Search Results (98)

Low thermal conductivity of phase change materials (PCMs) remains a major limitation in the design of efficient thermal energy storage systems. Enhancing the thermal performance of PCM storage units is therefore a critical design consideration. Fin geometry plays a pivotal role in improving the heat charging and discharging rates by influencing heat transfer mechanisms, particularly natural convection during melting. This study presents a two-dimensional numerical investigation of novel fin geometries aimed at accelerating the melting process of PCM in a double-pipe heat exchanger. Four fin designs are examined: single-step thickness reduction, double-step thickness reduction, stepwise thickness reduction/expansion, and smooth thickness reduction fins. These configurations are specifically developed to promote natural convection currents in the molten PCM regions adjacent to the fin’s surfaces. The enthalpy–porosity method is employed using ANSYS Fluent 19 to simulate the phase change process. The COUPLED algorithm is used for pressure–velocity coupling, with the PRESTO! scheme applied for pressure interpolation and a second-order upwind scheme adopted for the discretization of transport equations. The results demonstrate that the proposed thickness reduction fins significantly enhance the PCM melting rate by intensifying natural convection currents, driven by localized temperature gradients along the fin surfaces. Full article

Effective arsenic removal is a challenge when smelting high-arsenic copper minerals (HACMs, As > 3.0 wt%). Current arsenic-removal methods for HACM smelting cannot effectively remove arsenic and do not satisfy environmental requirements. This study argues that two-stage filtration based on Fe-25Al porous material and oxygen-controlled roasting is an effective technique for HACM arsenic removal (As = 11.8 wt%). The use of two-stage filtration facilitated double interception: particles larger than 10 μm were mechanically intercepted by the pore channels, and submicron particles (0.1–10 μm) were intercepted by the filter cake. Specifically, in the second stage, the flue gas underwent gradient rapid cooling, and the arsenic in the flue gas rapidly condensed, resulting in efficient arsenic removal. The purity of the condensed product, As₂O_3, was greater than 99%. Moreover, adding sand to the roasted mineral increased the specific surface area from 0.484 m²/g to 0.590 m²/g, reducing the “bottleneck effect” of pores; the addition of carbon further increased the surface area to 2.457 m²/g, inhibiting the formation of arsenate. When the mineral feed rate increased from 50 kg/h to 80 kg/h, the oxygen partial pressure decreased; this effectively inhibited the formation of iron arsenate, and the arsenic removal efficiency increased from 70.20% to 95.61%. The optimized process achieved ≥94% arsenic removal efficiency and ≥76% sulfur-fixation efficiency, with low energy cost. Material balance analysis showed that after arsenic removal, the Cu/Si to Fe/Si ratio of the copper mineral reached 1.5, which is appropriate for immediate subsequent smelting. This study provides a new technological strategy for HACM arsenic removal. Full article

Difficult, extreme operating conditions of parabolic antennas under precipitation and sub-zero temperatures require the creation of effective heating systems. The purpose of the research is to develop a multilayer coating containing two metal-ceramic layers, epoxy composite layers, carbon fabric, and an outer layer of basalt fabric, which allows for effective heating of the antenna, and to study the properties of this coating. The multilayer coating was formed on an aluminum base that was subjected to abrasive jet processing. The first and second metal-ceramic layers, Al₂O₃ + 5% Al, which were applied by high-speed multi-chamber cumulative detonation spraying (CDS), respectively, provide maximum adhesion strength to the aluminum base and high adhesion strength to the third layer of the epoxy composite containing Al₂O₃. On this not-yet-polymerized layer of epoxy composite containing Al₂O₃, a layer of carbon fabric (impregnated with epoxy resin) was formed, which serves as a resistive heating element. On top of this carbon fabric, a layer of epoxy composite containing Cr₂O₃ and SiO₂ was applied. Next, basalt fabric was applied to this still-not-yet-polymerized layer. Then, the resulting layered coating was compacted and dried. To study this multilayer coating, X-ray analysis, light and raster scanning microscopy, and transmission electron microscopy were used. The thickness of the coating layers and microhardness were measured on transverse microsections. The adhesion strength of the metal-ceramic coating layers to the aluminum base was determined by both bending testing and peeling using the adhesive method. It was established that CDS provides the formation of metal-ceramic layers with a maximum fraction of lamellae and a microhardness of 7900–10,520 MPa. In these metal-ceramic layers, a dispersed subgrain structure, a uniform distribution of nanoparticles, and a gradient-free level of dislocation density are observed. Such a structure prevents the formation of local concentrators of internal stresses, thereby increasing the level of dispersion and substructural strengthening of the metal-ceramic layers’ material. The formation of materials with a nanostructure increases their strength and crack resistance. The effectiveness of using aluminum, chromium, and silicon oxides as nanofillers in epoxy composite layers was demonstrated. The presence of structures near the surface of these nanofillers, which differ from the properties of the epoxy matrix in the coating, was established. Such zones, specifically the outer surface layers (OSL), significantly affect the properties of the epoxy composite. The results of industrial tests showed the high performance of the multilayer coating during antenna heating. Full article

