Search Results (15,920)

Stray current-induced corrosion poses a significant risk to the durability of reinforced concrete (RC) structures in electrified transit systems. This study addresses a critical knowledge gap by experimentally and analytically investigating the compression behaviors of circular RC columns under the combined effects of stray currents, chloride intrusion, and sustained service loads. The experimental program involved testing columns constructed with normal strength concrete (NSC) and moderate strength concrete (MSC) under accelerated corrosion induced by electrical potentials of 9 V and 18 V in a 3.5% NaCl solution. A key variable was the application of a sustained axial load, equal to 60% of the ultimate capacity, to simulate realistic service conditions. The findings revealed a severe deterioration in structural performance due to the synergistic effect of mechanical loading and corrosion. NSC columns subjected to 18 V potential and sustained axial loading exhibited a decrease in ultimate load-carrying capacity of up to 46% and a ductility reduction of approximately 69% compared to reference specimens. This damage was significantly more severe than in unloaded or lower-voltage (9 V) scenarios. Furthermore, MSC specimens demonstrated a strength loss of approximately 29% under similar aggressive conditions. An analytical confinement model, adjusted to account for corrosion by reducing the reinforcement cross-section and introducing a semi-empirical parameter α to represent localized pitting, showed strong agreement with the experimental stress–strain curves. The validated model provides a practical tool for assessing the residual capacity of corroded elements, addressing a crucial need in the maintenance of electrified transportation infrastructure. Full article

To address the stringent insulation safety requirements of modern high-voltage transformers, accurately characterizing the transient electric field is critical. However, a significant problem remains: current engineering models typically rely on static capacitive distributions, failing to capture the dynamic electric field distortion induced by rapid space charge injection under lightning impulses. Therefore, a non-contact spatial electric field measurement method based on the optical Kerr effect was employed to analyze the influence of electrode material, voltage amplitude, and wavefront time. Unlike traditional simulation models that often assume constant mobility and focus solely on the shielding effect, this study reveals a non-monotonic electric field evolution driven by a ‘Static-Dynamic’ mode transition. The proposed model highlights two critical breakthroughs: (1) Mechanism Innovation: It experimentally verifies that charge injection is governed by the ion charge-to-mass ratio rather than just the work function, leading to a newly identified field enhancement phase during the wavefront that overcomes the limitations of capacitive models that underestimate transient stress. (2) Parameter Quantification: Precise spatiotemporal thresholds are established—negative charges traverse the gap within ~200 ns, while positive charges require ~10 μs to reach equilibrium. These findings provide experimentally calibrated time constants for simulation correction and offer new criteria for optimizing electrode materials in UHV transformers to mitigate transient field distortion. Full article

Background: Type 1 diabetes (T1D) is associated with metabolic and neuromuscular impairments that may influence fatigue mechanisms and limit exercise tolerance. Although previous investigations have characterized muscle performance in T1D, the peripheral fatigue threshold, defined as the maximal sustainable level of peripheral fatigue, remains poorly understood in this population. This study aimed to compare the amplitude of the maximal peripheral fatigue threshold between individuals with T1D and healthy controls to elucidate the effects of T1D on neuromuscular function. Methods: Twenty-two participants (11 with T1D and 11 healthy controls) completed two randomized experimental sessions. In each session, 60 quadriceps maximal voluntary contractions (MVCs) were completed, performed for 3 s with 2 s of rest between contractions. One session was conducted under a non-fatigued control condition (CTRL), and the other followed a fatiguing neuromuscular electrical stimulation (FNMES) protocol. Central and peripheral fatigue were evaluated from the pre- to post-exercise changes in potentiated twitch force (ΔPtw) and voluntary activation (ΔVA), respectively. Critical torque (CT) was calculated as the average torque produced during the last 12 contractions, whereas the curvature constant of the torque–duration relationship (W′) was quantified as the area above CT. Results: Although both groups exhibited a decline in pre-exercise Ptw following the FNMES condition, no significant within-group differences in ΔPtw were observed between sessions (T1D: p = 0.34; controls: p = 0.23). Nevertheless, the extent of peripheral fatigue was significantly lower in participants with T1D than in controls (ΔPtw = −38 ± 11% vs. −52 ± 17%; p < 0.05). Additionally, W′ values were reduced by 24% in the T1D group relative to controls during the CTRL condition (p = 0.02), and CT was significantly lower in T1D participants (262 ± 49 N) compared to controls (353 ± 71 N; p < 0.01). A significant positive correlation was observed between ΔPtw and W′ across groups (r² = 0.62, p < 0.001), suggesting a mechanistic link between peripheral fatigue tolerance and work capacity. Conclusions: The present results indicate that, although individuals with T1D retain the capacity to develop peripheral fatigue, their fatigue threshold and critical torque are markedly attenuated relative to those of healthy individuals. This reduction reflects impaired neuromuscular efficiency and diminished tolerance to sustained contractile activity. The strong relationship between peripheral fatigue and work capacity underscores the contribution of peripheral mechanisms to exercise intolerance in T1D. These results enhance current understanding of fatigue physiology in diabetes and emphasize the need for tailored exercise and rehabilitation strategies to improve fatigue resistance and functional performance in this population. Full article

