International Journal of Mechanical and Materials Engineering最新文献

The insulating oil serves the dual purpose of providing insulation and cooling within transformers. This investigation aims to explore the impact of various nanoparticles on the dielectric breakdown voltage (BDV) of dielectric oils. The study examines the effect of the concentration of magnetic nanoparticles on the dielectric breakdown voltage of insulating oils. Nanoparticles such as iron (II, III) oxide (Fe₃O₄), cobalt (II, III) oxide (CO₃O₄), and ferrous phosphide (Fe₃P) were utilized to create nanofluids with carrier mediums consisting of mineral oil and synthetic ester oil. BDV determination was conducted using a VDE and S–S electrode system according to IEC 60156 standards. Nanofluid were prepared using a two-step method, and their concentrations ranged from 0.01 g/L, 0.02 g/L, and 0.04 g/L in base oils. Twelve iterations were conducted for each prepared nanofluid, and breakdown voltage measurements were recorded. The results indicate a noteworthy enhancement in the breakdown voltage of nanofluids. The statistical analysis was performed on the dielectric property of nanofluid samples for better breakdown accuracy. The maximum enhancement at specific nanoparticle concentrations was shown by each nanofluid. The results show that under the S–S electrode configuration, the greatest overall enhancement was observed for Fe₃P in mineral oil, with an enhancement of 70.05%, and Fe₃O₄ in synthetic ester oil, with an enhancement of 46.29%.

Machine learning-driven automated replication micrographs analysis makes possible rapid and unbiased damage assessment of in-service steel components. Although micrographs captured by scanning electron microscopy (SEM) have been analyzed at depth using machine learning, there is no literature available on the technique being attempted on optical replication micrographs. This paper presents a machine-learning approach to segment and quantify carbide precipitates in thermally exposed HP40-Nb stainless-steel microstructures from batches of low-resolution optical images obtained by replication metallography. A dataset of nine micrographs was used to develop a random forest classification model to segment precipitates within the matrix (intragranular) and at grain boundaries (intergranular). The micrographs were preprocessed using background subtraction, denoising, and sharpening to improve quality. The method achieves high segmentation accuracy (91% intergranular, 97% intragranular) compared to human expert classification. Furthermore, segmented micrographs were quantified to obtain carbide size, shape, and density distribution. The correlations in the quantified data aligned with expected carbide evolution mechanisms. Results from this study are promising but necessitate validation of the method on a larger dataset representative of evolution of thermal degradation in steel, given that characterization of the evolution of microstructure components, such as precipitates, applies to broad applications across diverse alloy systems, particularly in extreme service.

As the global agenda turns more towards the so-called challenge of climate change and lowering carbon emissions, research into green, renewable energy sources becoming nowadays more and more popular. Offshore wind power, produced by FOWTs (i.e., Floating Offshore Wind Turbines), is one such substitute. It is a significant industrial part of the contemporary offshore wind energy industry and produces clean, renewable electricity. Accurate operational lifetime assessment for FOWTs is an important technical safety issue, as environmental in situ loads can lead to fatigue damage as well as extreme structural dynamics, which can cause structural damage. In this study, in situ environmental hydro and aerodynamic environmental loads, that act on FOWT, given actual local sea conditions have been numerically assessed, using the FAST coupled nonlinear aero-hydro-servo-elastic software package. FAST combines aerodynamics and hydrodynamics models for FOWTs, control and electrical system dynamics models, along with structural dynamics models, enabling coupled nonlinear MC simulation in the real time. The FAST software tool enables analysis of a range of FOWT configurations, including 2- or 3-bladed horizontal-axis rotor, pitch and stall regulation, rigid and teetering hub, upwind and downwind rotors. FAST relies on advanced engineering models—derived from the fundamental laws, however with appropriate assumptions and simplifications, supplemented where applicable with experimental data. Recently developed Gaidai reliability lifetime assessment method, being well suitable for risks evaluation of a variety of sustainable energy systems, experiencing nonlinear, potentially extreme in situ environmental loads, throughout their designed service life. The main advantage of the advocated Gaidai risks evaluation methodology being its ability to tackle simultaneously a large number of dynamic systems' degrees of freedom, corresponding to the system's critical components.

La_1-xCa_x(B1,B2,B3)O₃ perovskite powders doped with calcium were synthesized and sintered. Calcium doping modified the A-site of the perovskite structure, while the B-site was composed of three cations in equal atomic amounts. Cations on the B-site included cobalt, chromium, iron, manganese, and nickel. Sintering temperature varied from 1200 to 1400 °C in air. Density measurements and microstructure imaging determined effect of composition on sintering. Electrical conductivity of sintered compacts was measured using the four-wire measurement method at temperatures of 300 to 900 °C in air. Electrical properties as a function of composition indicate the effect of calcium doping in combination with varied B-site substitution increases electrical conductivity and improves sintering.

Enabling gears to withstand loss of lubrication in gearboxes without secondary oil supply systems can reduce weight and space demand and thus fuel consumption. This study investigates the potential of surface and material technologies on the loss of lubrication performance of gears. Thereby, superfinished, coated, and nitrided gears are compared to ground gears. Systematic experiments under loss of lubrication are performed at a back-to-back gear test rig with circumferential speeds of up to 20 m/s and Hertzian pressures in the pitch point of up to 1723 N/mm². Torque loss, pinion bulk temperatures, and tooth flank surface are analyzed. The results show that surface and material technologies can greatly influence frictional behavior and damage initiation of gears operating under loss of lubrication. With the materials and conditions tested, superfinishing yields to accelerated rise of frictional losses and thus scuffing. Coatings lead to significantly enhanced service life under loss of lubrication by friction reduction and scuffing avoidance.

Taguchi’s method was applied to investigate the effect of main high energy ball milling (HEBM) parameters: milling time (MT), ball to powder weight ratio (BPWR), and milling speed (MS) on the CaCO₃ crystallite size. The settings of HEBM parameters were determined by using the L9 (3³) orthogonal experiments array (OA). The as-received and milled powders were characterized by X-ray diffraction (XRD) scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy. The crystallite size of CaCO₃ varied between 140 and 540 nm depending on the HEBM conditions. The analysis of variance (ANOVA) was used to find the significance and percentage of contribution of each milling parameter. It was established that the MT is the most effective parameter followed by MS and BPWR. A confirmation test was carried out with a 90% confidence level to illustrate the effectiveness of the Taguchi optimization method. The optimum milling parameter combination was determined by using the analysis of signal-to-noise (S/N) ratio. Based on the S/N ratio analysis, optimal HEBM conditions were found MT 10 h, MS 600 revolutions per minute (rpm), BPWR 50:1.