Refinement Of Parameters Research Articles

An approach for adaptive filtering with reinforcement learning for multi-sensor fusion in condition monitoring of gearboxes

Condition monitoring of gearboxes is integral to maintaining floor safety, system stability, and inventory management. Capturing vibration response using sensors and subsequent response analysis is the standard procedure for gearbox fault detection. However, the sensors are susceptible to non-constant reliability due to the convolution of vibration responses from multiple sources, background noise interference, and transfer-path effect. The problem is multi-fold when ideal sensor attachment locations are unavailable due to spatial constraints of industrial floors. The response component reflective of the fault information must be enhanced for adequate fault severity estimations. The present study addresses this hurdle by proposing a multi-sensor framework with available sensor attachment locations for gearbox condition monitoring. Adaptive filtering is done in the framework with parameters optimised to enhance fault information. A proximal policy optimisation agent is trained with a reinforcement learning environment for parameter refinement. Further, fault severity estimation is achieved by a weighted fusion of spectral features reflective of the side-band excitation effect caused by gear fault. The proposed method is applied to datasets acquired from an in-house seeded fault test bed. The proposed method underscores superior performance compared to conventional single-sensor-based fault severity analysis and alternate fusion approaches.

Fabrication of Mid-Infrared Porous Anodic Alumina Optical Microcavities via Aluminum Anodization.

This study reports the production of mid-infrared (MIR) porous anodic alumina (PAA)-based microcavities with tunable optical quality. The spectral position of the cavity resonance peak (λC), along with its intensity (IR) and Q-factor, varies depending on the geometric positioning of the cavity layer within the multilayer stack of alternating low- and high-porosity layers, as well as the type of cavity produced-either by high voltage (CvH-type) or low voltage (CvL-type) pulses. In most cases, PAA microcavities with CvH-type cavity layers exhibited superior light confinement properties compared to those with CvL-type cavities. Additionally, shifting the cavity layer from the center toward the edges of the multilayer stack enhanced the intensity of the resonance peak. For PAA microcavities with CvH-type cavity layers, the highest intensity (IR = 53%) and the largest Q-factor (Q = 31) were recorded at λC of around 5.1 µm. The anodization approach used in this study demonstrates significant potential for designing PAA-based microcavities with high optical performance in the MIR spectral region, especially with further refinement of electrochemical parameters. These findings pave the way for the development of new photonic materials specifically tailored for the MIR spectral range, broadening their applications in various optoelectronic and sensing technologies.

In-situ impedance diagnosis experiment and equivalent circuit modeling based on distribution of relaxation time method for unitized regenerative proton exchange membrane fuel cells

The Distribution of Relaxation Times (DRT) method provides a powerful approach for analyzing Electrochemical Impedance Spectroscopy (EIS) in electronic components, enabling clear separation of different impedance types and accurate equivalent circuit modeling. In this study, the DRT method was applied to unitized regenerative proton exchange membrane fuel cells (UR-PEMFCs) for the first time, establishing an impedance quantification model for in-situ diagnostics of multiple impedances. This foundational framework supports future durability testing efforts. In-situ impedance diagnostics were conducted in both Fuel Cell (FC) and Electrolytic Cell (EC) modes using a custom mode-switching platform. Results show that under constant electrode conditions, the bifunctional membrane electrode enhances round-trip efficiency by 15.75% compared to the constant gas mode. Through DRT imaging in FC and EC modes, impedance information corresponding to distinct relaxation peaks was extracted and assigned based on operational experiments. The three main peaks identified in both modes were attributed to proton transfer impedance, charge transfer impedance, and mass transfer impedance. Additionally, variations in operating conditions led to the appearance of new transport mechanisms within the charge transfer impedance. In FC mode, the DRT-based equivalent circuit model achieved precise fitting. Applying DRT to UR-PEMFCs improves water and bubble management, offering efficient optimization pathways and valuable guidance for cell design, operational parameter refinement, and durability monitoring.

