Yurii Tkachov

This study explores the transformative impact of artificial intelligence (AI) on the aerospace industry, focusing on changes in the qualification profile of professionals, managerial logic, and the need for interdisciplinary training. The research analyzes how the integration of AI, particularly in autonomous unmanned systems and predictive maintenance, reshapes the role of engineers from executors to participants in cognitive interaction with intelligent systems. It is identified that current engineering education lags behind the pace of AI development, posing a risk of losing the industry’s intellectual agency. The study proposes the necessity of radical changes in educational curricula to develop professionals capable of integrating algorithmic thinking, physical intuition, and systems thinking. The theoretical significance of the work lies in systematizing knowledge about the transformation of engineering expertise under AI integration, while its practical value lies in defining requirements for the content of educational programs. The findings can be used to modernize curricula and develop competency standards in aerospace engineering.

The study is dedicated to analyzing the key role of decision support systems (DSS) in modern science and industry, emphasizing their ability to automate complex processes, reduce the risks of erroneous decisions, and efficiently handle large volumes of data. The work explores the advantages of document-oriented databases (DODBs) over traditional relational databases for managing diverse datasets in materials science and aerospace engineering. The application of Elasticsearch (ES), a distributed search and analytics engine, is investigated for optimizing material selection and design processes in the aerospace industry. Attention is also given to the ethical aspects of using software for making critically important decisions and the synergy between advancements in computational technologies and materials science. The integration of DSS, DODBs, and ES demonstrates the potential to enhance the accuracy, efficiency, and reliability of data-driven decision-making.

Purpose: This article aims to analyze and summarize the practical experience of deploying and integrating platforms for open journals, conferences, and repositories with support for the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). It highlights the errors and challenges that journal and repository managers and administrators, as well as conference organizers, may encounter and offers recommendations to prevent and avoid these issues. Design / Method / Approach: The research is based on the analysis of real cases of deploying platforms for open science, including the analysis of errors and difficulties encountered during this process. The methodology includes the analysis of real-world cases, applying methods such as testing, validating the OAI-PMH protocol, observation, and studying regulatory and technical documents. Findings: Key errors and difficulties that organizers may face when creating open journals, conferences, and repositories with OAI-PMH support have been identified. Specific solutions are proposed to ensure the reliable operation of the metadata harvesting protocol (PMH), initial setup, and further integration of these platforms. Theoretical Implications: The study extends the understanding of the problems and challenges in the field of open science, particularly concerning the organizational and technical aspects of platforms with OAI-PMH support. Practical Implications: Practical recommendations are provided for administrators and managers of open journals, conferences, and repositories to avoid common mistakes and effectively use OAI-PMH to ensure data interoperability and accessibility. Originality / Value: The article presents an original analysis of real cases of implementing and integrating platforms with OAI-PMH support, making it a valuable resource for practitioners in the context of deploying platforms for open science. Research Limitations / Future Research: The study is limited to the analysis of specific cases and does not cover all possible aspects of deploying and integrating web applications with OAI-PMH support. Future research may focus on the quantitative analysis of the effectiveness of implementing these platforms and their long-term impact on open science. Paper Type: Case Study / Practitioner Paper.

The rapid digitalization of various societal sectors in Ukraine, including public services, banking, e-learning, and logistics, emphasizes the need for enhanced practical IT competencies among students. Despite the widespread availability of digital devices, educational curricula often lack tasks that provide hands-on experience with configuring networks and operating systems. This study aimed to address this gap by updating the curriculum of the "Information Processes and Methods of Their Algorithmization" course to focus on practical tasks that involve creating and configuring information resources. To achieve this, the Debian operating system was chosen due to its flexibility, low resource requirements, and compatibility with outdated hardware. Key tasks included configuring services for business and home use, such as remote server access, secure file sharing, web hosting, and office environments with essential software like LibreOffice and Thunderbird. Performance metrics, including memory usage and disk space utilization, were evaluated for different configurations of the operating system, demonstrating its efficiency even on low-resource systems. The results highlight Debian as a cost-effective and adaptable solution for both educational and small-scale business needs, fostering practical IT skills while minimizing hardware expenses. In comparison, Ubuntu Server was found to be more advanced yet resource-intensive, making it better suited for corporate environments. This study underscores the potential of open-source solutions like Debian for improving IT education and promoting their broader adoption in academic and business contexts.

