The study examines the complex processes of intelligent management of energy resilience in modern healthcare facilities during critical situations, large-scale failures, and prolonged outages of the external centralized power supply. The aim is to develop a comprehensive mathematical model and a Safe Deep Reinforcement Learning (Safe DRL) method that ensures guaranteed compliance with the strict physical and operational constraints of hospital energy systems, even during the intensive training phase of a neural network agent. The objectives are: to formalize in detail the decision-making procedure in an energy system in detail by transitioning to the paradigm of Constrained Markov Decision Processes (CMDP); to develop an innovative mathematical model featuring the implementation of a specialized safety layer based on Lyapunov functions; and to ensure high resilience and autonomy of the system through the implementation of a decentralized Edge-Fog data processing architecture. The methods used include: the theory of Constrained Markov Decision Processes (CMDP), deep reinforcement learning methods based on the Actor-Critic architecture, the mathematical apparatus of Lyapunov stability theory for the analytical correction of actions, and methods of simulation modeling for complex dynamic energy systems. The following results were obtained. In the course of the study, a Safe DRL method was proposed and substantiated, which integrates a Lyapunov-based projection directly into the training loop for the immediate correction of the agent’s control actions. This makes it possible to ensure strict theoretical guarantees of maintaining the required State of Charge (SoC) of battery systems and to prevent critical violations of energy system parameters beyond established safety limits, which is crucial for patient life-support. The effectiveness of the proposed approach was confirmed by a series of numerical experiments in the specialized environment, HospitalEnergyEnv. Under a full blackout scenario, the agent demonstrated adaptability and high accuracy in resource management without any violation of the established physical limits during the entire process of autonomous operation. Conclusions. The scientific novelty of the obtained results lies in the following: the existing optimization model for building energy management systems (BEMS) has been improved by introducing an analytical safety projection mechanism, which minimizes the risks of emergency equipment shutdown during the adaptation of artificial intelligence algorithms; further development of decentralized control methods for critical infrastructure based on Edge-Fog computing has been achieved, which significantly increases system fault tolerance in the event of a loss of connection with the global network and ensures obtaining quasi-optimal solutions in high-dimensional problems. The practical value of this work lies in the potential to create highly reliable autonomous energy systems for critical infrastructure facilities.

The subject of this research is ontology-oriented approaches to extracting business information from unstructured web sources. The aim of the article is to analyze the effectiveness of modern ontology-oriented approaches for extracting business information from unstructured web sources and to substantiate their feasibility for decision support systems. Such approaches are particularly important for the information and analytical support of aerospace enterprises, where activities require processing significant volumes of heterogeneous external information on cooperative relations, supplies, technical product support, regulatory requirements, and the market environment. Tasks include: analyzing the main challenges of collecting, mining, and processing business information from unstructured web sources; determining the feasibility of using ontologies to extract and integrate business information; and performing a comparative analysis of modern ontology-oriented approaches and identifying promising areas for future application. The study employed methods for analysis and generalization scientific sources, systems analysis, and comparative analysis, along with approaches to semantic and ontological modeling. The findings establish that the main factors complicating web-based business information extractions are the heterogeneity of data formats and structures, the ambiguity of natural language, the dynamism of the information environment, and the incompleteness or inconsistency of information. It is demonstrated that the use of ontologies enables the semantization, structuring, logical coordination, and integration of business information within a corporate knowledge base, while providing a foundation for improving the quality of analytical data processing. Modern ontology-oriented approaches are categorized into template-based methods, deep linguistic analysis, and machine learning. A comparative analysis reveals that hybrid approaches, which combine the advantages of various methodologies to ensure greater completeness, flexibility, and semantic consistency, are the most promising for decision support systems. Conclusions. The scientific novelty of the obtained results lies in the generalization and comparison of modern ontology-oriented approaches to extracting business information from unstructured web sources, considering their suitability for semantic data coordination, the formation of corporate knowledge bases, and their application in decision support systems.