Optimisation of Additively Manufactured Coiled Flow Inverters for Continuous Viral Inactivation Processes

This article reports the development and utilisation of an adaptive design workflow methodology for use as a platform technology for the printing, testing, and optimisation of biopharmaceutical processing reactors. This design strategy was developed by application to the complex structure of the coiled flow inverter (CFI). In this way, the many possible physical parameters of the reactor were optimised, via a combination of experimental results, computational fluid dynamics and machine learning approaches, to find the CFI setups that provide the optimal flow properties for a particular application.Additively manufactured reactors are seeing increasing interest in the field of biopharmaceutical production. This is because the desired output volumes are typically small and there is an increasing move towards adopting continuous production, to replace traditional batch production. This approach allows for the tailoring of reactors for a specific reaction, i.e. attempting to maximise the desired aspects of the reaction through refinement of the physical parameters of the reactor, so creating a large possible parameter space to explore.Consequently, the holistic optimisation of CFI reactors and 3D printing is established as providing better plug flow mixing relative to traditional tube coiled reactors. In addition, a trained metamodel in combination with multilayer equations is demonstrated to predict reactor performance quickly and accurately.

A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks

Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. The node correlation principle is applied to construct a node correlation matrix, and a minimum distance iteration algorithm is proposed to generate candidate topologies, which serve as graph inputs for the parameter estimation model. Based on the topological dependencies and convolutional properties of AC power flow equations, a PFGCN model is designed for line parameter estimation. Parameter refinement is achieved through an alternating iterative process of pseudo-trend calculation and neural network training. Training convergence and loss function values are used as feedback to filter and validate candidate topologies, enabling precise joint estimation of both topologies and parameters. The proposed method’s accuracy, transferability, and robustness are demonstrated through experiments on the IEEE-33 and modified IEEE-69 distribution systems. Multiple metrics, including MAPE, IAE, MAE, and R2, highlight the proposed method’s advantages over Adaptive Ridge Regression (ARR). In the C33 scenario, the proposed method achieves MAPEs of 4.6% for g and 5.7% for b, outperforming the ARR method with MAPEs of 7.1% and 7.9%, respectively. Similarly, in the IC69 scenario, the proposed method records MAPEs of 3.0% for g and 5.9% for b, surpassing the ARR method’s 5.1% and 8.3%.

ЯППИ – универсальный язык программирования миссий АНПА

This article proposes a universal language for developing autonomous underwater vehicle (AUV) missions. The main features of the proposed language are: the presence of high-level commands to describe the main tasks of underwater research, the use of commands in Russian, the readability of the created missions and their understandability for personnel involved in the use of underwater robotics, the lack of strict typing and parameterization of commands, the possibility of expanding the language by creating new commands based on the old ones. The article describes the main parameters of the language, syntax, semantics and basic commands, as well as language tools - a parameter refinement module, which interactively allows you to resolve uncertainty arising due to non-strict parameterization, low-level command module that implements more complex commands based on basic ones, a language extension module responsible for adding new commands, and a translator. The language translator allows you to translate created missions into task program codes for various AUVs. The created language is called URPL – Underwater Research Programming Language and at the current stage of research it is being tested and debugged on the AUVs of the IPMT FEB RAS, taking into account the experience accumulated at the institute.

Optimizing Cold Spray Parameters for High Entropy Alloy Coatings Using Taguchi and Box–Behnken Design Approaches for Mechanically Alloyed Powder

The present study focused on optimizing the cold spray (CS) process parameters for depositing Fe20Cr20Mn20Ni20Co20 (Cantor alloy) coatings using mechanically alloyed (MA) powder. A two-step design of experiments approach was employed, beginning with the initial screening of input variables using the L8 Taguchi method, followed by the refinement of process parameters through the Box–Behnken design of experiments. Key performance indicators included deposition efficiency (DE), coating thickness per pass, and microstructural parameters including porosity, cracks, and interfacial defects/delamination. The study identified process gas temperature as the primary factor influencing both DE and thickness per pass. Higher gas temperature and pressure, combined with increased scanning speed, resulted in higher DE. The DE of the MA Cantor alloy powder peaked at around 14-15%, with a deposit density greater than 99% achieved at the highest process gas temperature and pressure (1000 °C and 60 bar, respectively). The average hardness of the optimal CS coating deposited using MA powder was found to be 679 ± 17 HV0.1, which is approximately 90% greater than the average hardness reported for CS coatings deposited using atomized powder.

