Search Results (20,506)

Polymer-bonded Nd–Fe–B and Sm–Fe–N magnets have excellent magnetic properties, but their corrosion resistance is inferior. Polymer-bonded magnets, the binary alloys Nd–Fe and Sm–Fe, and the metals Fe, Nd, and Sm were investigated in electrolytes with a pH range of 1.8 to 12.8. Potentiodynamic polarisation measurements showed that these materials corrode in acidic (H₂SO₄) and near-neutral (Na₂SO₄ and NaCl) electrolytes. Iron passivates at pH > 9, but Nd and Sm passivate only in strongly alkaline electrolytes (pH > 12). The alloys and magnets combine the characteristics of the individual metals. Scanning electron microscopy with energy-dispersive X-ray spectroscopy characterised the surface layers before and after electrochemical measurements. The speciation and the depth distribution of elements in the surface layers were analysed using X-ray photoelectron spectroscopy. In the H₂SO₄, a non-protective layer was formed. In NaCl, the corrosion products were more abundant, consisting of a mixture of oxides, hydroxides, and chlorides, while in NaOH, an oxide/hydroxide layer was formed. The corrosion product layers formed in the H₂SO₄ and NaCl electrolytes were significantly thicker for the Sm–Fe–N magnet than for the Nd–Fe–B magnet. Understanding the differences and similarities in the electrochemical behaviour of magnets in various electrolytes is essential to overcoming corrosion-related problems. Full article

The integration of artificial intelligence (AI) and machine learning (ML) technologies in energy storage systems has emerged as a transformative approach in addressing the complex challenges of modern energy infrastructure. This comprehensive review examines current state of the art AI applications in energy storage, from battery management systems to grid-scale storage optimization. We analyze various AI techniques, including supervised learning, deep learning, reinforcement learning, and neural networks, and their applications in state estimation, predictive maintenance, energy forecasting, and system optimization. The review synthesizes findings from the recent literature demonstrating quantitative improvements achieved through AI integration: distributed reinforcement learning frameworks reducing grid disruptions by 40% and operational costs by 12.2%, LSTM models achieving state of charge estimations with a mean absolute error of 0.10, multi-objective optimization reducing power losses by up to 22.8% and voltage fluctuations by up to 71%, and real options analysis showing 45–81% cost reductions compared to conventional planning approaches. Despite remarkable progress, challenges remain in terms of data quality, model interpretability, and industrial implementation. This paper provides insights into emerging technologies and future research directions that will shape the evolution of intelligent energy storage systems. Full article

Distributed energy systems have gained increasing popularity due to their plethora of benefits. However, their evaluation in terms of reliability mostly concerns the time frequency domain, and, thus, merits associated with the spatial scale are often overlooked. A recent study highlighted the benefits of distributed production over centralized one by establishing a spatial reliability framework and stress-testing it for decentralized solar photovoltaic (PV) generation. This work extends and verifies this approach to wind energy systems while also highlighting additional challenges for implementation. These are due to the complexities of the non-linear nature of wind-to-power conversion, as well as to wind turbine siting, and turbine model and hub height selection issues, with the last ones strongly depending on local conditions. Leveraging probabilistic modeling techniques, such as Monte Carlo, this study quantifies the aggregated reliability of distributed wind power systems, facilitated through the capacity factor, using Greece as an example. The results underscore the influence of spatial complementarity and technical configuration on generation adequacy, offering a more robust basis for planning and optimizing future wind energy deployments, which is especially relevant in the context of increasing global deployment. Full article

The spatiotemporal uneven distribution of water resources conflicts sharply with human demands, with pumping stations facing efficiency decline due to aging infrastructure and complex hydraulic interactions. This study employs numerical simulation to investigate operational mechanisms in a parallel pump system at the Yanhuanding Yanghuang Cascade Pumping Station. Using ANSYS Fluent 2024 R1 and the SST k-ω turbulence model, we demonstrate that variable-speed control expands the adjustable flow range to 1.17–1.26 m³/s while maintaining system efficiency at 83–84% under head differences of 77.8–79.8 m. Critically, energy losses (δH) at the 90° outlet pipe junction escalate from 3.8% to 18.2% of total energy with increasing flow, while Q-criterion vortex analysis reveals a 63% vortex area reduction at lower speeds. Furthermore, a dual-mode energy dissipation mechanism was identified: at 0.90n₀ speed, turbulent kinetic energy surges by 115% with minimal dissipation change, indicating large-scale vortex dominance, whereas at 0.80n₀, turbulent dissipation rate increases drastically by 39%, signifying a shift to small-scale viscous dissipation. The novelty of this work lies in the first systematic quantification of junction energy losses and the revelation of turbulent energy transformation mechanisms in parallel pump systems. These findings provide a physics-based foundation for optimizing energy efficiency in high-lift cascade pumping stations. Full article

