Search Results (28,369)

This study addresses the challenge of drilling film-cooling holes in the turbine blades of aircraft engines. Titanium alloy TC4 was selected as the experimental material. The laser-drilling process was simulated with ANSYS to determine optimal parameters, which were subsequently applied in machining trials. An impact-drilling method was then used to evaluate how pulse width, pulse energy, and pulse count affect micro-hole entrance and exit diameters, taper, and roundness. Simulations revealed that pulse energy and pulse count predominantly govern entrance and exit diameters, whereas pulse count and pulse width exert a stronger influence on taper. Experiments confirmed that entrance and exit diameters increased as pulse energy rose from 2.0 J to 2.8 J; taper increased as pulse width widened from 0.6 ms to 1.4 ms; and entrance diameter, exit diameter, and taper all grew as pulse count rose from 40 to 60. Pulse width and pulse count also significantly affected hole roundness. Full article

Among the various renewable-powered pathways for green hydrogen production, solar photovoltaic (PV) technology represents a particularly promising option due to its environmental sustainability, widespread availability, and declining costs. However, the inherent intermittency of solar irradiance presents operational challenges for electrolyzers, particularly in terms of stability and efficiency. This study presents a MATLAB-based dynamic model of an off-grid, DC-coupled solar PV-Anion Exchange Membrane (AEM) electrolyzer system, with a specific focus on realistically estimating hydrogen output. The model incorporates thermal energy management strategies, including electrolyte pre-heating during startup, and accounts for performance degradation due to load cycling. The model is designed for a comprehensive analysis of hydrogen production by employing a 10-year time series of irradiance and ambient temperature profiles as inputs. The results are compared with two simplified scenarios: one that does not consider the equipment response time to variable supply and another that assumes a fixed start temperature to evaluate their impact on productivity. Furthermore, to limit the effects of degradation, the algorithm has been modified to allow the non-sequential activation of the stacks, resulting in an improvement of the single stack efficiency over the lifetime and a slight increase in overall hydrogen production. Full article

This study systematically investigates the high-velocity impact response and energy absorption characteristics of carbon fiber-reinforced plastic (CFRP)—aluminum foam (AlF) hybrid composite structures, aiming to address the growing demand for lightweight yet high-performance energy-absorbing materials in aerospace and protective engineering applications. Particular emphasis is placed on elucidating the influence of key geometric and material parameters, including the aspect ratio of the columns and the relative density of the AlF core. Experimental characterization was first performed using a split Hopkinson pressure bar (SHPB) apparatus to evaluate the dynamic compressive behavior of AlF specimens with four different relative densities (i.e., 0.163, 0.245, 0.374, and 0.437). A finite element (FE) model was then developed and rigorously validated against the experimental data, demonstrating excellent agreement in terms of deformation modes and force–displacement responses. Extensive parametric studies based on the validated FE framework revealed that the proposed CFRP-AlF composite structure achieves a balance between specific energy absorption (SEA) and peak crushing force, showing a significant improvement over conventional CFRP or AlF. The confinement effect of CFRP enables AlF to undergo progressive collapse along designated orientations, thereby endowing the CFRP-AlF composite structure with superior impact resistance. These findings provide critical insight for the design of next-generation lightweight protective structures subjected to extreme dynamic loading conditions. Full article

Compression pants, as functional sportswear providing external pressure, are widely used to enhance athletic performance and accelerate recovery. However, systematic investigations into their effectiveness during anaerobic exercise and the impact of different pressure levels on performance and post-exercise recovery remain limited. This randomized crossover controlled trial recruited 20 healthy male university students to compare the effects of four garment conditions: non-compressive pants (NCP), moderate-pressure compression pants (MCP), high-pressure compression pants (HCP), and ultra-high-pressure compression pants (UHCP). Anaerobic performance was assessed through vertical jump, agility tests, and the Wingate anaerobic test, with indicators including time at peak power (TPP), peak power (PP), average power (AP), minimum power (MP), power drop (PD), and total energy produced (TEP). Post-exercise blood lactate concentrations and heart rate responses were also monitored. The results showed that both HCP and UHCP significantly improved vertical jump height (p < 0.01), while MCP outperformed all other conditions in agility performance (p < 0.05). In the Wingate test, MCP achieved a shorter TPP compared to NCP (p < 0.05), with significantly higher AP, lower PD, and greater TEP than all other groups (p < 0.05), whereas HCP showed an advantage only in PP over NCP (p < 0.05). Post-exercise, all compression pant groups recorded significantly higher peak blood lactate (Lamax) levels than NCP (p < 0.05), with MCP showing the fastest lactate clearance rate. Heart rate analysis revealed that HCP and UHCP induced higher maximum heart rates (HR_max) (p < 0.05), while MCP exhibited superior heart rate recovery at 3, 5, and 10 min post-exercise (p< 0.05). These findings suggest that compression pants with different pressure levels yield distinct effects on anaerobic performance and physiological recovery. Moderate-pressure compression pants demonstrated the most balanced and beneficial outcomes across multiple performance and recovery metrics, providing practical implications for the individualized design and application of compression garments in athletic training and rehabilitation. Full article