Different crops have varying effects on soil factors, and their associated microbial community compositions also differ. Currently, there is limited comparative research on crops with distant phylogenetic relationships, such as those between gramineous and leguminous species. In this study, a pot experiment combined with high-throughput sequencing was conducted to enable a detailed comparison of microbial communities and soil factors across four crops: wheat, soybean, and two maize varieties. Compared to leguminous crops, differences between gramineous crops may be relatively smaller. The results showed that among the gramineous and leguminous crops, soybean had the lowest effect on soil electrical conductivity (EC) and available phosphorus (AP) (121.68 ± 2.70, 34.74 ± 1.02). The dominant fungi and bacteria phyla were Ascomycota and Proteobacteria; both were most abundant in the ZD958 variety, at 75.12% and 30.47%, respectively. The fungal diversity of ZD958 was most similar to that of W998, whereas the bacterial diversity of XY335 more closely resembled that of SB13. EC and AP were the key factors influencing fungal community composition, while alkali-hydrolyzable nitrogen (AN) was the key factor affecting bacterial community composition. These findings provide a basis for further in-depth research. Full article

Hydrogenated amorphous silicon (a-Si:H) is a mature thin-film technology for large-area devices and thin-film sensors, and its low-temperature growth via Plasma-Enhanced Chemical Vapor Deposition (PECVD) makes it particularly suitable for biomedical flexible and wearable platforms. However, the reliable integration of a-Si:H sensors on polymer substrates requires a quantitative assessment of their electrical stability under mechanical stress, since bending-induced variations may affect sensor accuracy. In this work, we provide a quantitative, direction-dependent evaluation of the static-bending robustness of both single-doped a-Si:H layers and complete p-i-n junction stacks on polyimide (Kapton^®), thereby linking material-level strain sensitivity to device-level functionality. First, n- and p-doped a-Si:H layers were deposited on 50 µm thick Kapton^® and then structured as two-terminal thin-film resistors to enable resistivity extraction under bending conditions. Electrical measurements were performed on multiple samples, with the current path oriented either parallel (longitudinal) or perpendicular (transverse) to the bending axis, and resistance profiles were determined as a function of bending radius. While n-type layers exhibited limited and mostly gradual variations, p-type layers showed a stronger sensitivity to mechanical stress, with a critical-radius behavior under transverse bending and a more progressive evolution in the longitudinal one. This directional response identifies a practical bending condition under which doped layers, particularly p-type films, are more susceptible to strain-induced degradation. Subsequently, a linear array of a-Si:H p-i-n sensors was fabricated on Kapton^® substrates with two different thicknesses (25 and 50 µm thick) and characterized under identical bending conditions. Despite the increased strain sensitivity observed in the single-layers, the p-i-n diodes preserved their rectifying behavior down to the smallest radius tested. Indeed, across the investigated radii, the reverse current at −0.5 V remained consistent, confirming stable junction operation under bending. Only minor differences, related to substrate thickness, were observed in the reverse current and in the high-injection regime. Overall, these results demonstrate the mechanical robustness of stacked a-Si:H junctions on polyimide and support their use as sensors for wearable biosensing architectures. By establishing a quantitative, orientation-aware stability benchmark under static bending, this study supports the design of reliable a-Si:H flexible sensor platforms for curved and wearable surfaces. Full article

In today’s world, the demand for compact, high-efficiency, and low-cost motors plays a significant role in the design of low-power electric machines. In combi fan applications, single-phase brushless direct current (BLDC) motors are generally preferred. Although these motors offer efficient and compact solutions, the occurrence of dead points at certain rotor positions creates a serious disadvantage that may prevent the motor from initiating motion. In this study, an asymmetric air gap design is proposed for a single-phase BLDC motor to eliminate the dead point problem and increase starting torque. The motor’s performance has been evaluated through analytical calculations and two-dimensional finite element analysis (FEA) conducted using ANSYS Electronics Desktop 2020 R2 (Maxwell) software. The results show that the asymmetric air gap effectively eliminates the dead point and improves the motor’s starting performance. However, torque ripple is still identified as a design parameter that must be considered. The scope of this study is not limited to single-phase BLDC motors; it also provides analytical approaches that can be applied to different electric motor designs, contributing to engineering applications in this field. Full article

