machinery
Not a member yet
8395 research outputs found
Sort by
Analysis of Turbulent Swirling Flow in a Pipe downstream of an Axial Fan Impeller: Experimental and Numerical Approaches
No full textExtended abstractMinistry of Science, Technological Development and Innovation of the Republic of Serbia under the Agreement on financing the scientific research work of teaching staff at accredited higher education institutions in 2025, no. 451-03-137/2025-03/200105 and for research staff no. 451-03- 136/2025-03/20010
EVALUATION OF SPECIMEN GEOMETRY FOR RELIABLE MECHANICAL TESTING OF THERMOPLASTIC ALIGNER MATERIALS
No full textA thorough understanding of the variuos mechanical properties of thermoplastic materials is crucial for effective aligner treatment. The appropriate testing parameters and specimen shape for mechanical testing of aligners must be determined in order to obtain reliable results and accurate testing conditions. The aim of this study was to evaluate whether the rectangular shaped specimens, as proposed in previous studies, are suitable for Digital Image Correlation (DIC) testing. In this experimental study, a single type of material was used: Leone aligner PET-G sheet. Material was used in its original, uniformed, and flat form without thermoforming process prior to testing. Rectangular specimens of 1 mm thickness were laser cut to 20x5 mm. A total of five specimens were tested. Each specimen was spray painted with white and black acrylic paint to generate high contrast spots. All specimens were tested individually after fixing them manually in the universal testing machine. The tensile test was performed at speed of 1 mm/min. The specimens’ behaviour under load was evaluated using DIC method. The results showed that rectangular specimens did not consistently fracture in the central region where the stress was expected to be the highest. Instead, fractures occurred in the clamping areas, indicating that the stress concentrations were introduced by the fixation, rather than the material itself. Based on these results, rectangular specimens are not recommended for mechanical testing of aligner materials, as they showed inconsistent fracture locations and non-uniform strain distribution
Molecular “Yin-Yang” Machinery of Synthesis of the Second and Third Fullerene C60 Derivatives
No full textTo overcome the negative effects of the biochemical application of nano-substances in medicine (toxicity problem), using the example of fullerene C60’s first derivative (fullerenol, FD-C60), we show that their biophysical effect is possible through non-covalent hydrogen bonds when around FD-C60 water layers are formed. SD-C60 (Zeta potential is −43.29 mV) is much more stable than fullerol (Zeta potential is −25.85 mV), so agglomeration/fragmentation of the fullerol structure, due to instability, can cause toxic effects. When fullerol in solution was exposed to an oscillatory magnetic field with Re (real) part [250/−92 mT, H(ωt) = Acos(ωt)], water layers around FD-C60 (fullerenol) are formed according to the Penrose process of 3D tiling formation, and the second derivative, SD-C60 (or 3HFWC), is self-organized. However, when Im (imaginary) part [250/−92 mT, H(ωt) = Bisin (ωt)] of the external magnetic field is applied in addition to SD-C60, ordered water chains and bubbling of water (“micelle”) are formed as a third derivative (TD-C60). Fullerol (FD-C60) interacts with biological structures biochemically, while the second (SD-C60) and third (TD-C60) derivatives act biophysically via non-covalent hydrogen bond oscillation. SD-C60 and TD-C60 significantly increased water solubility and reduced toxicity. The paper explains the synthesis of SD-C60 and TD-C60 from FD-C60 (fullerol) as a precursor by the influence of an oscillatory magnetic field (“Yin-Yang” principle) on hydrogen bonds in order to create water layers around fullerol. Examples of biomedical applications (cancer and Alzheimer’s) of this synergetic complex are given. This study shows that the “Yin-Yang” machinery, based on the nanophysics of C60 molecules and non-covalent hydrogen bonds, is possible. The first attempt has been composed to synthesize nanomaterial for biophysical vibrational nanomedicine
Comprehensive Method for Predicting Gas Turbine Cycle Performances Considering the Impact of Various Fuels
No full textGas turbines have advanced significantly in recent years, particularly in compressor and turbine efficiency because of aerodynamic breakthroughs based on numerical flow simulations. Additionally, modern energy demands have driven the adoption of alternative, environmentally friendly fuels such as hydrogen, ammonia, and methanol. These fuels significantly influence combustion gas composition, turbine inlet temperature, mass flow, blade cooling, and overall performance. Traditional cycle performance tools often rely on 0D maps for compressors and turbines, which have limitations in simulating these recent advancements. The proposed method replaces such maps with a 2D approach, utilizing detailed flow calculations for compressors and turbines at each operating point. It integrates combustion processes and secondary air systems and iteratively determines the turbine inlet temperature for precise predictions. This method accurately simulates air bleeds, cooling injections, and adjustments in inlet guide and stator vanes while accounting for the effects of fuel composition on performance. This paper demonstrates the methodology using an industrial gas turbine in which natural gas, hydrogen and hydrogen carriers are used as fuels. It shows the consequences of this for several components as well as the main thermodynamic operating parameters. The approach is fast and effective, enabling the optimization of diverse designs throughout development
Development of an Artificial Neural Network-Based Tool for Predicting Failures in Composite Laminate Structures
No full textComposite materials are widely used in aerospace, automotive, biomedical, and renewable energy sectors due to their high strength-to-weight ratio and design flexibility. However, their anisotropic and layered nature makes structural analysis and failure prediction challenging. Traditional methods require solving complex interlaminar stress–strain equations, demanding significant computational resources. This paper presents a bio-inspired machine learning approach, based on human reasoning, to accelerate predictions and reduce dependence on computationally intensive Finite Element Analysis (FEA). An artificial neural network model was developed to rapidly estimate key parameters—laminate thickness, total weight, maximum stress, displacement, deformation, and failure criteria—based on stacking sequence and geometry for a desired load case. Although validated using a specific composite beam, the methodology demonstrates potential for broader use in rapid structural assessment, with prediction deviations under 15% compared to FEA results. The time savings are particularly significant—while conventional FEA can take several hours or even days, the ANN model delivers accurate predictions within seconds. The approach significantly reduces computational time while maintaining precision. Moreover, with further refinement, this logic-driven model could be effectively applied to aircraft maintenance, enabling faster decision-making and improved structural reliability assessment.Grant numbers: 451-03-137/2025-03/200105 and 451-03-136/2025-03/20002