Concrete cracks and sulfate degradation severely compromise structural durability, highlighting the need for sustainable solutions to enhance longevity and minimize environmental impact. This study assesses the efficacy of bacterial self-healing technology utilizing Bacillus megaterium (BM) and Bacillus sphaericus (BS) in enhancing the resistance of concrete to sulfate attacks and improving its mechanical properties. Bacterial suspensions (1% and 2.5% of cement weight) were mixed with concrete containing silica fume or fly ash (10% of cement weight) and cured in freshwater or sulfate solutions (2%, 5%, and 10% concentrations). Specimens were tested for compressive strength, flexural strength, and microstructure using a Scanning Electron Microscope (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and X-ray diffraction (XRD) at various ages. The results indicate that a 2.5% bacterial content yielded the best performance, with BM surpassing BS, enhancing compressive strength by up to 41.3% and flexural strength by 52.3% in freshwater-cured samples. Although sulfate exposure initially improved early-age strength by 1.97% at 7 days, it led to an 8.5% loss at 120 days. Bacterial inclusion mitigated sulfate damage through microbially induced calcium carbonate precipitation (MICP), sealing cracks, and bolstering durability. Cracked specimens treated with BM recovered up to 93.1% of their original compressive strength, promoting sustainable, sulfate-resistant, self-healing concrete for more resilient infrastructure. Full article

Waste activated sludge (WAS), a byproduct of livestock wastewater treatment, poses significant disposal challenges due to its low biodegradability and potential environmental impact. Anaerobic digestion (AD) offers a sustainable approach for methane recovery and sludge stabilization. This study evaluates the biomethane potential (BMP) of WAS and its co-digestion with swine slurry (SS), water lily (Nymphaea spp.), and lotus (Nelumbo nucifera) shoot biomass to enhance methane yield. Batch BMP assays were conducted at substrate-to-inoculum (S/I) ratios of 1.0 and 0.5, with methane production kinetics analyzed using the modified Gompertz model. Mono-digestion of WAS yielded 259.35–460.88 NmL CH₄/g VS_added, while co-digestion with SS, water lily, and lotus increased yields by 14.89%, 10.97%, and 16.89%, respectively, surpassing 500 NmL CH₄/g VS_added. All co-digestion combinations exhibited synergistic effects (α > 1), enhancing methane production beyond individual substrate contributions. Lower S/I ratios improved methane yields and biodegradability, highlighting the role of inoculum availability. Co-digestion reduced the lag phase limitations of WAS and plant biomass, improving process efficiency. These findings demonstrate that co-digesting WAS with nutrient-rich co-substrates optimizes biogas production, supporting sustainable sludge management and renewable energy recovery in livestock wastewater treatment systems. Full article

Mechanical processes such as mixing, extrusion, and drying are key operations in food engineering, with a significant impact on product quality and process efficiency. The increasing complexity of food materials—due to non-Newtonian properties, multiphase structures, and thermal–mechanical interactions—requires advanced modeling approaches for process analysis and optimization. Computational Fluid Dynamics (CFD) has become a vital tool in this context. This review presents recent progress in the use of CFD for simulating key mechanical operations in food processing. Applications include the analysis of fluid flow, heat and mass transfer, and mechanical stresses, supporting improvements in mixing uniformity, energy efficiency during drying, and optimization of extrusion components (e.g., shaping dies). The potential for integrating CFD with complementary models for system-wide optimization is also discussed, including challenges related to scale-up and product consistency. Current limitations are outlined, and future research directions are proposed. Full article