The article focuses on the application of additive technologies in the design and manufacturing of aviation structural elements under modern requirements for strength, weight reduction, reliability, and economic efficiency. The goal of the study is to develop and substantiate an integrated analytical approach to optimizing additive manufacturing processes in aircraft production. The tasks addressed include: analyzing of current additive technologies in aerospace engineering; developing mathematical models of thermomechanical processes and porosity formation; optimizing of printing parameters and material composition; and conducting a comparative evaluation of traditional and additive manufacturing methods. The methods employed comprise the finite element method, thermomechanical and porosity modeling, multi-criteria optimization, and experimental investigation of the mechanical properties of metallic and polymer materials. The following results were obtained: mathematical models for predicting thermal deformation, structural heterogeneity, and strength characteristics were developed; the influence of process parameters on microstructure formation and defect minimization was determined; it was established that the optimization of printing parameters reduces material consumption by up to 30%, decreases product weight by 10–25%, reduces production time by a factor of 2–3, and lowers costs by up to 40%. A tensile strength of up to 1260 MPa was achieved for SLM-manufactured titanium components. In addition, the proposed integrated modeling approach enabled quantitative prediction of porosity levels and residual stress distribution, improving dimensional accuracy and structural reliability of critical aviation components. Comparative analysis confirmed that additive technologies demonstrate the highest efficiency in manufacturing geometrically complex and weight-critical parts, where traditional methods are limited in design flexibility and material utilization efficiency. Conclusions. The scientific novelty lies in the development of an integrated modeling and optimization framework for the additive manufacturing of aviation components, ensuring improved structural integrity, reduced defect levels, enhanced production efficiency, and increased competitiveness in aviation manufacturing.

The subject of the article is the processes of the automated analysis of server logs of software systems using Natural Language Processing methods. The goal is to develop an approach to improve the efficiency of log analysis by applying NLP algorithms and text compression methods. The tasks to be solved are: to study the features of logs as a source of technical information; to analyze modern methods of text data processing, including statistical, vector-based, and neural network approaches; to justify the feasibility of using NLP algorithms for automating log analysis; to substantiate the use of text compression methods to reduce data volume; and to develop a concept and architecture of an automated log analysis system. The methods used include: Natural Language Processing techniques such as TF-IDF, Word2Vec, FastText, and transformer-based models; text preprocessing methods; log compression and log template extraction approaches; and information retrieval methods applied to text corpora. The following results were obtained. A concept of an automated server log analysis system based on NLP methods and text compression algorithms was developed. A generalized system architecture was proposed, including modules for log collection, storage, filtering, preprocessing, text compression, NLP analysis, incident representation, and solution search. An algorithm for system operation was developed, providing step-by-step log processing while taking into account the textual nature and large volume of data. It was established that the use of NLP methods improves the accuracy of error detection and incident classification, while text compression reduces the computational load and increases system performance. Conclusions. The proposed approach improves the efficiency of software diagnostics, reduces the time required for error detection and resolution, and decreases the workload on developers. The scientific novelty of the obtained results lies in the following: an approach to automated log analysis combining NLP methods and text compression techniques was developed; a generalized architecture of a log analysis system that considers the specifics of unstructured textual data was proposed; existing log processing methods were further developed through the integration of modern NLP models with data volume optimization stages, which improves analysis efficiency in conditions of large-scale information flows