The rapid growth of electric vehicle (EV) deployment has created a strong demand for charging systems capable of handling higher power levels while preserving grid stability and maintaining satisfactory energy quality. In this work, a fast-charging architecture for 400 V battery systems is developed using a Vienna rectifier on the AC front end and a DC–DC buck converter on the DC stage. To enhance the performance of this topology, two complementary control techniques are combined: the Crystal Structure Algorithm (CryStAl), used for offline optimization of switching behavior, and a Random Decision Forest (RDF) model, employed for real-time adaptation to operating conditions. A clear, step-oriented derivation of the converter state–space equations is included to support controller design and ensure reproducibility. This control framework improves the key performance indices, including Total Harmonic Distortion (THD), ripple suppression, efficiency, and power factor correction. Specifically, the Vienna rectifier works on input current shaping and enhances the power quality, while the buck converter maintains a constant DC output appropriate for reliable battery charging. The simulation studies show that the combined CryStAl–RDF approach outperforms the conventional PI- and Particle Swarm Optimization (PSO)-based controllers. The proposed method achieves THD less than 2%, conversion efficiency higher than 97.5%, and a power factor close to unity. The voltage and current ripples are also significantly reduced, which justifies the extended life of the batteries and reliable charging performance. Overall, the results portray the potential of the combined metaheuristic optimization with machine learning-based decision techniques to enhance the behavior of power electronic converters for EV fast-charging applications. The proposed control method offers a practical and scalable route for next-generation EV charging infrastructure. Full article

The transition to renewable energy systems and electric mobility depends on the effectiveness, reliability, and durability of lithium-ion battery technology. Accurate modeling and control of battery systems are essential to ensure safety, efficiency, and cost-effectiveness in electric vehicles and grid storage. In engineering and materials science, battery models depend on physical parameters such as capacity, energy, state of charge (SOC), internal resistance, power, and self-discharge rate. These parameters are affected by measurement uncertainty. Despite the widespread use of lithium-ion cells, few studies quantify how measurement uncertainty propagates to derived battery parameters and affects predictive modeling. This study quantifies how uncertainty in voltage, current, and temperature measurements reduces the accuracy of derived parameters used for simulation and control. This work presents a comprehensive uncertainty analysis of 18650 format lithium-ion cells with nickel cobalt aluminum oxide (NCA), nickel manganese cobalt oxide (NMC), and lithium iron phosphate (LFP) cathodes. It applies the law of error propagation to quantify uncertainty in key battery parameters. The main result shows that small variations in voltage, current, and temperature measurements can produce measurable deviations in internal resistance and SOC. These findings challenge the common assumption that such uncertainties are negligible in practice. The results also highlight a risk for battery management systems that rely on these parameters for control and diagnostics. The results show that propagated uncertainty depends on chemistry because of differences in voltage profiles, kinetic limitations, and temperature sensitivity. This observation informs cell selection and testing for specific applications. Improved quantification and control of measurement uncertainty can improve model calibration and reduce lifetime and cost risks in battery systems. These results support more robust diagnostic strategies and more defensible warranty thresholds. This study shows that battery testing and modeling should report and propagate measurement uncertainty explicitly. This is important for data-driven and physics-informed models used in industry and research. Full article

It is now crucial to accurately monitor the state of health (SoH) of batteries in a setting where the use of electric vehicles (EVs) and renewable energy technologies is still growing. To solve this issue and evaluate the SoH, this paper makes use of deep learning technology. The suggested method incorporates voltage, current, and temperature data, which are important indications of the SoH and can potentially be obtained directly from the battery management system (BMS). Although deep neural networks (DNNs) have previously been employed for SoH estimation, our study distinguishes itself by implementing a robust, completely configurable DNN application in MATLAB/Simulink R2019a. This design enables the adjustment of activation functions, layer depth, and neuron count to adapt to different battery aging conditions. To achieve optimal performance, numerous configurations were examined, highlighting the relevance of hyperparameter setting. Our technique avoids traditional feature engineering while providing a practical, adaptive, and accurate SoH estimate framework appropriate for real-world integration. The precision of the improved model was then verified against a Li-ion battery dataset with various discharge profiles given by the national aeronautics and space administration (NASA). The collected findings revealed that the proposed method is more accurate and robust than other regularly used models. The DNN model achieved a Mean absolute error (MAE) of 1.433% and a Coefficient of determination of 0.99998, outperforming previous methods such as CNN-BiGRU, which reported an MAE of 2.448% in a recent publication. This study demonstrates the reliable performance of the DNN in predicting the SoH of Li-ion cells. Full article

