conditions are formed, characteristic of natural cryopegs in the region of study.

THE ESSENCE AND SCOPE OF UKRAINE'S VICTORY IN THE CONTEXT OF RUSSIA’S TOTAL WAR AGAINST THE UKRAINIAN STATE

The purpose of this article is to clarify the essence and scope of Ukraine's victory in the context of Russia's total war against the Ukrainian state. A system-situational approach, historical analysis, and modeling methods were employed to address the research questions. The article demonstrates that a military victory in the physical realm becomes less significant than its representation in virtual space, where it can be shaped through propaganda in the public consciousness. The factors influencing the dynamics of scientific and official discourse regarding perceptions of victory in modern warfare are identified. It has been established that the essence of Ukraine's victory in the total Russian-Ukrainian war is as follows: in the military context – the defeat of Russia and Ukraine's achievement of the political and military-strategic objectives of its national and just war; in the political context – subordinating Russia to the goals and objectives of Ukraine's diplomatic and strategic efforts aimed at restoring its territorial integrity; and in the praxeological context – Ukraine's achievement of a peace superior to that which existed before the war. Ukraine's victory in the total Russian-Ukrainian war, in substantive terms, consists of: in the military context – the defeat of Russian armed forces and the cessation of hostilities within Ukraine's borders; in the political context – the restoration of Ukraine’s territorial integrity; and in the international political context – achieving victory with guarantees of state sovereignty, territorial integrity, and the inviolability of borders from NATO and the EU, as well as securing peace without Russia's geopolitical dominance, a peace based on the rule of international law rather than the rule of force.

К совершенствованию технологии планирования транспортных процессов взаимодействия железнодорожных линий различных категорий

Purpose: increasing the efficiency of the transport network based on the developed balanced system of indicators for the interaction of railway lines of various categories. Methods: methods of control theory, system analysis, synthesis and mathematical modeling, theory of transport processes and systems, and transport planning methods are used. Results: a set of indicators that influence the interaction of railway lines of various categories is substantiated, based on a balanced system of indicators, which makes it possible to develop corrective measures to optimize the transportation process. Practical significance the implementation of the proposed solutions allows us to develop and specify a program to increase the efficiency of operation of low-intensity public railway lines and adjust scheduled transportation on these lines.

Network technologies as a tool for self-presentation of small religious groups (the case of Buddhism new for Russia)

This article analyses the results of a long-term study of the strategies of the Russian-speaking Buddhist community in relation to the Internet and new media. The authors present the results of a study of two cases of Buddhist digital innovators - communities of the Russian-speaking branch of the International Dzogchen Community and Russian Theravada. The authors chose these two cases for analysis, since they are representative for the sociological consideration of the multidimensional connections between online and offline communications of Russian digital Buddhism. The subject of the study seems to be especially relevant, since in modern sociology more and more attention is paid to the study of the influence of media technologies, the Internet and social media on religious traditions, practices and forms of social organisation of religion. The reason for this is the deepening process of mediatisation of modern societies. It has brought with it fundamental transformations of various spheres of social activity. Religions are involved in the process of mediatisation, that poses a number of serious questions that require new answers unknown to their traditions. Thus, the digitalisation of sacred texts brings with it the devaluation of the tradition of canonical knowledge and interpretation of sacred texts. In this complex context of reformatting social reality, Russian traditional religions are developing their own reflections and responses to the risks of mediatisation. On the Russian-language Internet, Orthodoxy, Islam, Judaism and Buddhism are represented by a rich variety of websites of the corresponding religious communities and centres of various trends, podcasts, channels, publics, groups and blogs on social networks. And, as the results of sociological research demonstrate, the discussion of the correlation of the value-normative coordinates of everyday life with religious-doctrinal regulation serves as the semantic core of trans-local religious communications mediated by media. New digital media challenge religious traditional values, since they provide space and ways of forming new religious authorities, new ways of influencing a certain religious group. The study was conducted using a methodology developed by the authors, combining methods of mathematical modelling and qualitative sociology. The authors conclude that, paradoxically, non-traditional Buddhist organisations in Russia have adopted an innovative strategy and have proven to be very creative in attracting new media and promoting their own presentations in the public space. And the directions of Buddhism that have been institutionalised in the past 30 years, on the contrary, are conservative.