Atmospheric climate science lacks the capacity to integrate thermodynamics with the gravitational potential of air in a classical quantum theory. To what extent can we identify Carnot’s ideal heat engine cycle in reversible isothermal and isentropic phases between dual temperatures partitioning heat flow with coupled work processes in the atmosphere? Using statistical action mechanics to describe Carnot’s cycle, the maximum rate of work possible can be integrated for the working gases as equal to variations in the absolute Gibbs energy, estimated as sustaining field quanta consistent with Carnot’s definition of heat as caloric. His treatise of 1824 even gave equations expressing work potential as a function of differences in temperature and the logarithm of the change in density and volume. Second, Carnot’s mechanical principle of cooling caused by gas dilation or warming by compression can be applied to tropospheric heat–work cycles in anticyclones and cyclones. Third, the virial theorem of Lagrange and Clausius based on least action predicts a more accurate temperature gradient with altitude near 6.5–6.9 °C per km, requiring that the Gibbs rotational quantum energies of gas molecules exchange reversibly with gravitational potential. This predicts a diminished role for the radiative transfer of energy from the atmosphere to the surface, in contrast to the Trenberth global radiative budget of ≈330 watts per square metre as downwelling radiation. The spectral absorptivity of greenhouse gas for surface radiation into the troposphere enables thermal recycling, sustaining air masses in Lagrangian action. This obviates the current paradigm of cooling with altitude by adiabatic expansion. The virial-action theorem must also control non-reversible heat–work Carnot cycles, with turbulent friction raising the surface temperature. Dissipative surface warming raises the surface pressure by heating, sustaining the weight of the atmosphere to varying altitudes according to latitude and seasonal angles of insolation. New predictions for experimental testing are now emerging from this virial-action hypothesis for climate, linking vortical energy potential with convective and turbulent exchanges of work and heat, proposed as the efficient cause setting the thermal temperature of surface materials. Full article

In this study, we aim to analyze the dispersion of ultrasonic waves due to second-gradient contributions and attenuation within the framework of continuum mechanics. To investigate dispersive behavior and attenuation effects, we consider the influence of both higher-order gradient terms (second gradients) and Rayleigh-type viscoelastic contributions. To this end, we employ the extended Rayleigh–Hamilton principle to derive the governing equations of the problem. Using a wave-form solution, we establish the relationship between the phase velocity and the material’s constitutive parameters, including those related to the stiffness of both standard (first-gradient) and second-gradient types, as well as viscosity. To validate the model, we use data available in the literature to identify all the material parameters. Based on this identification, we observe that our model provides a good approximation of the experimentally measured trends of both phase velocity and attenuation versus frequency. In conclusion, this result not only confirms that our model can accurately describe both wave dispersion and attenuation in a material, as observed experimentally, but also highlights the necessity of simultaneously considering both second-gradient and viscosity parameters for a proper mechanical characterization of materials. Full article

The mechanics of an elastic sheet reinforced with fiber mesh is investigated when undergoing bilateral in-plane bending and stretching. The strain energy of FRC is formulated by accounting for the matrix strain energy contribution and the fiber network deformations of extension, flexure, and torsion, where the strain energy potential of the matrix material is characterized via the Mooney–Rivlin strain energy model and the fiber kinematics is computed via the first and second gradient of deformations. By applying the variational principle on the strain energy of FRC, the Euler–Lagrange equilibrium equations are derived and then solved numerically. The theoretical results highlight the matrix and meshwork deformations of FRC subjected to bilateral bending and stretching simultaneously, and it is found that the interaction between bilateral extension and bending manipulates the matrix and network deformation. It is theoretically observed that the transverse Lagrange strain peaks near the bilateral boundary while the longitudinal strain is intensified inside the FRC domain. The continuum model further demonstrates the bidirectional mesh network deformations in the case of plain woven, from which the extension and flexure kinematics of fiber units are illustrated to examine the effects of fiber unit deformations on the overall deformations of the fiber network. To reduce the observed matrix-network dislocation in the case of plain network reinforcement, the pantographic network reinforcement is investigated, suggesting that the bilateral stretch results in the reduced intersection angle at the mesh joints in the FRC domain. For validation of the continuum model, the obtained results are cross-examined with the existing experimental results depicting the failure mode of conventional fiber-reinforced composites to demonstrate the practical utility of the proposed model. Full article