Bacillus cereus CGMCC 1.60196: a promising bacterial inoculant isolated from biological soil crusts for maize growth enhancement.

Soil microbial inoculants are widely recognized as an environmentally friendly strategy for promoting crop growth and increasing productivity. However, research on utilizing the microbial resources from desert biological soil crusts to enhance crop growth remains relatively unexplored. In the present work, a bacterial strain designated AC1-8 with high levels of amylase, protease, and cellulase activity was isolated from cyanobacterial crusts of the Tengger Desert and identified as Bacillus cereus (CGMCC 1.60196). The refinement of the fermentation parameters of B. cereus CGMCC 1.60196 determined that the most effective medium for biomass production was composed of 5 g/L glucose, 22 g/L yeast extract and 15 g/L MgSO4, and the optimal culture conditions were pH 6.0, temperature 37°C, inoculation quantity 3% and agitation speed 240 rpm. Furthermore, the utilization of B. cereus CGMCC 1.60196 has resulted in substantial improvements in various growth parameters of maize seedlings, including shoot length, shoot fresh and dry weights, root fresh and dry weights, and the contents of chlorophyll a, chlorophyll b, and total chlorophyll. The most pronounced growth promotion was observed at an application concentration of 1 × 109 CFU/m2. These results suggest that the novel B. cereus strain, isolated from cyanobacterial crusts, can be regarded as an exemplary biological agent for soil improvement, capable of enhancing soil conditions, promoting crop cultivation and supporting food production.

Vision-based autonomous robots calibration for large-size workspace using ArUco map and single camera systems

The low positioning accuracy of industrial robots limits their application in industry. Vision-based kinematic calibration, known for its rapid processing and economic efficiency, is an effective solution to enhance this accuracy. However, most of these methods are constrained by the camera's field of view, limiting their effectiveness in large workspaces. This paper proposes a novel calibration framework composed of monocular vision and computer vision techniques using ArUco markers. Firstly, a robot positioning error model was established by considering the kinematic error based on the Modified Denavit-Hartenberg model. Subsequently, a calibrated camera was used to create an ArUco map as an alternative to traditional single calibration targets. The map was constructed by stitching images of ArUco markers with unique identifiers, and its accuracy was enhanced through closed-loop detection and global optimization that minimizes reprojection errors. Then, initial hand-eye parameters were determined, followed by acquiring the robot's end-effector pose through the ArUco map. The Levenberg-Marquardt algorithm was employed for calibration, involving iterative refinement of hand-eye and kinematic parameters. Finally, experimental validation was conducted on the KUKA kr500 industrial robot, with laser tracker measurements as the reference standard. Compared to the traditional checkerboard method, this new approach not only expands the calibration space but also significantly reduces the robot's absolute positioning error, from 1.359 mm to 0.472 mm.

Novel vapor phase furfurylation for enhancing the dimensional stability of bamboo

Bamboo holds great promise as a versatile material in construction and engineering. However, its inherent hydrophilic nature, particularly within its cell walls, presents challenges for dimensional stability, limiting its widespread use. Addressing this challenge, vapor phase furfurylation (VPF) has emerged as a promising technique for bamboo modification. In this study, we applied VPF to bamboo for the first time, resulting in a significant transition towards hydrophobicity and a remarkable enhancement in dimensional stability (ASE > 50 %). Employing a multifaceted approach, we investigated the distribution gradient and penetration depth of FA resin within furfurylated bamboo using advanced methodologies such as scanning electron microscopy (SEM), nanoindentation, dynamic vapor sorption (DVS), and imaging FT-IR microscopy, demonstrating the improvement in dimensional stability is attributed to cell wall bulking from FA resin infiltration. Through meticulous parameter refinement, we identified an optimized protocol comprising 40 h of VPF exposure, a VPF temperature of 115 °C, and a 4.5 % concentration of maleic anhydride (MA). This tailored VPF methodology holds promise for enhancing the dimensional stability of bamboo while minimizing FA consumption, thus expanding its potential applications in construction and furniture industries.