Today, the space industry is undergoing a period of significant technological advancement. Continuous progress in additive manufacturing technologies and the adoption of modern materials for 3D printing are driving this transformation. This trend has intensified competition among various space companies—both state-owned and private—each striving to introduce innovative and unique solutions. FlightControl Propulsion, a private space company in Ukraine, is one such example. This study focuses on the design of the power frame for a low-thrust liquid rocket engine. Power frames in rocket engines must withstand significant mechanical loads. The application of topology optimization and high-strength steels enables the development of compact and efficient power frames that are substantially lighter than traditional designs. Additive manufacturing not only significantly reduces the production time of various parts and assemblies but also offers nearly unlimited possibilities for creating complex geometric shapes. What was previously challenging or even impossible to manufacture using conventional methods has now become feasible. This work examines a component fabricated using Selective Laser Melting (SLM) technology. A structured algorithm for topology optimization is described, along with the fundamental principle of the SIMP (Solid Isotropic Material with Penalization) topology optimization method. Numerical modeling of the stress-strain state for the initial design variant is performed using the finite element method (FEM) in a computer-aided engineering (CAE) environment. A computational finite element mesh is generated, and the safety margins of the design are evaluated. Following topology optimization, the final design variant is determined, and numerical modeling of the stress-strain state is repeated for the optimized design. The final design is then prepared for manufacturing and subsequent experimental testing.

High-manganese steel is characterized by a stable austenitic structure over a wide temperature range and the ability to strengthen during mechanical deformation. However, factors such as unstable mechanical properties in the initial state, susceptibility to thermal embrittlement, and poor machinability hinder its widespread use. This study investigates the effect of temperature-time parameters on the structure and properties of 9Г28Ю9МВБ steel. It was found that during slow cooling from temperatures of hot deformation, a large number of coarse phase particles of gray-blue color are precipitated in the steel, leading to a sharp decrease in impact toughness. It has been established that in the temperature range of 500-800 °C, the decomposition of the solid solution occurs with the precipitation of particles of the strengthening K-phase - (Fe,Mn)3AL,Cx. Simultaneous decrease in hardness and impact toughness in the temperature range of 750-950 °C contributes significantly to improved machinability. The lowest values of mechanical properties are achieved during aging at 700 °C, with the maximum improvement in machinability compared to other aging temperatures. For example, with an increase in the temperature of isothermal holding at 650 °C for 35 hours, the machinability improves by almost 1.7 times, and at the same duration at 700 °C - by 2.4 times. With an increase in the holding temperature to 950 °C for 35 hours, the machinability of the steel increases only by 1.6 times, which is due to less intensive decomposition of the supersaturated solid solution. Studies of the effect of annealing temperature on the uniformity of austenitic steel allowed to develop a heat treatment regimen for 9Г28Ю9МВБ austenitic steel, which includes heating to 1250°C, holding for 2 hours at this temperature, and subsequent cooling in water to fix a homogeneous supersaturated solid solution. After such heat treatment, the stripe-like microchemical inhomogeneity is significantly reduced, and the anisotropy coefficient does not exceed 1.1 units for all types of steel products.

This paper explores the potential of a decision support system for aerospace design that integrates semantic vector search and transfer learning. The study investigates the application of transfer learning to adapt pre-trained neural networks for aerospace-specific datasets and examines how semantic vector search can transform complex design data into high-dimensional vector representations to reveal latent relationships. Various supervised and unsupervised learning approaches are evaluated to address different phases of the aerospace lifecycle. Preliminary validation on both synthetic and real-world datasets indicates promising improvements in design analysis efficiency and predictive accuracy, laying a foundation for further development of AI-based decision support systems in aerospace engineering.

"Technological Foundations of Equipment Selection in Machine-Building Workshops" is a comprehensive resource designed to explore the key principles and methodologies involved in selecting appropriate equipment for machine-building industries. This book provides an in-depth analysis of organizational and technical aspects crucial for the construction of flexible manufacturing systems (FMS), a critical component in modern industrial operations. The text addresses the importance of automation in production processes, offering insights into the various types of automation systems, their classification, and their impact on the efficiency and flexibility of manufacturing operations. The first part of the book delves into the characteristics and methods of automating production processes, examining the significance of flexible manufacturing systems in the context of modern mechanical engineering. The discussion includes the evaluation of production types, from single-unit to mass production, and the corresponding selection of automation tools suited to each type. The book outlines essential techniques for designing machine-building workshops, including pre-project works, technical specifications, and sequential stages of workshop planning and implementation. In the second part, the book focuses on the key components of flexible manufacturing systems, providing a detailed classification of machine tool systems and industrial robots, which are integral to the functioning of FMS. The text emphasizes the characteristics of industrial robots and transport-accumulating systems, as well as the specific criteria for selecting technological devices. Additionally, it examines the storage systems and management structures needed to support FMS, highlighting the role of information systems and control principles that ensure effective operation. This book serves as a valuable guide for engineers, production managers, and students involved in the design, implementation, and optimization of manufacturing systems, offering both theoretical knowledge and practical insights for improving the technological capabilities of machine-building workshops.