Silicon (Si) and silicon carbide (SiC) are second- and third-generation semiconductor materials with excellent properties that are particularly suitable for applications in scenarios such as high temperature, high voltage, and high frequency. Si/SiC wafers face warpage and bending problems during production, which can seriously affect subsequent processing. Fast, accurate, and comprehensive detection of thickness, thickness variation, and flatness (including bow and warpage) of SiC and Si wafers is an industry-recognized challenge. Frequency scanning interferometry (FSI) can synchronize the upper and lower surfaces and thickness information of transparent parallel thin wafers, but it is still affected by multiple interfacial harmonic reflections, reflectivity asymmetry, and phase modulation uncertainty when measuring SiC thin wafers, which leads to thickness calculation errors and face reconstruction deviations. To this end, this paper proposes a high-precision facet reconstruction method for SiC/Si structures, which combines harmonic spectral domain decomposition, refractive index gradient constraints, and partitioning optimization strategy, and introduces interferometric signal “oversampling” and weighted fusion of multiple sets of data to effectively suppress higher-order harmonic interferences, and to enhance the accuracy of phase resolution. The multi-layer iterative optimization model further enhances the measurement accuracy and robustness of the system. The flatness measurement system constructed based on this method can realize the simultaneous acquisition of three-dimensional top and bottom surfaces on 6-inch Si/SiC wafers, and accurately reconstruct the key parameters, such as flatness, warpage, and thickness variation (TTV). A comparison with the Corning Tropel FlatMaster commercial system shows that this method has high consistency and good applicability. Full article

Hypereutectic Al-Si alloys containing primary Si exhibit unique material properties that make them suitable for various industrial applications. Understanding the characteristics of primary Si is crucial for predicting the effect of solidification conditions on the microstructure of these alloys. This paper presents a comprehensive characterisation study of primary Si in hypereutectic alloys. This study provides a detailed analysis of the size, distribution, and morphology of primary Si, providing valuable insights into the alloy structure, mechanical properties, and even the performance of the production process. The effect of forced melt flow by a rotating magnetic field (RMF) and solid/liquid front velocity on the size and morphology of primary Si in a hypereutectic Al-18 wt.% Si alloy was investigated. The purpose of using the RMF technique during the solidification process of Al-Si alloys is to enhance the alloy’s microstructure by inducing electromagnetic stirring. The hypereutectic samples were solidified at five different front velocities (0.02, 0.04, 0.08, 0.2, and 0.4 mm/s), under an average temperature gradient (G) of 8 K/mm, in a crystalliser equipped with an RMF inductor. Each sample was divided into two parts: the first solidified without stirring, while the second underwent electromagnetic stirring using RMF at an induction (B) of 7.2 mT. The results revealed that increasing the front velocity during solidification refined the primary Si in stirred and non-stirred parts. In non-stirred parts, it decreased dendritic forms and increased star-like Si, while polyhedral shapes remained nearly constant. Stirred parts showed stable Si morphology across velocities. Higher velocities also promoted equiaxed over elongated Si forms in both parts. Full article

