Federal University of Pernambuco

Abstract Poor thermal conductivity is common in batteries that use phase change material (PCM)-based thermal management systems (BTMS). This study introduces cylindrical rings and longitudinal fins to improve heat transfer. The thermal performance of several BTMS configurations is the primary focus of this analysis. The results show that the PCM–fin system offers superior temperature management compared to both pure PCM and pure battery systems. To understand the mechanisms, numerical simulations were compared against experimental data available in the literature. Numerical analysis shows that the fin shapes increase heat transmission and create a thermal conductive network inside the PCM, improving battery thermal performance. The study indicates that the effectiveness of thermal management is influenced by the number of extended surfaces and rings, as well as the heat generation rate. One cylindrical ring and eight longitudinal fins, with a dimensionless spacing of 0.2 between the battery and the ring, are recommended. The PCM–fin technology also effectively controls the battery's temperature rise at 20 W, proving its durability in high-heat conditions.

This study investigates the influence of ethanol and n-dodecane fuel blends on the performance and emissions of HCCI engines, with particular attention to how different ethanol concentrations impact piston work, in-cylinder pressure, temperature, and emissions such as CO2, CO, NOx, and soot. The findings demonstrate that increasing the ethanol content reduces CO and soot emissions due to the fuel's higher oxygen content and cleaner combustion profile. However, NOx emissions increase, likely resulting from leaner air–fuel mixtures that raise combustion temperatures. The 80% n-dodecane blend exhibits higher peak in-cylinder pressures and temperatures, indicating enhanced combustion efficiency. Ethanol’s higher latent heat of vaporization introduces a cooling effect that can delay its vaporization, promoting better fuel–air mixing but also potentially leading to incomplete combustion and lower in-cylinder pressure. Although ethanol-enriched blends can improve combustion efficiency and reduce certain emissions, the increase in NOx emissions underscores the complexity of balancing engine performance, combustion stability, and emissions control. This demonstrates the trade-off between improved combustion properties and environmental concerns when using fuel blends in HCCI engines.

In order to achieve a process for precision asphere and freeform surfaces, a medium-pressure plasma process (MPPP) is developed for high-rate precision machining of optical materials. Fused silica is commonly used to fabricate optical components such as mirrors, lenses, prisms, and photonic crystals. Plasma polishing, an unconventional method, is employed for the atomic-level removal of material from a substrate’s surface. While polishing the fused silica substrate, the focus lies on investigating process parameters, specifically the radio frequency (RF) power, sulfur hexafluoride/oxygen (SF6/O2) ratio, and plasma chamber pressure. The process parameters are maintained throughout all experiments, specifically a consistent sulfur hexafluoride/oxygen (SF6/O2) ratio of 1:1 and He:(SF6 + O2) of 90:10. The surface roughness, elemental composition, and morphology of the fused silica surface are examined by the three-dimensional (3D) optical profiler, energy-dispersive X-ray spectroscopy (EDX), and field emission scanning electron microscopy (FESEM), respectively. After the plasma process, the surface roughness changes marginally without any surface contaminations. However, upon removing the chemically modified layer, the etching process results in the development of a succession of pits. During polishing, the distinct pits merged together, forming a cohesive structure. Further, the contact angle is also examined on silicon dioxide (SiO2) surface before and after plasma process. The contact angle decreases from 89.6° to 23.2° before and after plasma polishing, respectively.

Abstract Magnetic domain walls are promising information carriers for developing next-generation high-processing speed spintronic devices. While extensive research has been conducted on field- and current-driven domain wall propagation from fundamental theoretical and practical applications viewpoint, the strain-controlled manipulation of domain walls in magnetostrictive materials with different crystal structures has recently gained significant attention. In this work, we theoretically investigate strain-driven domain wall motion in a transversely isotropic hexagonal magnetostrictive layer, incorporating the influence of nonlinear viscous damping. Our analysis is based on the one-dimensional extended Landau–Lifshitz–Gilbert equation, which captures the combined effects of a tunable magnetic field, spin-polarized current, magnetoelastic and anisotropy fields, and crystal symmetry. By applying the traveling wave method, we derive expressions for key dynamics such as the traveling wave profile, Walker breakdown, domain wall width, and velocity across both steady and precessional regimes. The results show that nonlinear viscous damping significantly influences domain wall motion, altering velocity behavior and expanding the steady propagating regime by shifting the Walker breakdown limit. In addition, the orientation of the magnetic field modulates the threshold and breakdown limits, affecting the range of steady propagation. Also, the numerical illustrations of the obtained analytical results yield a good qualitative agreement with recent observations.