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask spatiotemporal wave fields. The model training strategy integrates both complete and masked samples from pre- and post-sampling. This design encourages the network to learn and amplify subtle distributional differences. Consequently, small variations in convolutional responses become more informative for feature extraction. We considered the theoretical explanations for why this sampling-augmented training enhances sensitivity to minor signals and validated the approach experimentally. For the region 120–140° E and 20–40° N, a four-layer CSCL model using the first five moments as inputs achieved the best prediction performance. Compared to ConvLSTM, the R² for significant wave height improved by 2.2–43.8% and for mean wave period by 3.7–22.3%. A wave-energy case study confirmed the model’s practicality. CSCL may be extended to the prediction of extreme events (e.g., typhoons, tsunamis) and other oceanic variables such as wind, sea-surface pressure, and temperature. Full article

Chemical Looping Combustion (CLC) technology has emerged as a promising approach for carbon capture owing to its CO₂ separation capability, which addresses the pressing challenge of global climate change. Although iron-based oxygen carriers offer economic advantages owing to their abundance and low cost, their limited cyclic stability restricts their industrial deployment. This study focused on optimizing the performance of iron-based oxygen carriers through composite modification with Al₂O₃ and TiO₂. Using Cantera (2.5.0) software and the minimum Gibbs free energy principle, conversion rates and product distributions of Fe₂O₃, Fe₂O₃/Al₂O₃, and Fe₂O₃/TiO₂ were systematically analyzed under varying temperatures (800–950 °C), oxygen carrier-to-fuel molar ratios (O/C = 1–15), and pressures (0.1–1.0 MPa). The optimal conditions were identified as 900 °C, O/C = 8, and 0.1 MPa. After 50 simulation cycles, Fe₂O₃/Al₂O₃ and Fe₂O₃/TiO₂ achieved average total reaction counts of 503 and 543, respectively, substantially exceeding 296 cycles for Fe₂O₃. The results indicated that Al₂O₃ and TiO₂ improved cyclic stability via physical support and structural regulation mechanisms, thereby offering a practical carrier composite modification strategy. This study provides a theoretical basis for the development of high-performance oxygen carriers and supports the industrial application of CLC technology for efficient carbon capture and emission mitigation. Full article

Excessive use of antibiotic growth promoters (AGPs) in broiler rearing has led to severe issues due to antimicrobial resistance and drug residues in meat. This study was conducted to evaluate aqueous Moringa oleifera leaf extract (MOLE) as a natural alternative to antibiotics in broiler chickens. 150 broiler chicks were randomly distributed into five groups: one control, three MOLE-treated groups (60, 90, and 120 mL/L), and one Enrofloxacin-treated group (an antibiotic). The birds were monitored for a 35-day trial period, split further into a starter phase (0–21 days) and a finisher phase (22–35 days). The results were that at the starter phase of their lives, birds treated with MOLE120 experienced better body weight gain and optimal feed conversion ratio (FCR), which showed improved early growth performance. In the finisher phase, the MOLE90 group demonstrated the best FCR and a favorable weight gain, showing better efficiency at later stages. Crude protein digestibility was highest in the MOLE90 group (69.97%), and apparent metabolizable energy also increased in all MOLE-treated groups, especially MOLE120 (2938.9 kcal/kg). Regarding the blood lipid profile, the MOLE90 group had the lowest low-density lipoprotein (LDL) (82.3 mg/dL) and cholesterol (181.7 mg/dL), while MOLE120 achieved the highest high-density lipoprotein (HDL) level (92.6 mg/dL) with significant (p < 0.05) effects across all parameters. Triglycerides were slightly higher in MOLE groups but remained within physiological limits. In conclusion, MOLE supplementation, particularly at 90–120 mL/L, improved performance and blood lipid metabolism in a phase-specific manner. MOLE120 was more effective in early growth, while MOLE90 proved optimal in the finishing stage. This study supports the potential of MOLE as a phytogenic substitute for antibiotics in poultry production. Full article