Under the dual impetus of environmental regulations and reliability requirements, the Pb–free/Pb hybrid assembly process in aerospace-grade ball grid array (BGA) components has become an unavoidable industrial imperative. However, constrained process compatibility during single or multiple reflow protocols amplifies structural heterogeneity in solder joints and accelerates dynamic microstructural evolution, thereby elevating interfacial reliability risks at solder joint interfaces. This paper systematically investigated phase composition, grain dimensions, thickness evolution, and crystallographic orientation patterns of interfacial intermetallic compounds (IMCs) in hybrid micro-solder joints under multiple reflows, employing electron backscatter diffraction (EBSD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX). The result shows that the first reflow induces prismatic Cu₆Sn₅ grain formation driven by Pb aggregation zones and elevated Cu concentration gradients. Surface-protruding fine grains significantly increase kernel average misorientation (KAMave) of 0.68° while minimizing crystallographic orientation preference density (PF_max) of 15.5. Higher aspect ratios correlate with elongated grain morphology, consequently elevating grain size of 5.3 μm and IMC thickness of 5.0 μm. Subsequent reflows fundamentally alter material dynamics: Pb redistribution transitions from clustered to randomized spatial configurations, while grains develop pronounced in-plane orientation preferences that reciprocally influence Sn crystal alignment. The second reflow produces scallop-type grains with minimized dimensions of 4.0 μm and a thickness of 2.1 μm, with a KAMave of 0.37° and PF_max of 20.5. The third reflow initiates uniform growth of scalloped grains of 7.0 μm with a stable population density, whereas the fifth reflow triggers a semicircular grain transformation of 9.1 μm through conspicuous coalescence mechanisms. This work elucidates multiple reflow IMC growth mechanisms in Pb–free/Pb hybrid solder joints, providing critical theoretical and practical insights for optimizing hybrid technologies and reliability management strategies in high-reliability aerospace electronics. Full article

In recent years, there has been increasing interest in new materials such as ceramic matrix composites (CMCs) for power generation and aerospace propulsion applications through hydrogen combustion. This study employed a deep artificial neural network (DANN) model to predict the ablation performance of CMCs in the hydrogen torch test (HTT). The study was conducted in three phases to increase the accuracy of the model’s predictions. Initially, to predict the thermal behavior of ceramic composites, two linear machine learning models were used known as Lasso and Ridge regression. In the second step, four decision tree-based ensemble machine learning models, namely random forest, gradient boosting regression, extreme gradient boosting regression, and extra tree regression, were used to improve the prediction accuracy metrics, including root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R² score), and mean absolute percentage error (MAPE), relative to the previously introduced linear models. Finally, to forecast the thermal stability of CMCs with time, an optimized DANN model with two hidden layers having rectified linear unit activation function was developed. The data collection procedure involved preparing CMCs with continuous Yttria-Stabilized Zirconia (YSZ) fibers and silicon carbide (SiC) matrix using a polymer infiltration and pyrolysis (PIP) technique. The samples were exposed to a hydrogen flame at a high heat flux of 183 W/cm² for a duration of 10 min. A good agreement between the DANN model’s predictions and experimental data with an R² score of 0.9671, RMSE of 16.45, an MAE of 14.07, and an MAPE of 3.92% confirmed the acceptability of the developed neural network model in this study. Full article

Thermal printing is a prevalent method due to its advantages of rapid output, cost effectiveness, and ease of use. However, the quality of thermal printing is influenced by the printing speed, the temperature, and the concentration and characteristics of the materials. This research employs a BP neural network to forecast print quality, utilizing two activation functions. The findings indicate that a dual-layer hidden configuration utilizing the GeLU activation function yields a lower root-mean-square error (RMSE). The optimal configuration identified consists of six neurons in the first hidden layer and three neurons in the second hidden layer. To enhance the predictive performance, a PSO algorithm was integrated with the PSO-BP model to refine the parameter selection, which included ambient temperature, printing speed, and printing concentration, with iterative training and validation conducted via the gradient descent algorithm. The PSO-BP network achieved an MAE of 0.1108, an RMSE of 0.145, an MSE of 0.021, and an R² value of 0.9916 in predicting print quality. These results substantiate the stability and reliability of the neural network model developed with the PSO algorithm. Further validation with ten sets of test samples demonstrated that the model attained an average absolute error of 2.77% in print quality predictions, indicating robust generalization capabilities and precise forecasting. Full article

This study develops a computational framework for evaluating the residual strength of tungsten-copper functionally graded materials following crack-arrest hole drilling. The proposed methodology features two pivotal innovations: First, a phase-field isoparametric gradient elements is established through representing the gradient effect within the finite element stiffness matrices, incorporating both Amor and Miehe elastic energy decomposition schemes to address tension-compression asymmetry in crack evolution. Second, a multi-fidelity neural network strategy is integrated with the gradient phase-field element to mitigate characteristic length dependency in residual strength predictions. Comparative analyses demonstrate that the gradient finite element achieves smoother field transitions at element interfaces compared to conventional homogeneous elements, as quantified in both stress and damage fields. The Miehe decomposition scheme outperforms the Amor model in capturing complex crack trajectories. Validation against the average strain energy criterion indicates the present approach enhances residual strength prediction accuracy by 39.07% to 44.05%, establishing a robust numerical tool for damage tolerance assessment in graded materials. Full article

