Search Results (1,150)

Machining nickel-based superalloys, such as Inconel 718, poses a significant technological challenge due to their high-temperature strength and low thermal conductivity, leading to rapid tool wear. This paper presents a comprehensive comparative analysis of two roughing strategies: high-feed milling and plunge milling, utilizing a unique custom-designed milling head. The primary objective was to evaluate the impact of tool material reinforcement on the process by comparing SiC whisker-reinforced ceramic inserts (CW100) with non-reinforced inserts (CS300). The experiment involved measuring cutting force components, power consumption, and analyzing tool wear progression (VBB) and mechanisms. Results showed that the presence of the reinforcing phase is critical for reducing the axial force component (Fz), particularly in plunge milling, where CW100 inserts achieved a 30–35% force reduction and avoided the catastrophic failure observed in non-reinforced ceramics. Microscopic analysis confirmed that composite inserts undergo predictable abrasive wear, whereas CS300 inserts are prone to brittle fracture and spalling. Multi-criteria optimization using Grey Relational Analysis (GRA) identified high-feed milling with reinforced inserts as the most efficient strategy, while also positioning plunge milling with composites as a competitive, less energy-intensive alternative. Full article

To address the fluctuation and instability of renewable power generation and the steady-state demands of chemical processes, a single-channel, non-isothermal computational fluid dynamics 3D model was developed. This model explicitly incorporates the coupling effects of electrochemical reactions, two-phase flow, and heat transfer. Subsequently, the influence of key operating parameters on proton exchange membrane water electrolyzer (PEMWE) system performance was investigated. The model accurately predicts the current–voltage polarization curve and has been validated against experimental data. Furthermore, the CFD model was employed to investigate the coupled effects of several key parameters—including operating temperature, cathode pressure, membrane thickness, porosity of the porous transport layer, and water inlet rate—on the overall electrolysis performance. Based on the numerical simulation results, the evolution of the ohmic polarization curve under temperature gradient, the block effect of bubble transport under high pressure, and the influence mechanism of the microstructure of the multi-space transport layer on gas–liquid, two-phase flow distribution are mainly discussed. Operational strategy analysis indicates that the high-efficiency mode (4.3–4.5 kWh/Nm³) is suitable for renewable energy consumption scenarios, while the economy mode (4.7 kWh/Nm³) reduces compression energy consumption by 23% through pressure–temperature synergistic optimization, achieving energy consumption alignment with green ammonia synthesis processes. This provides theoretical support for the optimization design and dynamic regulation of proton exchange membrane water electrolyzers. Full article

This paper presents a systematic review of linear motor-based electromagnetic suspension, a key technology for reconciling vehicle comfort, handling stability, and energy consumption. The review focuses on two core areas: actuator configuration and control strategy. In configuration design, a comparison of moving-coil, permanent magnet synchronous (PMSLM), and switched-reluctance linear motors identifies the PMSLM as the mainstream approach due to its high-power density and performance. Key design challenges for meeting stringent vehicle operating conditions, such as mass-volume optimization, thermal management, and high reliability, are also analyzed. Regarding control strategy, the review outlines the evolutionary path from classical to advanced and intelligent control. It also examines the energy-efficiency trade-off between vibration suppression and energy recovery. Furthermore, the paper summarizes three core challenges for industrialization: nonlinear issues like thrust fluctuation and friction, the coupling of electromagnetic–mechanical–thermal multi-physical fields, and bottlenecks related to high costs and reliability verification. Finally, future research directions are envisioned, including new materials, sensorless control, and active safety integration for autonomous driving. Full article

This study investigates bit error rate (BER)-constrained power allocation for uplink non-orthogonal multiple access (NOMA) systems in which a base station employs one-bit analog-to-digital converters. Although one-bit quantization significantly reduces hardware costs and receiver power consumption, it also introduces severe nonlinear distortions that degrade detection performance. To address this challenge, a pairwise error probability expression is first derived for the one-bit quantized uplink NOMA model, from which an analytical upper bound on the BER is obtained. Based on this characterization, a fairness-driven max–min power allocation strategy is formulated to minimize the BER of the worst-performing user. A closed-form solution for the optimal power allocation is obtained under binary phase-shift keying (BPSK) signaling. Simulation results verify the tightness of the analytical BER bound and demonstrate that the proposed power allocation scheme provides noticeable BER improvements that compensate for the performance degradation caused by one-bit quantization. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