Bifacial photovoltaic (PV) modules have the advantage of using light reflected off of the ground to contribute to power production. Predicting the energy gain is challenging and requires complex models to do so accurately. Often, module degradation over time is neglected in models for the sake of simplicity or is underestimated. Comparing outdoor and indoor current–voltage (I–V) performance for bifacial modules is more challenging than for monofacial modules, as there are additional variables to consider such as rear albedo non-uniformity, cell mismatch, and their effects on temperature. This challenge is compounded when heterogeneous degradation modes occur, such as polarization-type potential-induced degradation (PID-p). To examine the effects of PID-p on I–V predictions using an empirical data-driven approach, 16 bifacial PERC modules are installed outdoors on racks with different albedo conditions. A subset is exposed to high-voltage biases of −1500 V or +1500 V. Outdoor data are traced at irradiance ranges of 150–250 W/m², 500–600 W/m², and 900–1000 W/m². These curves are corrected using control module temperature, wire resistivity, and module resistance measured indoors. We examine several methods to transform indoor I–V curves to accurately, and more simply than existing methods, approximate outdoor performance for bifacial modules without and with varying levels of PID-p degradation. This way, bifacial performance modeling can be more accessible and informed by fielded, degraded modules. Distributions of percent errors between indoor and outdoor performance parameters and Mean Absolute Percent Errors (MAPEs) are used to assess method quality. Results including low-irradiance data (150–250 W/m²) are discussed but are filtered for quantifying method quality as these data introduce substantial errors. The method with the most optimal tradeoff between low MAPE and analysis simplicity involves measuring the front side of a module indoors at an irradiance equal to plane-of-array irradiance plus the product of module bifaciality and albedo irradiance. This method gives MAPE values of 1–6.5% for non-degraded and 1.6–5.9% for PID-p degraded module performance. Full article

With strategic mineral exploration extending to deep and complex geological settings, traditional methods increasingly struggle to dissect metallogenic systems and locate ore bodies precisely. This synthesis of current progress in muon imaging (a technology leveraging cosmic ray muons’ high penetration) aims to address these exploration challenges. Muon imaging operates by exploiting the energy attenuation of cosmic ray muons when penetrating earth media. It records muon transmission trajectories via high-precision detector arrays and constructs detailed subsurface density distribution images through advanced 3D inversion algorithms, enabling non-invasive detection of deep ore bodies. This review is organized into four thematic sections: (1) technical principles of muon imaging; (2) practical applications and advantages in ore exploration; (3) current challenges in deployment; (4) optimization strategies and future prospects. In practical applications, muon imaging has demonstrated unique advantages: it penetrates thick overburden and high-resistance rock masses to delineate blind ore bodies, with simultaneous gains in exploration efficiency and cost reduction. Optimized data acquisition and processing further allow it to capture dynamic changes in rock mass structure over hours to days, supporting proactive mine safety management. However, challenges remain, including complex muon event analysis, long data acquisition cycles, and limited distinguishability for low-density-contrast formations. It discusses solutions via multi-source geophysical data integration, optimized acquisition strategies, detector performance improvements, and intelligent data processing algorithms to enhance practicality and reliability. Future advancements in muon imaging are expected to drive breakthroughs in ultra-deep ore-forming system exploration, positioning it as a key force in innovating strategic mineral resource exploration technologies. Full article

To address the issue of uncertainty in renewable energy and its impact on the safe and stable operation of power systems, this paper proposes a transient stability constrained optimal power flow (TSCOPF) calculation method that takes into account the uncertainty of wind power and load. First, a non-parametric kernel density estimation method is used to construct the probability density function of wind power, while the load uncertainty model is based on a normal distribution. Second, a TSCOPF model incorporating the critical clearing time (CCT) evaluation metric is constructed, and corresponding probabilistic constraints are established using opportunity constraint theory, thereby establishing a TSCOPF model that accounts for wind power and load uncertainties; then, a semi-invariant probabilistic flow calculation method based on de-randomized Halton sequences is used to convert opportunity constraints into deterministic constraints, and the improved sooty tern optimization algorithm (ISTOA) is employed for solution. Finally, the superiority and effectiveness of the proposed method are validated through simulation analysis of case studies. Full article

Vortex tubes are used in specialized scenarios where conventional refrigeration systems are impractical, such as tool cooling in CNC machines. The internal flow within a vortex tube is highly complex, with numerous factors influencing its energy separation process, and the coefficient of performance for refrigeration is relatively low. To investigate the impact of nozzle type on energy separation performance, vortex tubes with straight-type, converging-type, and converging–diverging-type nozzles were designed. Numerical simulation was conducted to explore their velocity, pressure, and temperature distribution at an inlet pressure of 0.7 MPa and a cold mass fraction of 0.1~0.9. The cooling effect, temperature separation effect, cold outlet mass flow rate, and refrigeration capacity of vortex tubes were assessed. The converging–diverging nozzle increases the gas velocity at the nozzle outlet while it does not significantly enlarge the airflow velocity in the vortex chamber. As the cold mass fraction rises, the cooling performance and cooling capacity of three vortex tubes first increase and then decrease. The maximum cooling effect and cooling capacity of vortex tubes are achieved at cold mass fractions of 0.3 and 0.7, respectively. Under identical conditions, the vortex tube with a converging nozzle achieves the highest cooling effect with a temperature drop of 36.6 K, whereas the vortex tube with converging–diverging nozzles possesses the largest gas flow rate, and the cooling capacity reaches 542.4 W. The vortex tube with straight nozzles exhibits the worst refrigeration performance with a cooling effect of 33.6 K and a cooling capacity of 465.9 W. It is indicated that optimizing the nozzle structure of the vortex tube to reduce flow resistance contributes to enhancing both the gas velocity entering the swirl chamber and the resultant refrigeration performance. Full article