Gradient damage processes in cementitious materials are generally produced by chemical and/or physical processes that travel from outside to inside. Depending on the type of damage, it can cause different effects such as decreased porosity, cracking, or steel corrosion in the case of carbonation, or increased porosity, micro-cracks, expansion, and spalling (also present in thermal damage) in the case of external attack by sulphates or acid attack. Therefore, estimating the boundaries of this damage is an essential task for concrete quality assessment. The first objective of this work was to use neural networks (NNs) for ultrasound tomographic reconstruction of concrete samples in order to estimate the advance front in gradient damage. Unlike the usual X-ray tomography, ultrasound tomography is affected by diffraction, among other factors. NNs can learn to compensate for these effects; however, they require a large amount of training data to achieve accurate results. In the case of cement-based materials, obtaining and measuring a real training database could be complicated, expensive, and time-consuming. For this purpose, a training process using simulated measurements was carried out. The second objective of this work was to demonstrate the feasibility of training neural networks through simulations, which reduces costs. Finally, the trained neural network for tomographic reconstruction was evaluated using real cylindrical concrete specimens. Each specimen consisted of an outer cylinder, representing externally exposed cement, and an inner cylinder, simulating the unaffected core. The Structural Similarity Index (SSIM) was used as a metric to assess the reconstruction accuracy, achieving values of 0.95 for simulated signals and up to 0.82 for real signals. Full article

To investigate the spatiotemporal distribution of early-age hydration heat-induced temperature fields, this study integrates distributed fiber optic sensing (DFOS) technology with a thermal parameter finite element model (FEM). First, a high-precision DFOS system and traditional point-type semiconductor sensors were deployed to continuously monitor the temperature of a 50 m large-volume concrete box girder (LVBG) over 100 h. Experimental results show that full-field LVBG temperature changes can be measured by DFOS compared to traditional point sensors. DFOS, leveraging its full-scale spatial coverage capability, revealed a three-stage temperature evolution: rapid heating (peak temperature of 79.4 °C at 40 h), sustained high temperatures (>75 °C for 20 h), and gradual cooling (rate: 0.45 °C/h). In contrast, conventional point sensors may miss localized hotspots due to insufficient spatial coverage. Second, a FEM was developed on the ABAQUS 2021 (finite element analysis software) platform, incorporating a UMATHT (user material thermal) subroutine to update temperature-dependent thermal conductivity and specific heat in real time during hydration heat transfer simulations. The proposed model significantly improved prediction accuracy by integrating parameter mechanisms (equivalent age), and it improved prediction accuracy by about 40% compared to static-parameter models. The FEM results exhibited strong consistency with DFOS-measured data, validating the model’s reliability in capturing thermal gradients in geometrically complex structures. This validated framework offers a robust tool for optimizing thermal management strategies in large-scale infrastructure projects. The research results of this paper can serve as a reference for the temperature measurement and prediction of large-volume concrete. Full article

Using machine learning models, this study innovatively introduces multi-element compositions to optimize the performance of spinel refractories. A total of 1120 spinel samples were fabricated at 1600 °C for 2 h, and an experimental database containing 112 data points was constructed. High-throughput performance predictions and experimental verifications were conducted, identifying the sample with the highest hardness, (Al₂Fe_0.25Zn_0.25Mg_0.25Mn_0.25)O₄ (1770.6 ± 79.1 HV1, 3.35 times that of MgAl₂O₄), and the highest flexural strength, (Al₂Cr_0.5Zn_0.1Mg_0.2Mn_0.2)O₄ (161.2 ± 9.7 MPa, 1.4 times that of MgAl₂O₄). Further analysis of phase composition and microstructure shows that the mechanism of hardness enhancement is mainly the solid solution strengthening of multi-element doping, the energy dissipation of the large-grain layered structure, and the reinforcement of the zigzag grain boundary. In addition to solid solution strengthening and a compact low-pore structure, the mechanism of improving bending strength also includes second-phase strengthening and phase concentration gradient distribution. This method provides a promising way to optimize the performance of refractory materials. Full article