Search Results (18,711)

Clouds are important for Earth’s energy budget and water cycles, and precisely characterizing their vertical structure is essential for understanding their impact. Although passive remote sensing offers broad coverage and high temporal resolution, sensor and algorithmic limitations impede the accurate depiction of cloud vertical profiles. To improve estimates of their key structural parameters, e.g., cloud top height (CTH) and cloud vertical extent (CVE), we propose a multi-source collaborative optimization algorithm. The algorithm synergizes the wide-coverage FY-4A (FengYun-4A) and DSCOVR (Deep Space Climate Observatory) cloud products with high-precision CloudSat vertical profile data and establishes LightGBM-based CTH/CVE optimization models. The models effectively reduce systematic errors in the FY-4A and DSCOVR cloud products, lowering the CTH Mean Absolute Error (MAE) to 1.8 km for multi-layer clouds, an improvement of 4–8 km over the original. The CVE MAEs for single- and multi-layer clouds are ~2.5 km. Some bias remains in complex cases, e.g., multi-layer thin clouds at low altitudes, and error tracing analysis suggests this may be related to cloud layer number misclassification. The proposed algorithm facilitates daytime near-hourly cloud retrievals over China and neighboring regions. Full article

The Ti-6Al-4V alloy is a typical α + β type titanium alloy and is widely used in the manufacture of aero-engine fans, compressor discs and blades. The working life of modern aero-engine components is usually required to reach more than 10⁸ cycles, which makes the infinite life design based on the traditional fatigue limit unsafe. In this study, through symmetrical loading high-cycle fatigue tests on Ti-6Al-4V titanium alloy, a nonlinear cumulative damage life prediction model was established. Further very-high-cycle fatigue tests of titanium alloys were carried out. The variation law of plastic strain energy in the evolution process of very-high-cycle fatigue damage of titanium alloy materials was described by introducing the internal stress parameter. A prediction model for the very-high-cycle fatigue life of titanium alloys was established, and the sensitivity analysis of model parameters was carried out. The results show that the established high-cycle/very-high-cycle fatigue models can fit the test data well. Moreover, based on the optimized model parameters through sensitivity analysis, the average error of the prediction results has decreased from 59% to 38%. The research aims to provide a model or method for predicting the engineering life of titanium alloys in the high-cycle/very-high-cycle range. Full article

This study focuses on the multiphase flow in horizontal wells based on distributed acoustic sensing (DAS) monitoring. Through laboratory experiments and field data analysis, it was found that the micro-seismic differences in flow patterns can be clearly observed in the fiber optic micro-seismic waterfall chart. In the case of slug flow, the DAS acoustic energy decreases when the inclination angle increases. The performance of annular flow is similar to that of bubble flow, with the DAS energy increasing as the inclination angle increases. Overall, the order of DAS acoustic energy from the strongest to weakest is slug flow, followed by annular flow, and then bubble flow. The research shows that fiber optic DAS monitoring signals can effectively identify differences in gas volume, well inclination, and flow pattern, which provides an important technical basis and research foundation for the monitoring and analysis of multiphase flow in horizontal wells. Full article

The promising development of hydrogen and fuel cell technologies has garnered increased attention in recent years, assuming a significant role in industrial applications and the decarbonisation of the shipping industry. Given that the shipping industry generates considerable greenhouse gas emissions, it is crucial and imperative to implement integrated solutions based on clean energy sources, thereby meeting the proposed climate objectives. This study presents the standard hydrogen production, storage, and transport methods and analysis technologies that use hydrogen fuel cells in marine and industrial applications. Technologies based on hydrogen fuel cells and hybrid systems will have an increased perspective of application in industry and maritime transport under the conditions of optimising technological models, developing the hydrogen industrial chain, and updating standards and regulations in the field. However, there are still many shortcomings. The paper’s main contribution is analysing the hydrogen industrial chain, presenting the progress and obstacles associated with the technologies used in industrial and marine applications based on hydrogen energy. Full article

As a pivotal economic province in China, Jiangsu’s efforts in civil building energy conservation are critical to achieving the national “dual carbon” goals. This paper proposes a hybrid model that integrates wavelet transform, support vector regression (SVR), and extreme learning machine (ELM) to predict the civil building energy consumption of Jiangsu Province. Based on data from statistical yearbooks, the historical energy consumption of civil buildings is calculated. Through a grey relational analysis (GRA), the key factors influencing the civil building energy consumption are identified. The wavelet transform technique is then applied to decompose the energy consumption data into a trend component and a fluctuation component. The SVR model predicts the trend component, while the ELM model captures the fluctuation patterns. The final prediction results are generated by combining these two predictions. The results demonstrate that the hybrid model achieves superior performance with a Mean Absolute Percentage Error (MAPE) of merely 1.37%, outperforming both individual prediction methods and alternative hybrid approaches. Furthermore, we develop three prospective scenarios to analyze civil building energy consumption trends from 2023 to 2030. The analysis reveals that the observed patterns align with the Environmental Kuznets Curve (EKC). These findings provide valuable insights for provincial governments in future policy-making and energy planning. Full article