ІННОВАЦІЙНІ ЄВРОПЕЙСЬКІ ПРАКТИКИ ДИДЖИТАЛІЗАЦІЇ ЯК КЛЮЧОВИЙ ЧИННИК ЗАБЕЗПЕЧЕННЯ СОЦІАЛЬНО-ЕКОНОМІЧНОЇ БЕЗПЕКИ В УМОВАХ ГЛОБАЛЬНИХ ВИКЛИКІВ

The purpose of the article is to develop a concept for implementing innovative European digitalization practices as a key factor in ensuring socio-economic security in the context of global challenges. Using the methods of risk-oriented approach, system analysis and modelling, the ways of using innovative European digitalization practices are considered. Research results: 1. The problems that arise in the modern analysis of the relationship between digitalization and socio-economic security are diagnosed. 2. The directions of technology development that can significantly improve socio-economic security are analyzed. Global challenges, such as pandemics, economic crises, and climate change, can significantly affect the processes of digitalization and their impact on socio-economic security. The scientific novelty of the study is that European digitalization practices are an important tool in ensuring socio-economic security in the face of global challenges, and therefore it is important to explore the latest approaches that can be offered for sustainable development.It has been proven that AI can analyze large amounts of data to predict economic crises, natural disasters, or social conflicts, allowing governments and companies to prepare for possible challenges in advance; using AI to optimize production, logistics, and resource management helps reduce costs and increase economic resilience. Cloud solutions provide access to important resources and services from anywhere in the world, which ensures the uninterrupted operation of businesses and government agencies in case of emergencies. Cloud platforms provide effective management of large amounts of data, which is necessary for strategic decision-making. The practical significance of the study is to overcome key challenges and use opportunities for further implementation of innovative European digitalization practices as a key factor in ensuring socio-economic security in the face of global challenges. The use of innovative European practices has a significant potential to accelerate the achievement of the SDGs but requires taking into account possible risks and challenges.

Проблемы поиска и преследования беспилотных летательных аппаратов с использованием теоретико-игрового подхода

Currently, the application of mathematical modeling theory is widely used in various fields and, among other things, can be used to prevent illegal actions. This study examines scenarios involving a single pursuer tracking a single evader, as well as situations involving multiple pursuers pursuing multiple evaders. The authors formulate this problem as a search and pursuit problem for four-rotor unmanned aerial vehicles (UAVs) or quadcopters. The solution to the problem is based on game theory, as it provides a mathematical framework for modeling and studying strategic interactions involving multiple decision makers. The authors consider a game where the set of strategies of the runner is the set of possible combinations of speeds and directions of his movement, and the set of strategies of the pursuer is the set of all possible permutations of the elements of his speeds. The matrix of the resulting game consists of elements that are the time of capture. A mathematical problem on the optimal assignments of searching and pursuing unmanned aerial vehicles is formulated and solved. Purpose: optimizing the effectiveness of strategies for detecting and capturing four-rotor unmanned aerial vehicles (quadcopters). Methods: methods of mathematical modeling, game theory apparatus, Hungarian method of solving the problem of assignments, decision theory, the principle of dynamic programming, the Maple package for solving examples were used. Results: In order to fulfill the conditions for solving the problem of optimal assignments, it is necessary and sufficient that it is balanced. This assignment problem can be balanced by entering the required number of fictitious boats or escaping boats. After that, it is possible to formulate and solve the dual problem of optimal appointments. The resulting game can be solved by any method of solving matrix games. In this way, it is possible to determine the policy of harassment and search between drones. Practical importance: All escaped quadcopter UAVs can be chased and successfully intercepted using the developed models. Examples of the study of mathematical models using the Maple software package are given.

The specifics of integrating distance learning technologies with traditional classroom instruction: How to design educational curricula in modern education?