Energy harvesting from topological interface states in an elastic beam array system
No full textRecent discoveries of exotic topological phenomena in mechanical phononics and metamaterials have become a prominent focus in engineering research. These findings not only expanded the functionality and potential applications of such artificial materials and structures but also went well beyond the initial identification of effects such as band gaps. In this study, we propose energy harvesting from localized interface modes in a periodic beam array system. The system is composed of elastically connected parallel beams, with bimorph piezoelectric beams considered at the interface. We derive a system of governing equations, and respective eigenvalue problems for both the full finite lattice model and a reduced-order model based on Bloch mode synthesis are defined and solved. We analyze the topological properties, eigenspectrum, frequency–voltage relationships, and steady-state responses to explore localized interface modes and their energy harvesting capabilities. Additionally, we investigate the robustness of the frequency of topologically protected interface modes in the presence of a mass defect in the lattice, demonstrating the efficiency of the proposed energy harvesting system
Measuring the semantic priming effect across many languages
No full textSemantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. Although previous studies provide insight into the cognitive underpinnings of semantic representations, they have suffered from small sample sizes and a lack of linguistic and cultural diversity. In this Registered Report, we measured the size and the variability of the semantic priming effect across 19 languages (n = 25,163 participants analysed) by creating the largest available database of semantic priming values using an adaptive sampling procedure. We found evidence for semantic priming in terms of differences in response latencies between related word-pair conditions and unrelated word-pair conditions. Model comparisons showed that the inclusion of a random intercept for language improved model fit, providing support for variability in semantic priming across languages. This study highlights the robustness and variability of semantic priming across languages and provides a rich, linguistically diverse dataset for further analysis. The Stage 1 protocol for this Registered Report was accepted in principle on 15 July 2022. The protocol, as accepted by the journal, can be found at https://osf.io/u5bp6 (registration) or https://osf.io/q4fjy (preprint version 6, 31 May 2022).br. 451-03-65/2024-03/20010
Semielliptical Modification of the Symmetrical Dolphin 0006 Airfoil
No full textcontract number 451-03-136/2025-03/200213 dated February 4, 2025
THE CALIBRATION PROCESS OF THE “TRIMBLE - YIELD MONITORING” SYSTEM FOR THE PURPOSE OF YIELD MAPPING DURING THE TECHNOLOGICAL OPERATION OF BARLEY AND WHEAT HARVESTING
No full textThe application of modern technologies such as sensors and GPS as well
as the collection, analysis and management of production data have become standard in
precision agriculture. Yield, the main goal of production, is not only the post-harvest value,
but also provides important information when measured spatially and analysed accurately.
Yield mapping involves collecting data from multiple locations within a plot and creating
a spatial representation of yield variation. This approach provides information about
the heterogeneity of the field and enables informed decisions and planning of subsequent
management measures. The experiment was conducted during the wheat and barley harvest
in 2024 in the South Banat region. A New Holland CR 7.90 combine harvester was equipped
with a Trimble Yield Monitoring System, which includes an optical mass flow sensor and
an Ag Leader moisture sensor to record yield data. The recorded parameters of grain
moisture, harvested mass and hectolitre weight were then used to calibrate the system. The
calibration reduced measurement errors to around 2% for harvested mass and 0.2% for grain
moisture, ensuring high accuracy of yield data. This paper presents the entire process of
installing and calibrating the yield mapping system.contract registration number: 451-03-137/2025-03/20010
The potential of different printing materials for 3D printed beehives
No full textThe development of 3D printed beehives represents a promising innovation in the field of apiculture, offering the potential to revolutionise hive design. The functionality of traditional wood and beeswax-based hives is endangered by various factors: beeswax mechanical strength, deterioration of the structure due to temperature variation, susceptibility to different pests, proneness for mould formation, etc. Exploring the application of 3D printing technology applied to hive construction requires consideration of factors such as durability, insulation, ventilation, topology optimisation to reduce weight and cost, ease of assembly, and bee harvesting. The choice of materials, ranging from plastic to advanced and biodegradable composites, plays a critical role in enhancing hive sustainability and overall bee health. To make 3D-printed beehives more environmentally friendly, biodegradable materials are a better choice. Some suitable options include PLA, UV-resistant nonoilen, wood-based composite filaments, and innovative materials, which contain organic components and degrade naturally over time. Nevertheless, the potential of 3D-printed structures as a viable alternative in beekeeping has yet to be fully explored, considering the unique properties of each material. Some of the problems that need to be considered are material durability, moisture sensitivity, temperature and chemical resistance, vibration resistance, UV resistance and ageing, surface roughness, and at the end, from an economic side, the cost and accessibility of different materials. This work highlights the potential of additive manufacturing technology to support sustainable beekeeping practices, considering the large variety of materials and their unique properties