Nowadays, the finishing of optical components has drawn the attention of many researchers. The recent optics industries need precise surface quality with negligible subsurface defects. Surface characteristics and bulk properties of optics play a significant role in the performance and life of the optical device. Surface finish and surface integrity requirements of optical elements used in navigation-grade inertial sensors such as gyros and accelerometers are extremely demanding. A novel non-contact medium-pressure plasma process is designed and developed to get an ultra-smooth surface with a minimum sub-surface defect for optical components. In the current study, experiments are carried out at RF power of 80, 40, and 20 W for different plasma chamber pressures, i.e., 5, 10, and 20 mbar, with fixed parameters, i.e., 90:10 gas composition and machining time of 45 min. The results reveal the maximum reduced thickness and material removal rate achieved are 6.67 μm and 0.11 mm3/min, respectively. FESEM images reveal that surface cracks, micro holes, grooves, and tearings are reduced after the plasma processing. Surface roughness value after plasma treatment slightly increases from 2.36 to 2.86 µm without any surface contamination at optimal parametric conditions. EDX outcomes show that elements C, O, Ce, and Si appear on fused silica before processing, and Si, F, and O are observed after plasma treatment. The occurrence of F atoms reveals the reactions during plasma processing with the substrate surface. The contact angle measurements show that the measured contact angle decreases after plasma processing. Moreover, the UV–Vis (ultraviolet–visible) is examined before and after plasma polishing.

Physics-informed machine learning (PIML) represents a promising area within Laser-Assisted Machining (LAM) process. However, previous approaches have heavily relied on extensive datasets for their success, posing challenges given the limited availability of data in LAM process. Addressing this issue, the current study introduces new Architecture-guided PIML (APIML) framework established on deep unfolding, tailored by scenarios with sparse datapoints. Specifically, APIML is designed to predict thermal histories in the LAM process. The architectural design of APIML draws inspiration from iterative temperature model equations, where each iteration corresponds to a neural network layer. APIML framework hyperparameters are meticulously optimized, and their performance rigorously evaluated. Notably, when tested with thousand data points split at an 80:20 ratio, APIML achieves a mean absolute percentage error (MAPE) of 3.6% and an R2 value of 0.941 Comparative analysis pits APIML against traditional machine learning models including artificial neural network, decision tree regressor, random forest regressor, and support vector regressor. Results demonstrate APIML’s superior performance: it achieves a 56.98% lower MAPE and a 17.1% higher R2 compared to the best-performing decision tree regressor model among the traditional approaches. In essence, APIML showcases promising advancements in leveraging physics-informed machine learning to overcome data scarcity challenges in LAM process, offering more accurate predictions of thermal histories crucial for optimizing machining processes.

The growing demand for advanced materials in engineering applications necessitates the development of composites with enhanced mechanical properties and machinability. This study explores the mechanical properties and machinability of a novel smart hybrid composite composed of Aluminium-Copper (Al-Cu) alloy reinforced with Silicon Carbide (SiC) and Graphene Nanoplatelets (GNPs). The hybrid composite was fabricated using a stir casting method, ensuring uniform dispersion of reinforcements to achieve superior mechanical characteristics. Mechanical properties, including tensile strength, hardness, and compressive strength, were experimentally determined and analysed. Furthermore, the machinability of the hybrid composite was evaluated using Water Jet Machining (WJM), a non-conventional machining technique known for its precision and versatility in handling advanced materials. Key machining parameters such as kerf width, surface roughness, and material removal rate were optimized to assess the machinability of the developed composite. To enhance the understanding of the relationship between composition, mechanical properties, and machinability, a Machine Learning (ML) model was developed using Artificial Neuron Network, Random Forest Regressor and Decision Tree algorithms. The ML model predicted the outcomes of mechanical testing and machining, providing a reliable framework for optimizing composite material design. The results reveal that the addition of SiC and GNPs significantly improves the composite's mechanical properties while maintaining favourable machinability characteristics under WJM. This study demonstrates the potential of smart hybrid composites in engineering applications where both high performance and efficient machinability are essential, and it highlights the effectiveness of machine learning in optimizing material development processes.

ABSTRACTThis article presents a novel strategy for reducing mutual coupling and Radar Cross Section (RCS) in a 2 × 1 MIMO antenna system tailored for military radar applications. The proposed arrangement involves two identical radiators oppositely arranged with an H‐shaped strip placed between the radiators. A wide slot, stub projection, and frequency selective surface (FSS) are engraved on the backside. The overall dimension of the suggested system is 25 × 25 × 1.6 mm3, which resonates at 6.09 and 8.34 GHz with an impedance bandwidth of 6.16 GHz from 2.95 to 9.11 GHz. The isolation (≤ −30 dB) is obtained by incorporating a wide slot and an H‐shaped strip between the radiators. A grid of miniature square slots is etched on the rear side of the antenna, leading to an additional enhancement in the isolation and monostatic RCS reduction by more than 25 dB. The diversity gain (DG) and envelope correlation coefficient (ECC) are also examined for the suggested antenna to validate its diversity performance. The congruence between the validated and simulated results is remarkably strong, rendering it a fitting choice for military and airborne devices/applications operating within the C and X bands.

