Romanian Research and Development Institute for Gas Turbines

This review examines the combustion characteristics of hydrogen-enriched natural gas with a specific focus on residential appliances, where safety, efficiency, and emission performance are critical. Drawing on experimental studies, numerical simulations, and regulatory considerations, the paper synthesizes current knowledge on how hydrogen addition influences flame stability, flashback phenomenon, thermal efficiency, pollutant formation, and flame geometry. Results across cooktop burners, boilers, and other domestic systems show that moderate hydrogen blending not only can reduce CO and CO2 emissions and enhance combustion efficiency but also can increase burning velocity, diffusivity, and flame temperature, thereby elevating flashback and NOx risks. The review highlights the blending limits, design adaptations, and operational strategies required to ensure safe and effective integration of hydrogen into residential gas infrastructures, supporting its role as a transitional low-carbon fuel.

Using Machine Learning (ML) in educational management transforms higher education strategy. This study examines students’ views on machine learning (ML) technologies and how they might be used to plan, monitor, and predict student performance. The Faculty of Industrial Engineering and Robotics surveyed 118 third-year undergraduates. It featured closed- and open-ended questions to collect quantitative and qualitative data. Descriptive statistics showed broad patterns, inferential tests (Chi-square, t-test, ANOVA) showed group differences, regression models predicted school outcomes, and exploratory factor analysis (EFA) and clustering found hidden attitudes and student profiles. A multi-method quantitative approach combining descriptive statistics, inferential tests, regression modeling, and exploratory techniques (EFA and clustering) was employed. The findings show that most students realize that ML may help them be more productive, adapt their study pathways, and learn about the future. Concerns remain regarding its accuracy, overreliance, and morality. The findings indicate that ML can both support and challenge educational management, depending on how responsibly it is implemented. Results show that institutions may utilize ML as a strategic tool to boost academic progress and make better judgments, provided they incorporate it responsibly and follow ethical rules and training.

This study investigated the electrochemical deposition of a novel composite polymer, 3-methylpyrrole–sodium dioctyl sulfosuccinate/3-methylthiophene (P3MPY–AOT/P3MTP), as protective coatings on OL 37 steel for anticorrosion applications. The anionic surfactant sodium dioctyl sulfosuccinate used in the deposition process enhances the protective efficiency of the coating. The coating films were characterized by CV (cyclic voltammetry), FT-IR (Fourier-transform infrared spectroscopy), and SEM (scanning electron microscopy). The anticorrosive properties of OL 37 steel coated with P3MPY-AOT/P3MTP were investigated in 1 M HCl using potentiostatic and potentiodynamic polarization, as well as electrochemical impedance spectroscopy (EIS). The coated samples exhibited a corrosion rate nearly ninefold lower than the bare substrate, with protection efficiencies exceeding 90%. Optimal performance was obtained for electrochemical deposition of P3MPY-AOT/P3MTP at potentials of 1.0, 1.2, and 1.4 V, current densities of 3 and 5 mA/cm2, and a molar ratio of 5:3 for 20 min. The influence of electrochemical polymerization parameters—including applied potential, current density, scan rate, cycle number, and monomer ratio—on the anticorrosion efficiency of the coatings was systematically evaluated, allowing the identification of optimal synthesis conditions. Overall, the results confirm that P3MPY-AOT/P3MTP coatings provide highly effective corrosion protection for OL 37 steel in acidic environments.

This paper introduces a gesture-controlled conversational interface driven by a local AI model, aimed at improving accessibility and facilitating hands-free interaction within digital environments. The technology utilizes real-time hand gesture recognition via a typical laptop camera and connects with a local AI engine to produce customized learning materials. Users can peruse educational documents, obtain topic summaries, and generate automated quizzes with intuitive gestures, including lateral finger movements, a two-finger gesture, or an open palm, without the need for conventional input devices. Upon selection of a file, the AI model analyzes its whole content, producing a structured summary and a multiple-choice assessment, both of which are immediately saved for subsequent inspection. A unified set of gestures facilitates seamless navigating within the user interface and the opened documents. The system underwent testing with university students and faculty (n = 31), utilizing assessment measures such as gesture detection accuracy, command-response latency, and user satisfaction. The findings demonstrate that the system offers a seamless, hands-free user experience with significant potential for usage in accessibility, human–computer interaction, and intelligent interface design. This work advances the creation of multimodal AI-driven educational aids, providing a pragmatic framework for gesture-based document navigation and intelligent content enhancement.