Building geometric parameters are key factors influencing energy-efficient building design. However, the systematic influence of building geometric parameters on energy use intensity (EUI) across varying climate regions and building envelope thermal performance levels remains incompletely elucidated, hindering the quantitative assessment of their energy-saving benefits in diverse regions and operational scenarios. This study employs a zonal sensor-optimized coupled daylighting–thermal simulation to analyze the impact of building geometric parameters and their values on annual total EUI across different climate regions and building envelope thermal performance levels. The interquartile range (IQR), sensitivity analysis (SA), and energy saving rate (ESR) analysis are utilized. The results showed the following: (1) The energy-saving benefits of geometric parameters were the greatest in severe cold (SevC) and temperate regions (TRs), with IQRs ranging from 28.50 to 39.87 kWh/m², followed by hot summer–warm winter (HS-WW), cold (Cld), and hot summer–cold winter (HS-CW) regions. While high-performance building envelopes significantly reduce EUI, the energy-saving benefits associated with geometric parameters remain undiminished. (2) The WWR is the parameter most sensitive to EUI, with SA reaching a maximum of 41.19%, notably exceeding 20% in HS-CW regions, HS-WW regions, and TRs; floor height has the lowest sensitivity, with SA reaching a maximum of 5.65%. (3) In different climate regions, the influence of floor height and building footprint area on the ESR shifts between positive and negative correlations, while the WWR and window sill height consistently exhibit positive correlations with the ESR in all climate regions. This study provides a quantitative decision-making basis for optimizing building geometric parameters in different climate regions to achieve high-performance building shapes during the early stages of architectural design. Full article

This study simulates an open-cavity random distributed Raman amplifier for optimal performance across a 5 THz S-band spectrum (196.2–201.1 THz; 1490.76–1527.99 nm), evaluating its capacity via a 50-channel WDM grid with 100 GHz spacing. The primary Raman pump wavelength was tuned from 1318 to 1344 nm to identify the optimal point. A Fiber Bragg Grating (FBG), placed at the end of a 60 km single-mode fiber and upshifted 88 nm from the pump, enhances efficiency by transferring energy to the amplified signal, minimizing power variation. Results yield < 2 dB gain ripple across channels using raw Raman amplification without flattening filters with minor degradation from residual channels, confirming the DRA design’s viability for high-density S-band optical communication expansion. Full article

The Archimedes spiral hydrokinetic turbine (ASHT), an innovative horizontal-axis design, holds significant potential for harvesting energy from localized ocean and river currents. However, prolonged operation can result in blade erosion, which reduces efficiency and may lead to operational failures. To ensure reliability and prevent damage, it is essential to accurately identify the locations and progression of wear caused by sand particle impacts. Using a CFD–DPM approach, this study systematically investigates the effects of sand concentration and particle size on erosion rates and distribution across nine ASHT configurations, along with the underlying physical mechanisms. The results indicate that erosion rate increases linearly with sand concentration due to higher particle impact frequency. Erosion zones expand from the blade tip edges toward mid-span regions and areas near the hub as concentration increases. Regarding particle size, the erosion rate increases rapidly and almost linearly for diameters below 0.6 mm, but this growth slows for larger particles due to a “momentum–quantity trade-off” effect. Blade angle also exerts a tiered influence on erosion, following the pattern medium angles > small angles > large angles. Medium angles enhance the synergy between normal and tangential impact components, maximizing erosion. Erosion primarily initiates at the blade tips and edges, with the most severe wear concentrated in these high-impact zones. The derived erosion patterns provide valuable guidance for predicting erosion, optimizing ASHT blade design, and developing effective anti-erosion strategies. Full article

This paper proposes a real-time energy management strategy for low-voltage microgrids that combines short-horizon forecasting with a rule-based supervisory controller to coordinate battery energy storage usage and reactive power support provided by flexible alternating current transmission technologies. The central contribution is the forecast-informed, joint orchestration of active charging and reactive power dispatch to regulate voltage and preserve stability under large photovoltaic variability and uncertain electric vehicle demand. The work also introduces a resilience response index that quantifies performance under external disturbances, forecasting delays, and increasing levels of electric-vehicle integration. Validation is carried out through time-domain numerical simulations in MATLAB/Simulink using realistic solar irradiance and electric vehicle charging profiles. The results show that the coordinated strategy reduces voltage deviation events, maintains stable operation across a wide range of scenarios, and enables electric vehicle charging to be supplied predominantly by renewable generation. Sensitivity analysis further indicates that support from flexible alternating current devices becomes particularly decisive at high charging demand and in the presence of forecasting latency, underscoring the practical value of the proposed approach for distribution-level microgrids. Full article