The article provides a systematic review of the PGML concept as a new approach to modeling complex engineering problems in aerospace engineering. Particular attention is focused on how the integration of physical laws with machine learning algorithms transforms traditional approaches to analysis, design, and operation of structures. The principles underlying PGML are examined: modification of the loss function to account for residuals of physical equations, constructive embedding of symmetries into the model structure, and the development of a computational space based on physical invariants. The methodology of this work is based on the analysis of four aerospace-related problems: material fatigue prediction, flutter prediction—an aerodynamic phenomenon characterized by unstable self-oscillations of structures—design of thin-walled structures, and structural condition monitoring. Approximately 30 peer-reviewed sources were analyzed, enabling the identification of key directions for implementation in the design and analysis of the aerospace industry. A limitation of the PGML analysis is the limited number of published sources, which may be somewhat superficial. Key PGML implementations — namely PINNs, GAPINN, NSFnet, multi-fidelity, and hybrid models—and their effectiveness in cases of limited data samples are analyzed. Attention is also given to model interpretability issues, including the application of explainable ML, symbolic regression, and Bayesian approaches for solution verification in critical scenarios. The main challenges for widespread PGML adoption are identified: balancing physical and statistical components, scalability limitations in multiphase environments, certification complexity, and instability in solving inverse problems. The study concludes that PGML transcends being a mere application of machine learning and emerges as a new epistemological platform for formulating engineering hypotheses.

Improving the characteristics of industrial alloys, particularly their corrosion resistance, is a relevant task for both metallurgists and materials science specialists. The implementation of new technologies and the selection of materials for specific operating conditions stimulate the development of technological methods for altering the characteristics of base alloys. The investigation and application of new effective modifiers and modification technologies represent an important research direction. Under certain operating conditions, aluminum alloys, particularly the Al-Zn-Mg-Cu system, are subjected to significant corrosive influences, which negatively affect their mechanical properties and longevity. Therefore, the challenge lies in the necessity to develop new approaches and technologies. Specifically, this study proposes a method for modifying the alloys using titanium carbide powder and investigates the effect of this modification on the corrosion resistance of the studied alloy. The aim of the work is to establish the relationship between the structure, physical-mechanical properties, and corrosion resistance. To achieve this goal, modification of alloys V93 and V95 with titanium carbide powder was carried out, and the microstructures and corrosion resistance before and after modification were studied. Corrosion tests, including laboratory methods, encompassed the determination of general, intergranular, and corrosion cracking, which allowed for the assessment of the effectiveness of the proposed modification method. The results obtained confirmed the effectiveness of modifying the studied alloys with titanium carbide of 15 µm particle size. Observations of corrosion processes indicated a reduction in overall corrosion, with the area of corrosion sites decreasing from 70% to 50% after modification. There was also a significant reduction in intergranular corrosion in alloy V95, indicating an improvement in its corrosion resistance. In both modified alloys, intergranular corrosion was not observed, and corrosion cracking of the base metal was absent during the 60-day test period. The test results indicate an increase in corrosion resistance after modification.

This analytical study aims to synthesize recent advances in Physics-Informed Neural Networks (PINNs) and assess their applicability for solving physical partial differential equations in aerospace engineering. The analysis integrates findings from benchmark problems such as flow over a cylinder, the NACA0012 airfoil, and the inverse Burgers’ equation, highlighting methodological developments including gradient-enhanced and volume-weighted formulations, adaptive sampling, transfer learning, and geometry-aware or isogeometric extensions. The results demonstrate that volume-weighted PINNs achieve 1–3% accuracy compared with reference CFD solutions and reduce viscosity estimation errors to approximately 1.5% in inverse problems. These findings confirm the theoretical potential of PINNs to unify physical modeling, data assimilation, and inverse analysis within a consistent computational framework. At the same time, practical deployment remains limited by the representational capacity of standard architectures in high-compressibility and multiscale regimes. Addressing these challenges requires the use of multi-frequency and weak-form neural formulations, as well as verifiable error estimates and generalization metrics for realistic geometries. Thus, the study provides both a systematization of the state of the art and a roadmap for developing physics-grounded, data-efficient modeling techniques in future aerodynamic design and simulation.