Spatial variability of CH4 uptake in aerated soils of Yellow River Delta

The widespread occurrence of aerated plain soils underscores their significant role in the global soil methane (CH4) sink budget. However, plain soils are poorly characterized in terms of spatial variability of CH4 uptake and the relevant control. We investigated the intra- and inter-site spatial variability of CH4 uptake through flux measurements in intact soil cores from five non-wetland sites within the Yellow River Delta, each representing a distinct land use/cover type. Methane uptake rates were highest in undisturbed forest cores. The rates were very low, often falling below the detection limit, in cores from the other four sites. The significant correlation between CH4 uptake and bulk density across sites suggests the integrative role of bulk density for the effects of different disturbances (including salt stress and succession) on CH4 uptake. Methane uptake was heterogeneous at the within-site scale as indicated by large coefficients of variations (CVs). Soil texture variation manipulated the within-site pattern of CH4 uptake in the low-salinity sites. Salt affected the spatial variation of CH4 uptake only at high level of salinity. Neither Potter's nor Ridgwell's models effectively captured the within-site variation of CH4 uptake due to a texture-associated bias in the models. Establishing a quantitative relationship between CH4 uptake and clay content at the field scale in alluvial plain soils will facilitate the refinement of model parameters linked to texture and rectify biases in CH4 estimation. These results provide an insight for the biogeochemical control of CH4 uptake in alluvial plain soils and have important application for improving CH4 models.

Solid Solution Behavior of High Performance BiOBrxI1-X Photoanodes Probed Using Solid-State NMR, XRD and Optical Spectroscopy

Bismuth oxyhalides (BIOX) are a family of solution-synthesizable 2D layered semiconductors constituted of earth-abundant elements namely bismuth, oxygen and the halogens. BIOX based thin films and heterojunctions are the subject of immense scientific interest due to their impressive performance in photocatalytic and photoelectrochemical devices. Solid solutions of Br/I bismuth oxyhalides exhibit continuously tunable absorption and emission spectra similar to that observed in mixed halide MAPbClxBryI3-x-y perovskites and mixed anion III-V GaAsxP1-x semiconductors. The excited state decay showed two components for all the mixed halide BiOX semiconductors with a dominant short-lived component in the nanosecond range and a weaker longer-lived component of ~100 ns. The average photoluminescence (PL) lifetime varied from 5.4 ns in bare BiOI to 1.49 ns in bare BiOBr with intermediate compositions exhibiting monotonically increasing average PL lifetimes with increasing iodine content. Using a combination of characterization techniques such as solid-state nuclear magnetic resonance (ssNMR), x-ray diffraction (XRD), x-ray photoelectron spectroscopy (XPS) and optical spectroscopy, we demonstrate the formation of atomic level solid solutions in Br/I bismuth oxyhalides. Through refinement of the lattice parameters using powder XRD and complementary atomic-level 209Bi SSNMR spectroscopy, we found that Vegard's law was obeyed by the BIOBrxI1-x solid solutions. By varying the Br:I ratio, it is also possible to tune the electronic band gap as well as the energy levels corresponding to the conduction band minimum (ECB, min) and the valence band maximum (EVB, max), thus allowing control over the thermodynamic driving force available to drive chemical reactions using photogenerated or electrically injected charge carriers. Suppression of the longitudinal optical phonon in solid solutions of Br/I bismuth oxyhalides is suggestive of lower phonon scattering of charge carriers and improved electronic transport. A synergistic enhancement of more than 1 order of magnitude in the photoelectrochemical water-splitting performance was obtained for the optimized solid solution (BiOBr0.67I0.33) under AM 1.5G 1 sun illumination. Our results pave the way for a one pot synthesis protocol to form BiOX thin films, powders and colloidal suspensions with deterministic control over the stoichiometry.