The widespread adoption of nonlinear loads in industry has introduced significant power quality issues in electric power distribution grids. The integration of these nonlinear loads has led to the proliferation of serious power quality problems such as the generation of harmonics and reactive power that negatively impact the quality and stability of the electrical grid. In addition to eliminating current harmonics, a shunt active power filter (APF) can also provide reactive power compensation. By dynamically adjusting the reactive power injection, these APFs can improve the power factor of the system and maintain the desired voltage regulation. The proposed control leverages the differential flatness property of the SAPF system, allowing for exact linearization and simplified tracking control without requiring complex modulation techniques. In this paper, a flatness-based control scheme is proposed for a three-phase three-level Neutral Point Clamped (NPC) APF. The main objectives of this work are twofold. The first objective is to mitigate current harmonics and compensate the reactive power drawn by nonlinear loads. The second objective focuses on maintaining a stable DC-link capacitor voltage of the active power filter (APF). To meet these requirements, a cascaded control structure is used, where the external loop regulates the DC-link voltage, while the inner loop is responsible for harmonic current compensation. The effectiveness of the proposed control strategy is validated through simulation results obtained using the MATLAB/Simulink R2024a environment. Full article

Vehicle-to-grid (V2G) services are gaining attention as a strategy to integrate electric vehicles (EVs) into sustainable energy systems. Although technological aspects have been widely studied, methodologies for identifying optimal V2G hubs and forecasting the energy available for grid transfer remain limited. This study introduces a data-driven approach to (i) identify the optimal V2G region based on the aggregated parking duration using floating car data (FCD; collected from GPS-enabled vehicles); (ii) estimate the surplus battery capacity of electric vehicles in that region; and (iii) forecast the energy transferable to the grid. The methodology applies spatial k-means clustering to define candidate zones, computes aggregated parking durations, and selects the optimal hub. The surplus energy is estimated considering the daily mobility needs of users, 20% reserve, and transfer rates. For forecasting, autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are implemented and compared. The proposed methodology has been applied to a real case study, using 58 days of FCD observations. The empirical findings of this study show the goodness of the proposed methodology, and the opportunity offered V2G technology to support the sustainable use of energy. The ARIMA model demonstrated a superior forecasting performance with an RMSE of 52.424, MAE of 36.05, and MAPE of 12.98%, outperforming LSTM (RMSE of 99.09, MAE of 80.351, and MAPE of 53.20%) under the current data conditions. The results of this study suggest that for supporting grid needs of a medium-sized city, V2G plays a key role, and at the current status of the EV penetration, the use of FCD and predictive approaches is paramount for making an informed decision. Full article

Electric vehicles (EVs) are widely recognized as a key strategy for improving global sustainability; however, their implications for building safety, particularly under fire conditions, require further investigation. This study examines the structural response of reinforced concrete (RC) beams exposed to EV fire scenarios, which are characterized by more severe thermal demands than the ISO 834 standard fire curve adopted in structural fire design, including EN 1992-1-2. A coupled thermal–mechanical finite element analysis (FEA) was performed on nine RC beams, considering variations in reinforcement layout, rebar diameter, and concrete cover thickness. When compared with fire resistance times predicted by standardized design procedures, the numerical results indicate that EV fires accelerate building damage by up to 27% within the first 60 min of exposure. Increasing the concrete cover to at least 30 mm and adopting multiple reinforcement layers were shown to enhance fire performance by reducing heat transfer to the steel reinforcement and lowering stress levels within the cross section. The findings demonstrate that current fire design provisions may underestimate the structural demands imposed by EV fire scenarios. Consequently, this study highlights the need to revise fire resistance criteria and reinforcement detailing rules to ensure adequate safety and resilience of RC structures in sustainable built environments subjected to emerging EV fire hazards. Full article