The increasing prevalence of chronic metabolic diseases poses a significant challenge in the modern world, impacting healthcare systems and individual life expectancy. The World Health Organization (WHO) recommends that older adults (65+ years) engage in 150–300 min of moderate-intensity or 75–150 min of vigorous-intensity physical activity, alongside muscle-strengthening and balance-training exercises at least twice a week. However, nearly one-third of the adult population (31%) is physically inactive, which increases the risk of developing obesity, type 2 diabetes, cardiovascular diseases, hypertension, and psychological issues. Physical activity in the form of aerobic exercise, resistance training, or a combination of both is effective in preventing and managing these metabolic diseases. In this review, we explored the effects of exercise training, especially on respiratory and pulmonary factors, including oxygen consumption, pulmonary ventilation, and blood gas analyses among adults. During exercise, oxygen consumption can increase up to 15-fold (from a resting rate of ~250 mL/min) to meet heightened metabolic demands, enhancing tidal volume and pulmonary efficiency. During exercise, the increased energy demand of skeletal muscle leads to increases in tidal volume and pulmonary function, while blood gases play a key role in maintaining the pH of the blood. In this review, we explored the influence of age, body composition (BMI and obesity), lifestyle factors (smoking and alcohol use), and comorbidities (diabetes, hypertension, neurodegenerative disorders) in the modulation of these physiological responses. We underscored exercise as a potent non-pharmacological intervention for improving cardiopulmonary health and mitigating the progression of metabolic diseases in aging populations. Full article

To address compacted soils with high power consumption and waterlogging risks in rice–rapeseed rotation areas of the Yangtze River, this study designed a ditching machine combining a stepped cutter head and trapezoidal cleaning blade, where the mechanical synergy between components minimizes energy loss during soil-cutting and -throwing processes. We mathematically modeled soil cutting–throwing dynamics and blade traction forces, integrating soil rheological properties to refine parameter interactions. Discrete Element Method (DEM) simulations and single-factor experiments analyzed impacts of the inner/outer blade widths, blade group distance, and blade opening on power consumption. Results indicated that increasing the inner/outer blade widths (200–300 mm) by expanding the direct cutting area significantly reduced the cutter torque by 32% and traction resistance by 48.6% from reduced soil-blockage drag; larger blade group distance (0–300 mm) initially decreased but later increased power consumption due to soil backflow interference, with peak efficiency at 200 mm spacing; the optimal blade opening (586 mm) minimized the soil accumulation-induced power loss, validated by DEM trajectory analysis showing continuous soil flow. Box–Behnken experiments and genetic algorithm optimization determined the optimal parameters: inner blade width: 200 mm; outer blade width: 300 mm; blade group distance: 200 mm; and blade opening: 586 mm, yielding a simulated power consumption of 27.07 kW. Field tests under typical 18.7% soil moisture conditions confirmed a <10% error between simulated and actual power consumption (28.73 kW), with a 17.3 ± 0.5% reduction versus controls. Stability coefficients for the ditch depth, top/bottom widths exceeded 90%, and the backfill rate was 4.5 ± 0.3%, ensuring effective drainage for rapeseed cultivation. This provides practical theoretical and technical support for efficient ditching equipment in rice–rapeseed rotations, enabling resource-saving design for clay loam soils. Full article

This study examines the adverse environmental impacts of solar photovoltaic parks located in established protected areas, aiming to determine the level of landscape fragmentation through the calculation of relevant landscape metrics. For this purpose, a case study was carried out in a Mediterranean Natura 2000 Special Protection Area (SPA), and landscape metrics were calculated using Geographic Information System spatial analysis tools. The analysis of metrics showed that the installation of renewable energy parks within the designated protected area negatively affect landscape fragmentation and the absence of carefully defined and evidence-based mitigation measures. The land cover categories that are significantly affected are those considered critical habitats of bird species that have been designated as SPAs. The results of this study highlight the need to integrate, in the National Renewable Energy Spatial Plans, specific biodiversity objectives, such as conservation objectives and the suspension of the installation of photovoltaic parks in certain areas that are important for conservation of biodiversity, in order to ensure the overall sustainability of renewable energy production. Full article

With the increasing demand for high integration, low cost, and large capacities in satellite systems, integrating the antenna and microwave component into the same system has become appealing to the satellite engineer. The dual C-type gold ribbon, performing as the key electromagnetic signal bridge between the microwave component and the antenna, has a significant impact on the electrical performance of satellite antennas. However, during its manufacturing and operating, the interconnection geometry undergoes deformation due to mounting errors and environmental loads. Consequently, these parasitic geometry parameters can significantly increase energy loss during the signal transmission. To address this issue, this paper has proposed a method for determining the design range of the geometrical parameters of the dual C-type gold ribbon, and applied it to the performance prediction of the microstrip antennas and the parameter optimization of the gold ribbon. In this study, a mechanical response analysis of the antennas in the operating environment has been carried out and the manufacturing disturbance has been considered to calculate the geometry fluctuation range. Then, the significance ranking of the geometry parameters has been determined and the key parameters have been selected. Finally, the chaos feedback adaptive whale optimization algorithm–back propagation neural network has been used as a surrogate model to establish the relationship between the geometry parameters and the antenna electromagnetic performance, and the multi-objective red-billed blue magpie optimization algorithm has been combined with the surrogate model to optimize the configuration parameters. This paper provides theoretical guidance for the interconnection geometry design and the optimization of the integration module of the antennas and microwave components. Full article