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

Modern warfare has changed the understanding of strategy, tactics, and the use of new types of equipment, both in terms of operational-tactical actions and new technologies in military systems. The emergence of UAVs of various types has enabled their use not only on the battlefield but also deep behind enemy lines. However, to achieve parity with the adversary in military operations, it is necessary to ensure the rapid implementation of innovations in the development of new military equipment, with the ability to scale and mass-produce the required volume of weaponry. Therefore, research on establishing a new set of design activities that will reduce the development the life cycle (LC) of military equipment development is relevant. The subject of the study is the development of analytical and simulation models that make it possible to analyze and plan the necessary project activities to reduce the LC of high-tech products (UAVs). The purpose of the study is to create a set of mathematical and simulation models that enable the formation of a new product architecture, the optimization of key project indicators, and defining project activities aimed at LC reduction under limited capabilities. The tasks addressed include: systematically analyzing the LC of developing a high-tech product with a focus on its reduction; creating a new method for forming the product architecture using a component-based approach; establishing parallel design processes that will ensure LC reduction; optimizing the time, cost, and risks of project activities under resource constraints; modelling of the proposed activities for LC reduction in the development of a high-tech product (UAV). The research utilizes a range of mathematical methods and models, including: system analysis for establishing the sequence of project activities that ensure LC reduction; a model of component-based representation of the new product architecture; a method for selecting a rational design variant using lexicographic ordering; a model for optimizing time and cost under acceptable project risks; agent-based simulation of the sequence of project activities to ensure LC reduction for new equipment development. The following results were achieved: a system representation of project activities for LC reduction was proposed; a component architecture of the new product was substantiated; the basic architecture was adapted to the requirements of developing new equipment; a set of possible design variants for creating new equipment was generated, with the selection of a rational one; time and cost optimization was conducted within acceptable levels of project risk; and a simulation of the sequence of project activities was performed. Conclusions: the proposed approach makes it possible to justify project activities for reducing the development LC of new equipment, enabling the rapid production of relevant systems under the special conditions of the country. The scientific novelty of the approach lies in the scientific justification of project activities for LC reduction using a component-based method and the parallel execution of design processes, which ensures the rapid development of military equipment and enables the mass production of relevant systems for the frontline.

The embedded computer is a key element of the avionics of both spacecraft and unmanned aerial vehicles (UAVs). Its reactivity determines the system’s ability to ensure that the reaction time to external events meets the specified real-time constraints. This study aims to investigate the reactivity of the microcontroller of the embedded computing system as a component of the overall technical efficiency of the hardware and software complex. The mathematical model of reactivity, as well as methods, tools, and technology for experimental research of the reactivity of a real-time operating system, are the subjects of this study. This study aims to develop and verify a model of the reactivity of embedded computer software for use in nanosatellites and unmanned aerial vehicles. This study substantiated the need to create a set-theoretic model of the reactivity of embedded avionics systems for small spacecraft and unmanned platforms. A constraint analysis (Worst-Case Execution Time, WCET) was performed for typical onboard computer tasks. The requirements for the model were formulated, and its mathematical description and software implementation were developed. Numerical modeling methods were used to verify the simulation results’ compliance in the actual processes in the system. Results. The proposed mathematical model of reactivity considers the stochastic characteristics of event flows and two-stage interrupt processing, allowing the probability of a timely system response to various types of events to be assessed. Experimental verification confirmed the model’s correctness and suitability for analyzing the Boryviter / Falco platform under the control of the FreeRTOS operating system. The scientific novelty lies in the development of a new concept for optimizing the work of the real-time operating system scheduler of UAV and nanosatellites OBC, which is based on a complex mathematical model of energy consumption, computational performance and reactivity of the OBC, which, unlike power consumption control mechanisms in processors with dynamic frequency or voltage regulation, is specifically focused on OBC microcontrollers, in which the frequency and voltage are fixed, and optimization is possible only through the correct choice of the policy for controlling energy-saving modes.Conclusions. The developed model can be used to assess and improve the reactivity of computing systems, including the university nanosatellite Khai-1KA and unmanned aviation systems. Further research is aimed at creating instrumental software for reactivity analysis to support software engineering of new generations of aerospace and unmanned platforms with a high level of autonomy, considering the implementation of AI/ML tools.

The subject of this article is ejector nozzles, which are intended for the thrust augmentation of jet engines. The goal of this study is to analyze the contradictions between technical requirements and the requirements of manufacturability and price, between the required high characteristics of ejectors and compactness, and between the conflicting goals of achieving a high mixing rate with low losses of the total pressure of the primary flow within a short total length. Moreover, this study aims to identify methods for rationally reducing these contradictions by using effective mixers of the primary and secondary flows. The tasks to be solved are: development of a classification, revealing the advantages and drawbacks of thrust augmenting ejector nozzles, and analysis of the main directions in ejector nozzle research (theoretical research based on simplified models, experimental research using improved models, and research using computational fluid dynamics models). The following methods were used: search of corresponding information sources on the Internet and analysis based on operational experience in the aviation branch. The following results were obtained: in terms of information sources, the classification of thrust augmenting ejector nozzles was developed, their advantages and drawbacks were formulated, and the results of available research were analyzed. Investigations of thrust augmenting ejector nozzles for micro-turbojets were analyzed separately. Conclusions. The scientific novelty of the results obtained is as follows: 1) information from numerous sources of literature that clarifies classification features, advantages, and drawbacks of thrust augmenting ejector nozzles, development of these nozzles investigations by theoretical and experimental methods were collected; 2) a very limited number of publications and absence (in open-source literature) of methodology as for designing thrust augmenting ejector nozzles for micro-turbojet engines was discovered. Thus, the necessity of developing a design methodology for thrust augmenting ejector nozzles for micro-turbojets has been justified. The goal and challenges of the following research are outlined.