Community energy systems are expected to play an increasingly important role in the decarbonization of the residential sector, but their operation depends on how different electricity and heat storage technologies are configured and used. Existing studies typically examine storage options in isolation, limiting the comparability of their operational roles. This study addresses this gap by developing a decision-support framework that enables a consistent, operation-focused comparison of battery energy storage, hydrogen storage, and electric-vehicle-based storage within a unified community-scale hybrid energy system. The model represents electricity and heat balances in a hub formulation that couples photovoltaic and wind generation, a gas engine, an electric boiler, thermal and electrical storage units, hydrogen conversion and storage, and an aggregated fleet of electric vehicles. It is applied to a stylized Polish residential community using local demand, generation potential, and electricity price data. A set of single-technology and multi-technology scenarios is analyzed to compare how storage portfolios affect self-sufficiency, self-consumption, grid exchanges, and operating costs under current electricity market conditions. The results show that battery and electric vehicle storage primarily provide short-term flexibility and enable price-driven arbitrage, as reflected in the highest contribution of battery discharge to the electricity supply structure (5.6%) and systematic charging of BES and EVs during low-price hours, while hydrogen storage supports intertemporal shifting by charging in multi-hour surplus periods, reaching a supply share of 1.4% at the expense of substantial conversion losses. Moreover, the findings highlight fundamental trade-offs between cost-optimal, price-responsive operation and autonomy-oriented indicators such as self-sufficiency and self-consumption, showing how these depend on the composition of storage portfolios. The proposed framework, therefore, provides decision support for both technology selection and the planning and regulatory assessment of community energy systems under contemporary electricity market conditions. Full article

Droplet-based microfluidics has emerged as a powerful platform for precise fluid manipulation in biomedical, chemical, and material science applications. Among various geometries, T-junction microchannels are widely utilized for droplet generation and splitting due to their simplicity and reliability. This review provides a comprehensive overview of droplet splitting mechanisms in T-junction microfluidic systems, with particular emphasis on the role of actuation methods in enhancing control and functionality. We first discuss the fundamental physics governing droplet behavior, including the influence of capillary and viscous forces, flow regimes, and geometric parameters. Passive strategies based on flow rate tuning and channel design are outlined, followed by an in-depth examination of active actuation techniques: thermal, electrical, magnetic, acoustic, and pneumatic and their effects on droplet dynamics. In addition, the review highlights computational modeling approaches and experimental tools used to characterize and predict splitting behavior. Finally, we explore the current challenges and future directions in integrating multifunctional actuation systems for real-time, programmable droplet control in lab-on-a-chip platforms. This article serves as a foundational resource for researchers aiming to advance microfluidic droplet manipulation through actuator-enabled strategies. Full article

Heavy-duty vehicles dominate global freight movement and primarily rely on fossil-derived diesel fuel. However, fluctuations in crude oil prices and evolving emissions regulations have prompted interest in alternative powertrains to enhance fleet energy resiliency. This study paired real-world operational data from a large commercial fleet with high-fidelity vehicle models to evaluate the potential for replacing diesel internal combustion engine (ICE) trucks with alternative powertrain architectures. The baseline vehicle for this analysis is a diesel-powered ICE truck. Alternatives include ICE trucks fueled by bio- and renewable diesel, compressed natural gas (CNG) or hydrogen (H₂), as well as plug-in hybrid (PHEV), fuel cell electric (FCEV), and battery electric vehicles (BEV). While most alternative powertrains resulted in some payload capacity loss, the overall fleetwide impact was negligible due to underutilized payload capacity for the specific fleet considered in this study. For sleeper cab trucks, CNG-powered trucks achieved the highest replacement potential, covering 85% of the fleet. In contrast, H₂ and BEV architectures could replace fewer than 10% and 1% of trucks, respectively. Day cab trucks, with shorter daily routes, showed higher replacement potential: 98% for CNG, 78% for H₂, and 34% for BEVs. However, achieving full fleet replacement would still require significant operational changes such as route reassignment and enroute refueling, along with considerable improvements to onboard energy storage capacity. Additionally, the higher total cost of ownership (TCO) for alternative powertrains remains a key challenge. This study also evaluated lifecycle impacts across various fuel sources, both fossil and bio-derived. Bio-derived synthetic diesel fuels emerged as a practical option for diesel displacement without disrupting operations. Conversely, H₂ and electrified powertrains provide limited lifecycle impacts under the current energy scenario. This analysis highlights the complexity of replacing diesel ICE trucks with admissible alternatives while balancing fleet resiliency, operational demands, and emissions goals. These results reflect a US-based fleet’s duty cycles, payloads, GVWR allowances, and an assumption of depot-only refueling/recharging. Applicability to other fleets and regions may differ based on differing routing practices or technical features such as battery swapping. Full article