AC/DC parallel transmission is a critical approach for large-scale centralized transmission. Existing assessments of power transfer capability in AC/DC corridors rarely incorporate comprehensive security and stability constraints, potentially leading to overestimated results. This paper investigates a grid-forming renewable energy system integrated via AC/DC parallel transmission. First, the transmission section’s power transfer limit under N-1 static security constraints is determined. Subsequently, analytical conditions satisfying synchronization and frequency stability constraints are derived using the equal area criterion and frequency security indices, revealing the impacts of AC/DC power allocation and system parameters on transfer capability. Finally, by integrating static security, synchronization stability, and frequency stability constraints, an operational region for secure AC/DC power dispatch is established. Based on this region, an optimal power allocation scheme maximizing the corridor’s transfer capability is proposed. The theoretical framework and methodology enhance system transfer capacity while ensuring AC/DC parallel transmission security, with case studies validating the theory’s correctness and method’s effectiveness. Full article

Distributed acoustic sensing shows great potential for pipeline monitoring. However, internally deployed and unfixed sensing cables are highly susceptible to disturbances from water flow noise, severely challenging impact source localization. This study proposes a novel two-step method to address this. The first step employs Variational Mode Decomposition (VMD) combined with Short-Time Energy Entropy (STEE) for the adaptive extraction of impact signal from noisy data. STEE is introduced as a stable metric to quantify signal impulsiveness and guides the selection of the relevant intrinsic mode function. The second step utilizes the Pruned Exact Linear Time (PELT) algorithm for accurate signal segmentation, followed by an unsupervised learning method combining Dynamic Time Warping (DTW) and clustering to identify the impact segment and precisely pick the arrival time based on shape similarity, overcoming the limitations of traditional pickers under conditions of complex noise. Field tests on an operational water pipeline validated the method, demonstrating the consistent localization of manual impacts with standard deviations typically between 1.4 m and 2.0 m, proving its efficacy under realistic noisy conditions. This approach offers a reliable framework for pipeline safety assessments under operational conditions. Full article

Atmospheric aerosols significantly impact solar photovoltaic (PV) energy generation through their effects on surface solar radiation. This study quantifies the impact of PM_2.5 pollution on PV power output using observational data from 10 stations across Hebei Province, China (2018–2019). Our analysis reveals that elevated PM_2.5 concentrations substantially attenuate solar irradiance, resulting in PV power losses reaching up to a 48.2% reduction in PV power output during severe pollution episodes. To capture these complex aerosol–radiation–PV interactions, we developed and compared the following six machine learning models: Support Vector Regression, Random Forest, Decision Tree, K-Nearest Neighbors, AdaBoost, and Backpropagation Neural Network. The inclusion of PM_2.5 as a predictor variable systematically enhanced model performance across all algorithms. To further optimize prediction accuracy, we implemented a stacking ensemble framework that integrates multiple base learners through meta-learning. The optimal stacking configuration achieved superior performance (MAE = 0.479 MW, indicating an average prediction error of 479 kilowatts; R² = 0.967, reflecting that 96.7% of the variance in power output is explained by the model), demonstrating robust predictive capability under diverse atmospheric conditions. These findings underscore the importance of aerosol–radiation interactions in PV forecasting and provide crucial insights for grid management in pollution-affected regions. Full article

This study presents a comprehensive analysis of the long-term energy performance and economic viability of offshore wind farms planned for locations within the Polish Exclusive Economic Zone of the Baltic Sea. It focuses on the impact of wind farm layout, aerodynamic wake effects, and rotor blade surface degradation. Using the Jensen wake model, modified Weibull wind speed distributions are computed for various turbine spacing configurations (5D, 8D, and 10D) and wake decay constants $k_w\in \{0.02;0.03;0.05\}$. The results reveal a trade-off between turbine density and individual turbine efficiency: tighter spacing increases the total annual energy production (AEP) but also intensifies wake-induced losses. The study shows that cumulative losses due to wake effects can range from 16.5% to 38%, depending on the scenario considered. This corresponds to capacity factors ranging from 33.4% to 45.2%. Finally, lifetime productivity scenarios over 20 and 25 years are analyzed, and the levelized cost of electricity (LCOE) is calculated to assess the economic implications of design choices. The analysis reveals that, depending on the values of the considered parameters, the LCOE can range from USD 116.3 to 175.7 per MWh produced. The study highlights the importance of early stage optimization in maximizing both the energy yield and cost-efficiency in offshore wind farm developments. Full article