Building energy consumption accounts for a significant portion of total society energy use, and photovoltaic technology is being rapidly deployed across the construction sector. In order to improve the efficiency with which photovoltaic shading devices capture solar energy, a numerical calculation model for the ideal tilt angle of these devices is constructed in this study. This model is based on clear-sky solar radiation calculation algorithms and solar radiation resources across different latitudes. In order to maximize solar radiation collection, an ideal control strategy for photovoltaic shading devices on buildings with varied orientations at different latitudes and in different months is derived through numerical simulations. The findings demonstrate that the building’s orientation has a significant role in determining how well photovoltaic shading systems use solar energy. In winter, the ideal tilt angle for south-facing facades increases by 10° for every 10° increase in latitude. And for every 25° rise in latitude, the ideal tilt angle increases by only around 10° in summer. By applying optimal regulatory strategies, solar radiation consumption efficiency of roughly 65% can be attained, providing a reference basis for boosting power generating efficiency and building energy saving. Full article

The development of electric quarry transport puts a significant strain on local power grids, leading to sharp peaks in consumption and degradation of power quality. Existing methods of peak smoothing, such as generation control, virtual power plants, or intelligent load management, have limited efficiency under the conditions of stochastic and high-power load profiles of industrial charging stations. A new strategy for direct charge and discharge management of a system for integrated battery energy storage (IBES) is based on dynamic iterative adjustment of load boundaries. The mathematical apparatus of the method includes the formalization of an optimization problem with constraints, which is solved using a nonlinear iterative filter with feedback. The key elements are adaptive algorithms that minimize the network power dispersion functionality (i.e., the variance of $P_{grid}\left(t\right)$ over the considered time interval) while respecting the constraints on the state of charge (SOC) and battery power. Numerical simulations and experimental studies demonstrate a 15 to 30% reduction in power dispersion compared to traditional constant power control methods. The results confirm the effectiveness of the proposed approach for optimizing energy consumption and increasing the stability of local power grids of quarry enterprises. Full article

In recent years, the manufacturing industry and power sector have collectively accounted for nearly 60% of global carbon emissions, presenting a formidable obstacle to achieving net-zero targets by 2050. To address the urgent need for industrial decarbonization, this paper proposes a profit-driven framework for low-carbon manufacturing that synergistically integrates green certificates, demand response, distributed generation, and carbon capture, utilization, and storage (CCUS) technologies. A comprehensive optimization model is formulated to enable manufacturers to maximize profits through strategic participation in electricity, carbon, green certificate, and industrial manufacturing product markets simultaneously. By solving this optimization problem, manufacturers can derive optimal production decisions. The framework’s effectiveness is demonstrated through a case study on lithium-ion battery manufacturing, which reveals promising outcomes: meaningful profit growth, substantial carbon emission reductions, and only minimal impacts on production output. Furthermore, the proposed demand response strategy achieves significant reductions in electricity consumption during peak hours, while the integration of distributed generation systems markedly decreases reliance on the main grid. The incorporation of CCUS extends the clean operation periods of thermal power units, generating additional revenue from carbon trading and CO₂ utilization. In summary, the proposed model represents the first unified profit-maximizing optimization framework for low-carbon manufacturing industries, shifting from traditional cost minimization to profitability optimization, addressing gaps in fragmented low-carbon strategies, and providing a replicable blueprint for carbon-neutral operations while enhancing profitability. Full article

This paper develops an optimal dispatch model for an integrated wind–solar hydrogen-to-ammonia system to address the mismatch between renewable-energy fluctuations and chemical production loads. The model incorporates renewable variability, electrolyzer dynamics, hydrogen-storage regulation, and ammonia-synthesis load constraints, and is solved using a multi-time-scale MILP framework. An efficiency-priority power allocation strategy is further introduced to account for performance differences among electrolyzers. Using real wind–solar output data, a 72-h case study compares three operational schemes: the Balanced Scheme, the Steady-State Scheme, and the Following Scheme. The proposed Balanced Scheme reduces renewable curtailment to 2.4%, lowers ammonia load fluctuations relative to the Following Scheme, and decreases electricity consumption per ton of ammonia by 19.4% compared with the Steady-State Scheme. These results demonstrate that the integrated dispatch model and electrolyzer-cluster control strategy enhance system flexibility, energy efficiency, and overall economic performance in renewable-powered ammonia production. Full article