The active and widespread transition to distance learning modalities was prompted by the COVID-19 pandemic and is currently considered a prospective paradigm for the development of the education system. This shift necessitates the creation of corresponding curricula and programs capable of fostering student competencies through online and blended learning. The purpose of this research is to examine the specifics and formulate an algorithm for the development of educational programs incorporating distance and blended learning technologies. Analytical, sociological (utilizing semi-structured surveys administered to 96 educators and 128 students on the subject of distance and blended learning and its methodological support), modeling, and mathematical-statistical methods (Student's t-test and analysis of variance ANOVA) were employed in this study. The survey confirmed the hypothesis of differences in attitudes toward distance learning among students and educators. Educational needs and challenges were identified, leading to the development of an algorithm for designing educational programs integrating distance and blended learning technologies. An algorithm has been devised for the development of educational programs based on the integration of distance and blended learning technologies. The study underscores the importance of continuity across educational levels and the early cultivation of distance learning skills. Prospective research endeavors are intended to be directed toward the examination of the organization of distance and blended learning for students with special educational needs.

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

Interfaces, Free Boundaries and Geometric Partial Differential Equations

Partial differential equations arising in the context of interfaces and free boundaries encompass a flourishing area of research. The workshop focused on new developments and emerging new themes. At the same time also new interesting results on more traditional areas like, e.g. regularity theory and classical numerical approaches have been addressed. By convening experts from various disciplines related to modeling, analysis, and numerical methods concerning interfaces and free boundaries, the workshop facilitated progress on longstanding open questions and paved the way for novel research directions.

Mathematical modeling of pollution of underground aquifers due to mining of minerals

Purpose. The research aims to create a mathematical model of salt contamination spreading through underground aquifers in the event of depressurization of the hydrocarbon production well crater for further assessment of environmental and economic damage from these processes. Methods. To predict the environmental and economic damage from salt contamination, the distribution of concentrations of harmful substances was investigated, taking into account the number of supply sources and their intensity over time, based on situ studies at the Rybalske Oil Field, Okhtyrskyi District of Sumska Oblast in Ukraine, where there were technological failures wells, accompanied by open fountains with the release of large amounts of highly mineralized water and the formation of craters. Mathematical modelling methods were used to process the data from the study of accidental technogenic pollution of underground aquifers. Findings. Based on real data from the study of the processes of potential salt contamination spread in fresh aquifers as a result of accidents at hydrocarbon production facilities, a mathematical model of salt contamination spreading in drinking groundwater in the event of depressurization of an oil field well crater has been developed. Potential economic losses in case of possible groundwater contamination with highly mineralized solution, which can into drinking groundwater aquifers, are substantiated. It has been established that in connection with the occurrence of an emergency situation due to the release of formation water to the surface in the territory of oil and gas fields, the formation of technogenic meromictic reservoirs is possible, which is confirmed by the example of the Rybalske Oil Field. It is proved that the total mineralization of crater water increases linearly with depth of the reservoir occurrence, and a similar dependence is characteristic of the chloride ion content. Originality. For the first time, a multicomponent mathematical model of mineral salt migration processes in underground freshwater aquifers in the case of depressurization of a meromictic reservoir has been developed. Practical implications. The research results obtained using numerical methods make it possible to predict the processes of spreading harmful substances in drinking underground aquifers as a result of emergencies at oil and gas fields, taking into account the number of sources of pollutants penetrating the study area, the heterogeneity of properties of the environment into which the harmful substance enters, and to assess the dynamics of changes in the concentration of these substances and time with further assessment of environmental and economic damage from these processes.

МОДЕЛИРОВАНИЕ ПРОЦЕССА ПРОГНОЗИРОВАНИЯ ДЕФИЦИТА ВЫЧИСЛИТЕЛЬНЫХ МОЩНОСТЕЙ НА ЗНАЧИМОМ ОБЪЕКТЕ КРИТИЧЕСКОЙ ИНФОРМАЦИОННОЙ ИНФРАСТРУКТУРЫ

The aim of the study is to formalize the process of forecasting the computing power deficit at significant objects of critical information infrastructure using a functional model. The task is to create a correct functional model for forecasting the computing power deficit. To solve this problem, the BPwin software product and functional modelling methods are used. The novelty of the work lies in using the analytical alignment of dynamic series to forecast the computing power deficit at significant objects of critical information infrastructure. As the study result, the authors build a functional model of the process of forecasting the computing power deficit at significant objects of critical information infrastructure. Thus, the resulting functional model reflects the technological features of the process of forecasting the computing power deficit and can be automated and applied to significant objects of critical information infrastructure in the future.