This study investigates a ducted propfan in reverse mode, considering the influence of air bypass from outside the duct or air leakage after the propfan impeller. The size of the bypass windows and the dependence of the air flow rate through the bypass windows of a ducted propfan in reverse mode when changing the flight speed and the dependence of the reverse thrust of a ducted propfan in reverse mode, considering the influence of air intake from outside the duct or air leakage after the propfan impeller, are the subjects of the study. The study object is a ducted propfan with rotating blades of the impeller of a bypass ratio turbojet engine with an ultra-high bypass ratio (m = 20). The bypass windows are located behind the propfan impeller. The purpose of this study is to study the reverse thrust of a ducted propfan in reverse mode, considering the influence of air intake from outside the duct or air leakage after the propfan impeller. The flow simulation in a ducted propfan with rotating blades of the impeller of an ultra-high bypass ratio turbojet engine with an ultra-high bypass ratio was conducted under standard atmospheric conditions in the reverse mode in the range of the M number from 0.1 to 0.25 for the variants of the absence of bypass windows and with an increase in the relative area of the bypass windows from 0 to 0.63. The dependences of the change in the air flow rate through the bypass windows of the ducted propfan when changing the speed and the reverse thrust characteristics of the ducted propfan in the reverse mode were obtained. The flow visualization in several modes is presented, which confirms the efficiency of air bypass behind the propfan impeller. The scientific novelty and practical significance of the research results lie in the fact that new data were obtained on determining the rational size of the bypass windows and the dependence of the change in air flow rate through the bypass windows of a ducted propfan in reverse mode on the change in speed and thrust characteristics of the reverse of the ducted propfan in reverse mode, considering the influence of air intake from outside the hood or air leakage after the propeller fan impeller.

The mixing chamber of a turbofan jet engine with an additional turbofan unit is the object of this study. The subject of the research is the influence of the bypass ratio on the formation of the temperature field in the mixing chamber before the turbofan unit turbine. This study investigates how the bypass ratio affects the temperature distribution in a turbofan engine’s mixing chamber. Modern aircraft engines’ three-stream architecture improves thrust-specific fuel consumption by combining a powerful gas generator with a turbofan attachment. A critical stage in the operation of such an engine is the mixing process of the hot gas flow after the gas generator turbine with the cold airflow of the second stream, which passes through the fan. The mixed flow is formed and directed to the turbine of the turbofan unit in the mixing chamber, and its thermal uniformity is crucial for ensuring reliable blade operation. Non-uniformity of the temperature field before the turbine leads to significant thermal gradients along the height of the turbofan blades. High-temperature gradients cause additional thermal stresses in the blade material, reducing its mechanical strength. The calculations show that the use of a mixing chamber significantly reduces the gas temperature at the turbofan inlet to the turbine section. This study aims to determine the optimal bypass ratio for different thermal conditions at which the mixing chamber achieves the best temperature field uniformity. To achieve this goal, the numerical simulation method was used. A series of simulations was performed in ANSYS Fluent using the Reynolds Stress Model (RSM) turbulence model. Under constant total pressure, the temperature parameters at the inlets of both streams were varied, allowing a comprehensive analysis of the influence of thermal boundary conditions on mixing efficiency. The dependence of temperature non-uniformity on the optimal bypass ratio was established. The practical value of this study lies in the possibility of optimizing the design of turbofan engine mixers by selecting the optimal bypass ratio (m) and minimizing temperature non-uniformity (σ₁), which ensures reduced thermal loads, improved fuel efficiency, increased thermal efficiency, and extended engine life while maintaining environmental standards.