This paper presents a comprehensive framework for real-time energy management in microgrids integrating distributed renewable energy sources and demand response (DR) programs. To address the inherent uncertainties in key operational variables—such as load demand, wind speed, solar irradiance, and electricity market prices—this study employs a probabilistic modeling approach. A two-stage stochastic optimization method, combining mixed-integer linear programming and optimal power flow (OPF), is developed to minimize operational costs while ensuring efficient system operation. Real-time dynamic pricing mechanisms are incorporated to incentivize consumer load shifting and promote energy-efficient consumption patterns. Three microgrid scenarios are analyzed using one year of real historical data: (i) a grid-connected microgrid without DR, (ii) a grid-connected microgrid with 10% and 20% DR-based load shifting, and (iii) an islanded microgrid operating under incentive-based DR contracts. Results demonstrate that incorporating DR strategies significantly reduces both operating costs and reliance on grid imports, especially during peak demand periods. The islanded scenario, while autonomous, incurs higher costs and highlights the challenges of self-sufficiency under uncertainty. Overall, the proposed model illustrates how the integration of real-time pricing with stochastic optimization enhances the flexibility, resilience, and cost-effectiveness of smart microgrid operations, offering actionable insights for the development of future grid-interactive energy systems. Full article

As a key node of the energy internet, the park-level integrated energy system undertakes the dual functions of improving energy supply reliability and promoting low-carbon development in the transformation of the global energy structure. The need to simultaneously meet terminal energy demand and market regulation requirements constrains operational optimization due to factors such as energy price fluctuations. Future research should focus on supply–demand coordination mechanisms and energy efficiency improvement strategies to advance the high-quality development of such systems. To this end, this study constructs a collaborative optimization framework integrating demand response based on a dual-compensation mechanism and dynamic multi-energy pricing and incorporates it into a Stackelberg game-based low-carbon economic dispatch model. By incorporating a dynamic multi-energy pricing mechanism, the model coordinates and optimizes the interests of the upper-level park integrated energy system operator (PIESO) and the lower-level park users. On the supply side, the model couples a two-stage power-to-gas (P2G) device with a stepwise carbon trading mechanism, forming a low-carbon dispatch system enabling source–grid–load coordination. On the demand side, an integrated demand response mechanism with dual compensation is introduced to enhance the coupling intensity of multi-energy flows and the adjustability of price elasticity. The simulation results show that, compared with traditional models, the proposed optimization framework achieves improvements in three dimensions: carbon emissions, economic benefits, and user costs. Specifically, the carbon emission intensity is reduced by 28.04%, the operating income of the PIESO is increased by 29.53%, and the users’ energy consumption cost is decreased by 13.05%, which verifies the effectiveness and superiority of the proposed model. Full article

The study analyzed the energy flow of a second-generation Toyota Mirai FCEV under Real Driving Conditions (RDC) in ECO and Normal driving modes. The results demonstrated significant operational differences between the two modes. The ECO mode reduced the maximum motor torque from 286.5 Nm to 187.6 Nm (−51%) but increased the high-voltage (HV) battery State of Charge swing (ΔSOC = 17.26% vs. 10.59%, +63%). Regenerative energy recovery rose by ~19.8% overall and by 25.7% in urban driving. The ECO mode exhibited higher HV battery cycling (4.03 Wh vs. 3.27 Wh) and slightly higher fuel cell energy use in urban conditions (+8.5%). The average fuel cell power was 36% higher in Normal mode, whereas the HV battery output was 11.4% higher in ECO mode. Hydrogen consumption in Normal mode was two times higher in urban and highway phases and three times higher in rural driving compared to ECO mode. In summary, the ECO mode enhances regenerative energy utilization and reduces total onboard energy consumption, at the expense of peak torque and increased battery cycling. These results provide valuable insights for optimizing energy management strategies in fuel cell electric powertrains under real driving conditions. The study introduces an independent methodology for high-resolution (1 Hz) electric energy-flow monitoring and quantification of energy exchange between the fuel cell, high-voltage battery, and powertrain system under Real Driving Conditions (RDC). Unlike manufacturer-derived data or laboratory simulations, the presented approach enables empirical validation of on-board energy management strategies in production FCEVs. The results reveal distinctive energy-flow patterns in ECO and Normal modes, offering reference data for the optimization of future hybrid control algorithms in hydrogen-powered vehicles. Full article