Laser powder bed fusion of 2507 duplex stainless steel: Microstructure, mechanical properties, and corrosion performance

Duplex stainless steels offer a unique combination of advantages derived from both ferritic and austenitic structures, providing excellent mechanical properties and corrosion resistance. Nevertheless, conventional metallurgical techniques such as casting and rolling encounter challenges when it comes to the rapid fabrication of intricate components. In this study, we explore the microstructure, mechanical properties, and corrosion performance of 2507 duplex stainless steel manufactured through laser powder bed fusion (L-PBF). Through meticulous refinement of L-PBF parameters, we successfully achieved a dense sample featuring a hardness exceeding 400 HV, a relative density surpassing 98 %, and a surface roughness below 15 μm. This was obtained using a scanning speed of 700 mm/s and a laser power of 210 W. Due to the high cooling rate of L-PBF process, L-PBFed 2507 exhibits a ferritic microstructure distinct from that of rolled 2507 duplex stainless steel. Additionally, a considerable presence of internal dislocations can be observed in the L-PBFed 2507 material. The 2507 stainless steel exhibited an outstanding yield strength of 1198 MPa and an ultimate tensile strength of 1269 MPa, surpassing the performance of rolled 2507 duplex stainless steel. The corrosion current density was nearly negligible (approximately 10−6 A cm−2), with a passivation window expanding 1.2 V relative to the corrosion potential and a passivation platform below 10−4 A cm−2. These corrosion characteristics were comparable to those observed in rolled 2507 duplex stainless steel. This research demonstrates the suitability of L-PBFed 2507 stainless steel in environments requiring high strength and corrosion resistance, thereby opening up avenues for high-performance components in advanced engineering applications.

Influence of Remote Laser Cutting on Magnetic Loss and Mechanical Properties in 0.2 mm Silicon Steel

Energy loss due to eddy current generation is a significant factor in reduced efficiency of electrical machines, therefore motor cores with thinner sheets are required due to their effect in reducing eddy currents. However, reducing the thickness of laminations is challenging due to the high tooling costs in blanking due to the small tolerances required, whilst other methods such as conventional laser cutting are too slow to be commercially viable. In this paper the influence of remote laser cutting on thin electrical steel sheets (Cogent/Tata Steel NO20 3.2% Si) has been investigated on the tensile strain to fracture, fatigue life, edge hardness, and energy loss in an AC magnetisation cycle for two laser power levels. The laser power has found to have no statistically significant influence on the bulk mechanical properties of the material, but significantly affects the measured specific loss. The latter was associated with the increased edge hardness at higher laser powers. Based on the parameters selected it is highly questionable whether RLC offers a viable method for lamination cutting, significant refinement of the laser parameters and/or the use an alternative laser such as an ultra-fast pulsed laser is required to be competitive with existing methods. Additionally, it is shown that energy loss increases linearly with respect to length of the cut edge - to the minimum cut spacing tested of 6 mm. These results can be used to optimise laser parameters to reduce the degradation of magnetic properties by decreasing laser power where possible.

Application of the Modified Universal Soil Loss Equation (MUSLE) for the prediction of sediment yield in Agewmariam experimental watershed, Tekeze River basin, Northern Ethiopia

The study utilized the Modified Universal Soil Loss Equation (MUSLE) to predict sediment loss and evaluate the model's performance in the Agewmariam experimental watershed in order to support the planning, management, and appropriate use of the soil and water resources in the watershed. The natural resources conservation service (NRCS) curve number method was used to model runoff energy factor. By overlaying maps of runoff energy, soil erodibility, slope length and steepness, cover management, and support practice factors with assigned values, the cumulative effect of these parameters for the suspended sediment yield was calculated using the ArcGIS raster calculator. The runoff energy factor was the most sensitive parameter, followed by slope length and steepness factor. To improve the model's fit to the local conditions, the initial abstraction to storage ratio (λ) of the runoff energy factor was reduced to 0.023, and the MUSLE model coefficient and exponent were adjusted to 1 and 0.59, respectively. During calibration, the mean observed and estimated suspended sediment yields were 0.2 and 0.23 ton/ha, respectively, while during validation, they were 0.7 and 0.53 ton/ha, respectively. The model evaluation showed that the MUSLE model, without calibration, was not appropriate for estimating runoff and sediment yield. However, with appropriate calibration, the model showed good performance with a coefficient of determination (R2), coefficient of efficiency (E), and index of agreement (d) of 0.85, 0.85, and 0.96 respectively, during calibration and 0.84, 0.65, and 0.83 respectively, during validation. Based on these findings, this study suggests that the calibrated MUSLE model can be used to prioritize soil and water conservation interventions within the watershed or can be extrapolated to neighboring similar watersheds. Further refinement of model input parameters using more data from the watershed is recommended to increase the prediction accuracy of the model.