This paper presents a concise analytical review of selected case studies on the integration of multiphysics methods in aerospace structural design, with emphasis on rocket and high-speed vehicle applications. The review synthesizes peer-reviewed publications (2022–2025) indexed in Web of Science and Scopus that address coupled Computational Fluid Dynamics—Finite Element Method (CFD–FEM) analyses, fluid–structure interaction (FSI), conjugate heat transfer (CHT), ablation and thermal protection modeling, and multidisciplinary design optimization (MDO) incorporating surrogate and machine-learning accelerants. Source selection and evaluation prioritized methodological transparency, relevance to structural design tasks, and applicability of results to engineering practice. The surveyed studies indicate that fully coupled CFD–FEM and FSI formulations are capable of reproducing key interactions among aerodynamic, thermal and structural fields, while posing significant numerical and computational demands related to mesh compatibility, stiffness of governing equations and temporal integration. CHT approaches yield improved estimates of interfacial heat fluxes for nozzles and TPS elements when turbulent flow models are combined with wall conduction models. Multidimensional ablation models provide mechanistic insight into material removal processes but require careful treatment of thermochemical coupling and validation against experimental data. Recent developments in multifidelity optimization and gradient-enhanced surrogate models demonstrate potential to reduce computational cost in MDO workflows without discarding essential physics. Given the limited scope inherent to a concise analytical review, conclusions are presented as informed recommendations rather than exhaustive generalizations. Recommended practical measures for engineering application include: explicit consistency of boundary and initial conditions across disciplinary solvers; use of adaptive local mesh refinement in regions of strong gradients; adoption of hybrid coupling strategies (e.g., weak/iterative coupling, multiscale and multifidelity frameworks) to balance accuracy and cost; and integration of surrogate or data-driven models into optimization loops with rigorous uncertainty quantification and targeted validation for TPS, nozzle and thin-walled components. These recommendations aim to support practitioners and researchers seeking to improve predictive capability and computational efficiency in multiphysics design procedures for aerospace systems.

This paper provides a brief review of artificial intelligence (AI) methods for sustainable aerospace systems, focusing on predictive and generative models that enable innovation in Industry 4.0 and Industry 5.0. Predictive AI models are analyzed in terms of their capacity to estimate remaining useful life (RUL), optimize maintenance planning, and enhance safety management of critical aerospace components, such as turbofan engines and aircraft bearings. Generative models, including GANs, VAEs, and diffusion-based approaches, are examined as enablers of aerodynamic design optimization, structural reliability assessment, and digital twin integration, significantly reducing costs of physical testing and accelerating certification. The review highlights how physics-informed neural networks (PINNs) integrate physical laws into machine learning, ensuring reliability and interpretability in real-world aerospace applications. Key challenges such as data scarcity, algorithmic transparency, and compliance with certification standards are addressed, alongside perspectives for hybrid AI approaches combining symbolic reasoning, neural networks, and digital twins. The findings demonstrate that the integration of predictive and generative AI methods not only supports sustainable development goals by reducing emissions, resource consumption, and downtime, but also strengthens the competitiveness of the aerospace sector in the European digital transition.

Purpose. This study aims to develop a structured four-tier taxonomy that systematically organizes aerospace engineering tasks suitable for the application of Physics-Informed Neural Networks (PINNs), while validating this classification through a literature review and identifying opportunities for future research. Design / Method / Approach. The methodology involves grouping tasks into four distinct tiers—Physical Modeling, Dynamic Analysis, Functional Assessment, and System-Level Assessment—based on their physical, operational, and systemic characteristics. This framework is subsequently populated with real-world examples derived from the analysis of 145 peer-reviewed studies. Findings. The reviewed literature confirms a balanced distribution of PINNs applications across all tiers. Contrary to initial assumptions, studies were identified even in areas previously presumed underrepresented, such as acoustic modeling, optical simulations, and environmental impact assessment. This outcome reveals the broader applicability of PINNs and calls for a reassessment of current assumptions regarding underexplored domains. Theoretical Implications. The proposed taxonomy offers a coherent framework for structuring interdisciplinary PINNs applications by integrating physics-based modeling with machine learning across aerospace engineering contexts. Practical Implications. It provides engineers and researchers with a practical roadmap for selecting PINNs methods tailored to specific problem types, potentially improving computational efficiency and enhancing predictive accuracy in aerospace design and analysis. Originality / Value. The study’s originality lies in its empirically validated, four-tier taxonomy that synthesizes the fragmented body of literature on PINNs in aerospace, offering a unified perspective for researchers and practitioners. Research Limitations / Future Research. While the taxonomy covers a wide range of existing applications, future studies should consider extending it with new tiers—particularly related to manufacturing-aware modeling—and pursue methodological standardization to ensure reproducibility and scalability. Article Type. Review.