This study presents an efficient and reproducible framework for estimating wind power density (WPD) across Mexico using a Dense Neural Network (DNN) trained exclusively on ERA5 and ERA5-Land reanalysis data. The model is designed as a computationally efficient surrogate that reproduces the statistical behavior of the ERA5 benchmark while enabling national-scale WPD mapping and short-term projections at minimal computational cost. Meteorological variables—including wind components at 10 m and 100 m, surface temperature, pressure, and terrain elevation—were harmonized on a 0.25° grid for the 1971–2024 period. A chronological dataset split (70-20-10%) was applied to realistically evaluate forecasting capability. The optimized DNN architecture (512-256-128 neurons) achieved high predictive performance (R² ≈ 0.91, RMSE ≈ 6.2 W/m²) and accurately reproduced spatial patterns and seasonal variability, particularly in high-resource regions such as Oaxaca and Baja California. Compared with deeper neural architectures, the proposed model reduced training time by more than 60% and energy consumption by approximately 40%, supporting principles of sustainable computing and Industry 5.0. The resulting WPD fields, delivered in interoperable NetCDF formats, can be directly integrated into decision-support tools for wind-farm planning, smart-grid management, and long-term renewable-energy strategies in data-scarce environments. Full article

The implementation of photoelectric conversion in photoelectric integrated systems requires the design of photodetectors (PDs) with quick response times and low power consumption. In this work, the self-powered photodetector was prepared by antimony selenide (Sb₂Se₃) microwires (MW)/Se microtube (MT) heterojunction by coating Ag nanowires (NW). The incorporation of Ag-NW involves dual enhancement mechanisms. First, the surface plasmon resonance (SPR) effect amplifies the light absorption across UV–vis–NIR spectra, and the conductive networks facilitate the rapid carrier transport. Second, the type-II band alignment between Sb₂Se₃ and Se synergistically separates photogenerated carriers, while the Ag-NW further suppress the recombination through built-in electric field modulation. The optimized device achieves remarkable responsivity of 122 mA W⁻¹ at 368 nm under zero bias, with a response/recovery time of 8/10 ms, outperforming most reported Sb₂Se₃-based detectors. The heterostructure provides an effective strategy for developing self-powered photodetectors with broadband spectral adaptability. The switching ratio, responsivity, and detectivity of the Sb₂Se₃-MW/Se-MT/Ag-NW device increased by 260%, 810%, and 849% at 368 nm over the Sb₂Se₃-MW/Se-MT device, respectively. These results show that the addition of Ag-NW effectively improves the photoelectric performance of the Sb₂Se₃-MW/Se-MT heterojunction, providing new possibilities for the application of self-powered optoelectronic devices. Full article

Organic photovoltaics (OPVs) based on non-fullerene acceptors (NFAs) are rapidly advancing as lightweight, flexible, and low-cost solar technologies, with power conversion efficiencies approaching 20%. To ensure that environmental sustainability progresses symmetrically alongside performance improvements, it is essential to quantify the environmental footprint of these emerging technologies, particularly during early development stages when material and process choices remain adaptable. This study presents a cradle-to-gate life cycle assessment (LCA) of PTB7-Th:ZY-4Cl solar cells, aiming to identify asymmetries in environmental impact distribution and guide eco-efficient optimization strategies. Using laboratory-scale fabrication data, global warming potential (GWP), cumulative energy demand (CED), acidification (AP), eutrophication (EP), and fossil fuel depletion (FFD) were evaluated via the TRACI methodology. Results reveal that electricity consumption in thermomechanical operations (ultrasonic cleaning, spin coating, annealing, and stirring) disproportionately dominates most impact categories, while chemical inputs such as PEDOT:PSS, PTB7-Th:ZY-4Cl precursors, and solvents contribute significantly to fossil fuel depletion. Substituting grid electricity with renewable sources (hydro, wind, PV) markedly reduces GWP, and solvent recovery or replacement with greener alternatives offers further gains. Although extrapolation to a 1 m² pilot-scale module reveals impacts higher than established PV technologies, prospective scenarios with realistic efficiencies (10%) and lifetimes (10–20 years) suggest values of ~150–500 g CO₂-eq/kWh—comparable to fullerene OPVs and approaching perovskite and thin-film benchmarks. These findings underscore the value of early-stage LCA in identifying asymmetrical hotspots, informing material and process optimization, and supporting the sustainable scale-up of next-generation OPVs. Full article