Study on the relevant parameters of direct economic loss assessment of reinforced concrete structure buildings considering seismic fortification and use function

The main cost and decoration cost of the building are the key parameters in the evaluation of the direct economic loss of the building. In the Earthquake Site Work Part 4 : Direct Loss Assessment of Disasters ( GB / T 18208.4-2011 ), considering factors such as economic development level, structural type, and building use, based on expert experience and engineering cost data, a grading index was developed and a corresponding correction coefficient was given to more accurately and reasonably assess the loss of the main building and the loss of decoration. In view of the rapid development of China 's economy and the acceleration of urbanization in the past ten years, the original grading indicators and coefficients need to be adjusted to adapt to the reality. This paper focuses on the reinforced concrete structure with various functions and wide distribution. By collecting a large number of corresponding building main body and decoration cost data, and combining with expert experience data, this paper analyzes the influence of seismic fortification level on the main body cost, as well as the influence of economic development level, decoration grade, use function and other factors on the decoration cost. The research results of this paper aim to provide reference for the update and refinement of several key parameters in the specification.

Optimizing surface finish in FDM-printed polycarbonate spur gears through abrasive flow finishing: insights from physics and material science perspectives

In recent times, the usage of polymers has experienced notable growth across diverse manufacturing sectors. Polymeric gears, integral to automation, material handling systems, toys, and household appliances, have become ubiquitous. Although additive manufacturing techniques, especially Three-Dimensional (3D) printing, offer versatile applications, they grapple with challenges, notably poor surface finishing attributed to layer accumulation. This work explores the field of abrasive flow machining (AFM) in experimental settings using FDM-printed polymeric gears. The AFM medium concoction involves coal ash powder as the foundational material, EDM oil as the carrier fluid, and the infusion of glycerin as additives. Rigorous investigations were undertaken to pinpoint the optimal viscosity of the AFM medium and refine process parameters with a central focus on enhancing surface quality. A Taguchi L9 Design of Experiment (DOE) was meticulously crafted for parameter optimization using the Minitab statistical software. The investigation established a functional relationship between the output parameter (surface roughness) and key input variables (layer thickness, abrasive percentage, abrasive mesh size, and finishing time). The maximum level of AFM media optimization was attained at 33% abrasive concentration, 220 abrasive mesh size, and 60% liquid synthesizer. Additionally, the results of the investigation showed that a media viscosity of 0.50 Pa-sec, layer thickness of 0.1, and culminating time of 45 min were the optimal values for the most % improvement in surface roughness. The initial surface roughness underwent a profound reduction from 12.30 μm to 0.30 μm, marking an exceptional improvement of 97.56%. This inquiry contributes significant insights into the refinement of AFM parameters for elevating the surface finish of FDM-printed polymeric gears, promising enhanced performance across diverse applications.

Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio

This research investigates the comparative predictive efficacy of two leading machine learning methodologies, specifically the XGBoost and Random Forest models, in estimating ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions along the east coast of North America. Using annual temperature datasets and relevant oceanographic parameters, the data is carefully processed, cleaned and sorted into training and test subsets via the RStudio Platform. The performance evaluation model is carried out using predetermined machine learning assessment criteria, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared. The results show the superiority of the XGBoost model compared to Random Forest in terms of prediction accuracy and minimizing prediction errors. The XGBoost model shows lower MSE values and higher R-squared values than the Random Forest model, indicating its better capacity in explaining data variations. XGBoost consistently provides more accurate predictions and shows higher sensitivity in identifying important factors influencing ocean temperature fluctuations than Random Forest. This research significantly improves understanding and prognostic capabilities regarding ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions. Empirical evidence underlines the efficacy of the XGBoost model in predicting ocean temperatures in the studied region. Continuous model evaluation and parameter refinement for both methodologies is critical to establishing standards for optimal prediction performance. The findings of this research have implications for the fields of oceanography and climate science, and offer potential pathways to comprehensively understand and mitigate the impacts of climate change on marine ecosystems.