Plastics are widely used across various applications from packing to commercial products. Once discarded, they were subjected to environmental stresses, causing them to degrade into microplastics (MPs). These small, invisible pollutants pose a significant threat to aquatic ecosystems, gradually compromising the resilience and vitality of the natural environment. Moreover, MPs will act as carriers for other contaminants, for example, heavy metals (HMs). Although many studies have explored MPs and HMs independently, their combined behavior and interactions remain poorly understood. Understanding these interactions is increasingly important given rising pollution levels. MP formation and adsorption behavior are heavily influenced by factors such as UV aging, which remains unclear. In this study, both virgin and UV-aged MPs specifically PET and PP (the most widely used plastics globally) were examined in their interactions with copper (Cu2+) solutions. Surface analysis techniques such as FTIR, SEM, XRD, and AAS were employed to compare the virgin and UV-aged MPs. The results revealed that UV-aged MPs exhibited high adsorption capacities for HMs compared to virgin MPs, which can be attributed to increased pore volume and oxidative degradation. Adsorption capacity differences at various concentrations showed up to a 20% increase, with UV-aged PET MPs displaying capacities ranging from 0.6 to 3.54 mg/g. Similarly, UV-aged PP MPs showed a 15% increase in adsorption capacity ranging from 1.51 to 4.25 mg/g. The present study provided the significant evidence on the behavior of MPs adsorption and underscores the need for further research on the long-term environmental impacts of aged MPs and their interactions with pollutants.

Amid an increase in global electricity demand and concerns over climate change, fossil fuels are driven primarily by a lack of supply. To overcome these concerns, distributed energy resources (DERs) are considered an alternative. However, DERs produce uncontrollable and stochastic power output, which creates imbalances in generation and demand profiles. Further, errors introduced due to forecasting of power output from photovoltaic (PV), wind turbine (WT), load profile, and electricity prices also create an imbalance power (IP). During the forecasting stage, the presence of outliers in the historical datasets impacts the accuracy and performance of the forecasting model; therefore, there is a need to detect and impute these outliers into the actual data before forecasting. A two-stage scheduling methodology was proposed to mitigate the IP arising in the real-time market due to uncertainties and errors in forecasting. In the first stage, the outliers are detected using K-means and autoencoder, imputing these outliers with the neighbors using K-nearest neighbor, and in the next stage, dispatch schedule of each generator was found to mitigate the difference between forecasted and actual values. The hybrid approach starts with data segmentation and applies K-means clustering, which divides data points that are associated with clusters and helps in the identification of underlying patterns and anomalies. After that, anomalies within each cluster are found using an autoencoder neural network, which becomes more adept at identifying intricate nonlinear relationships in the data. The reconstruction error of the autoencoder is used to identify abnormalities. The KNN technique makes sure that values that are imputed are relevant to the context and do not add bias to the dataset. The approach is validated by using an IEEE-33 bus system where the analysis of the impact of electricity price variation on the operational expenditure and the impact of variation in load demand on operational cost has been analyzed.

In the present study, Laser-Assisted Machining was opted to obtain the machinability characteristics of Niobium alloy (C-103). The experiments were conducted by considering input variables, known as independent cutting parameters such as speed, laser power, feed, and depth of cut and the output parameters, known as responses like cutting force and surface finish of the cutting process. L9 orthogonal array was used to conduct the experiments. Taguchi technique implemented to optimize the responses. Cutting force reduces with an increase in speed and laser power and increases with an increase in feed and depth of cut. Surface roughness increases with the depth of cut and feed rate and decreases with an increase in speed and laser power. Also found, the laser has a significant effect on the responses compared to other machining parameters. Regression models for cutting force and surface roughness were developed. The effectiveness of these models was checked with regression co efficient, R-Square = 98.21%, Adjusted R-Square = 96.42%, Predicted R-Square = 92.93% for cutting force and R-Square = 92.82%, Adjusted R-Square = 89.63%, Predicted R-Square = 87.45% for surface roughness. The predicted cutting force and surface roughness for the optimal laser power of 550W, depth of cut of 0.25 mm, speed of 540 rpm and feed rate of 0.02 mm/rev (A3B1C3D1) was 24.71 N and 0.3413 µm respectively. The actual cutting force and surface roughness obtained from experiments for the same process parameters were 26.44 N and 0.375 µm respectively. The percentage of error for cutting force and surface roughness is 6.54 and 9.33 respectively which is acceptable statistically. The results obtained from laser assisted machining were also compared with conventional machining.