This study focused on the thermal stability and ablative behavior assessment of five newly developed composite TPS configurations. All ten test samples were 3D printed via FDM using various fire-retardant thermoplastic materials, with and without reinforcement. Eight samples integrated a new thermal management internal air chamber conceptualized architecture. A prompt feasible approach for the flame resistance preliminary assessment of ablative TPS samples was developed, using an in-house oxy-acetylene torch test bench. Experimental OTB ablation tests involved exposing the front surface samples to direct flame at 1450 ± 50 °C at 100 mm distance. For each configuration, two samples were tested: one subjected to 30 s of flame exposure and the other to 60 s. During testing, internal temperatures were measured at two backside sample contact points. Recorded temperatures remained below 46 °C, significantly under the maximum allowable back face temperature of 180 °C set for TPSs. The highest mass losses were measured for PC and PETG FR materials, achieving around 19% (30 s) and, respectively, 36% (60 s), while the reinforced configurations showed overall only a third of this reduction. The study’s major outcomes were the internal air chamber concept validation and identifying two reinforced configurations as strong candidates for the further development of 3D-printed ablative TPSs.

The promising development of hydrogen and fuel cell technologies has garnered increased attention in recent years, assuming a significant role in industrial applications and the decarbonisation of the shipping industry. Given that the shipping industry generates considerable greenhouse gas emissions, it is crucial and imperative to implement integrated solutions based on clean energy sources, thereby meeting the proposed climate objectives. This study presents the standard hydrogen production, storage, and transport methods and analysis technologies that use hydrogen fuel cells in marine and industrial applications. Technologies based on hydrogen fuel cells and hybrid systems will have an increased perspective of application in industry and maritime transport under the conditions of optimising technological models, developing the hydrogen industrial chain, and updating standards and regulations in the field. However, there are still many shortcomings. The paper’s main contribution is analysing the hydrogen industrial chain, presenting the progress and obstacles associated with the technologies used in industrial and marine applications based on hydrogen energy.

This research activity proposes to produce composite hydrogel–bioactive glass. The primary purpose of this research is to develop and optimize 3D-printed scaffolds using doped bioglass, aimed at enhancing bone regeneration in bone defects. The bioglass, a bioactive material known for its bone-bonding ability (SiO2–P2O5–CaO–Na2O), co-doped with europium and silver was synthesized and doped to improve its biological properties. This doped bioglass was then combined with a biocompatible hydrogel, chosen for its adequate cellular response and printability. The composite material was printed to form a scaffold, providing a structure that not only supports the damaged bone but also encourages osteogenesis. A variety of methods were employed to assess the rheological, compositional, and morphological characteristics of the samples: Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS). Additionally, simulated body fluid (SBF) immersion for bioactivity monitoring and immunocytochemistry for cell viability were used to evaluate the biological response of the scaffolds.

The RANS model is optimized for steady and decelerating axisymmetric turbulent jets.The model was found to be the most suitable candidate for optimization, and two optimized coefficient sets were developed as a result.Each set minimizes the sum of root mean square errors, with one set focused on mean velocities and the other on both mean velocities and Reynolds shear stress.The self-similarity of standard and optimized RANS coefficient sets was then investigated.The optimized sets demonstrated improved self-similarity over the standard coefficient set, showing a close match with a value of 2.2.

Propulsion groups based on gas turbines are subassemblies that must be replaced in the case of the revitalization of a ship of a certain age. Due to the consumption of the vital resource of the ship's old gas turbines, they must be replaced with new turbines that have the same power, but greater efficiency due to the new manufacturing concept. The paper presents the case of replacing an old generation turbine with a modern turbine used in aviation but adapted for marine use. The new turbine together with the related automation was first tested on the stand and then was integrated on the ship instead of the old one. The tests carried out confirmed the fairness of the chosen solution, the power parameters being those expected. In tests the power and consumption of the new propulsion line were compared with the old propulsion line, the results showing a power developed equal to lower fuel consumption.

Scalar dissipation rate (SDR) evolution in a stopping turbulent jet was analysed using direct numerical simulations and a theoretical approach. After the jet is stopped, a deceleration wave for the SDR propagates downstream with a speed similar to that for axial velocity. Upstream of the deceleration wave, mean centreline SDR becomes proportional to axial distance, and inversely proportional to the square of time. After passing of the deceleration wave, normalised radial profiles of SDR and its axial, radial and azimuthal components reach self-similar states, denoted decelerating self-similar profiles, which are different from their steady-state counterparts. Production and destruction terms in the mean SDR transport equation remain dominant in the decelerating self-similar state. The theoretical approach provides an explicit prediction for the radial profile of a turbulent fluctuation term of the mean SDR transport equation. Three turbulent SDR models are validated, and modifications suitable for the decelerating jet are proposed, based on a self-similarity analysis.