ВИЗНАЧЕННЯ ВПЛИВУ ВНУТРІШНІХ ЕНЕРГІЙ КРИСТАЛІЧНОЇ РЕШІТКИ НА ОТРИМАННЯ НАНОСТРУКТУР У ПОВЕРХНЕВИХ ШАРАХ АЛЮМІНІЄВИХ СПЛАВІВ

The paper presents the results of calculating the crystallization energy and examines its influence on the size of the nanostructured grain during ion-plasma treatment of aluminum (VD17) with oxygen and nitrogen ions. To address this task, we employ a previously proposed model, which considers the impact of individual ions on thermal conductivity and thermoelasticity in the affected area, taking into account their energy, charge, and type. Initially, we estimate the potential number of particles in the nanostructure. Then, we compute the energy required for atomizing the grain from atoms and chemical compounds. By determining the total atomization energy of the grain (Eas), we establish the necessary energy for its formation (Es = 1.1Eas). This energy enables the determination of all characteristics in the ion's action area, such as temperature, temperature rise rate, thermal stresses, strain rate, grain size, volume, and depth of the nanostructure, as well as the actual number of particles in the nanostructure. The calculations demonstrate that the crystallization energy increases the ion energy required to obtain nanostructures. At energies close to 3∙102 eV, it ranges from 0.1 to 7 eV, which can be disregarded, while at energies close to 1.6∙104 eV, crystallization energy ranges from 2.1∙102 to 1.2∙104 eV, with higher values for oxygen ions. Additionally, the calculations show that ion charge significantly affects crystallization energy; for large ion charges, it increases. All of this underscores the necessity of considering crystallization energy only at energies of 2∙103 – 2∙104 eV, allowing refinement of the technological parameters of ion-plasma treatment of aluminum alloys to increase the likelihood of obtaining nanostructures. Furthermore, the ability to determine the sizes of nanostructures allows predicting the physical and mechanical characteristics of surface layers of processed materials. These studies may be of interest to specialists involved in surface strengthening of aluminum alloy surfaces and further research into nanostructures

Using Asteroseismology to Calibrate the Physical Parameters of Confirmed Exoplanets and Their Evolved Host Stars

Asteroseismology offers a profound window into stellar interiors and has emerged as a pivotal technique in exoplanetary research. This study harnesses the Transiting Exoplanet Survey Satellite observations to reveal, for the first time, the asteroseismic oscillations of four exoplanet-hosting stars. Through meticulous analysis, we extracted their asteroseismic signatures, enabling the precise determination of stellar masses, radii, luminosities, and surface gravities. These parameters exhibit markedly reduced uncertainties compared to those derived from spectroscopic methods. Crucially, the exoplanets orbiting these stars were initially identified via radial velocity measurements. The refinement of host stellar masses necessitates a corresponding adjustment in planetary characteristics. Employing asteroseismology, we recalibrated the exoplanets’ minimum masses and semimajor axes—a novel approach in the field. For instance, the exoplanet HD 5608 b's minimum mass, denoted as Msini , was ascertained to be 1.421 ± 0.091M J through the integration of asteroseismic and radial velocity data. Similarly, two planets within the 7 CMa system yielded Msini values of 1.940 ± 0.064M J and 0.912 ± 0.067M J , respectively. Two planets in the HD 33844 system presented Msini figures of 1.726 ± 0.145M J and 1.541 ± 0.182M J , while the HIP 67851 system's planets registered Msini at 1.243 ± 0.139M J and a notably higher 5.387 ± 0.699M J . This investigation extends beyond mere parameter refinement, it underscores the synergy between asteroseismology and exoplanetology, yielding unprecedented precision in system metrics. Focusing on a quartet of K-type giants in advanced evolutionary phases, our work positions these systems as invaluable astrophysical laboratories, offering insights into the potential trajectory of our own solar system's fate.