Precise evaluation of risk and reliability is crucial for decision making and predicting the outcome of investment in a photovoltaic power system (PVPS) due to its intermittent source. This paper explores different methodologies for risk evaluation and reliability assessment, which can be categorized into qualitative, quantitative, and hybrid qualitative and quantitative (HQQ) approaches. Qualitative methods include failure mode analysis, graphical analysis, and hazard analysis, while quantitative methods include analytical methods, stochastic methods, Bayes’ theorem, reliability optimization, multi-criteria analysis, and data utilization. HQQ methodology combines table-based and visual analysis methods. Currently, reliability assessment techniques such as mean time between failures (MTBF), system average interruption frequency index (SAIFI), and system average interruption duration index (SAIDI) are commonly used to predict PVPS performance. However, alternative methods such as economical metrics like the levelized cost of energy (LCOE) and net present value (NPV) can also be used. Therefore, a risk and reliability approach should be applied together to improve the accuracy of predicting significant aspects in the photovoltaic industry. Full article

The construction sector presently consumes about 40% of global energy and generates 36% of CO₂ emissions, making facade retrofits a priority for decarbonising buildings. This review clarifies how ventilated facades (VFs), wall assemblies that interpose a ventilated air cavity between outer cladding and the insulated structure, address that challenge. First, the paper categorises VFs by structural configuration, ventilation strategy and functional control into four principal families: double-skin, rainscreen, hybrid/adaptive and active–passive systems, with further extensions such as BIPV, PCM and green-wall integrations that couple energy generation or storage with envelope performance. Heat-transfer analysis shows that the cavity interrupts conductive paths, promotes buoyancy- or wind-driven convection, and curtails radiative exchange. Key design parameters, including cavity depth, vent-area ratio, airflow velocity and surface emissivity, govern this balance, while hybrid ventilation offers the most excellent peak-load mitigation with modest energy input. A synthesis of simulation and field studies indicates that properly detailed VFs reduce envelope cooling loads by 20–55% across diverse climates and cut winter heating demand by 10–20% when vents are seasonally managed or coupled with heat-recovery devices. These thermal benefits translate into steadier interior surface temperatures, lower radiant asymmetry and fewer drafts, thereby expanding the hours occupants remain within comfort bands without mechanical conditioning. Climate-responsive guidance emerges in tropical and arid regions, favouring highly ventilated, low-absorptance cladding; temperate and continental zones gain from adaptive vents, movable insulation or PCM layers; multi-skin adaptive facades promise balanced year-round savings by re-configuring in real time. Overall, the review demonstrates that VFs constitute a versatile, passive-plus platform for low-carbon buildings, simultaneously enhancing energy efficiency, durability and indoor comfort. Future advances in smart controls, bio-based materials and integrated energy-recovery systems are poised to unlock further performance gains and accelerate the sector’s transition to net-zero. Emerging multifunctional materials such as phase-change composites, nanostructured coatings, and perovskite-integrated systems also show promise in enhancing facade adaptability and energy responsiveness. Full article

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article

This paper proposes a harmonic screening-based method for calculating the cogging torque of the axial flux permanent magnet synchronous motor. The magnetic field energy in the air gap is derived from the air gap flux and the magnetomotive force of rotor. The cogging torque is then obtained using the energy-based method. Compared with finite element analysis, the proposed approach is significantly faster while maintaining high accuracy. It is particularly effective for scenarios involving stator staggering, which can facilitate quick calculation of cogging torques of many different staggering angles, offering rapid insights into motor performance during the initial design. The method achieves a similarity accuracy with FEA results and reduces computation time, demonstrating both its efficiency and reliability. Full article

Water Supply and Distribution Networks (WSDNs) offer underexplored potential for energy recovery. While many studies confirm their technical feasibility, few assess the long-term operational compatibility and economic viability of such solutions. This study evaluates the energy recovery potential of the Brussels Capital Region’s WSDN using four years (2019–2022) of operational data. Rather than focusing on available technologies, the analysis examines whether the real behavior of the network supports sustainable energy extraction. The approach includes network topology identification, theoretical power modeling, and detailed flow and pressure analysis. The Brussels system, composed of a Water Supply Network (WSN) and a Water Distribution Network (WDN), reveals strong disparities: the WSN offers localized opportunities for energy recovery, while the WDN presents significant operational constraints that limit economic viability. Our findings suggest that day-ahead electricity markets provide more suitable valorization pathways than flexibility markets. Most importantly, the study highlights the necessity of long-term behavioral analysis to avoid misleading conclusions based on short-term data and to support informed investment decisions in the urban water–energy nexus. Full article

This study conducts a comparative evaluation of Turkiye’s carbon emission profile from both sectoral and global perspectives. Utilizing 2022 data from 76 countries, it applies two widely recognized multi-criteria decision-making (MCDM) methods: MEREC, for determining objective weights of criteria, and PROMETHEE II, for ranking countries based on these criteria. All data used in the analysis were obtained from the World Bank, a globally recognized and credible statistical source. The study evaluates seven criteria, including carbon emissions from the energy, transport, industry, and residential sectors, along with GDP-related indicators. The results indicate that Turkiye’s carbon emissions, particularly from industry, transport, and energy, are substantially higher than the global average. Moreover, countries with higher levels of industrialization generally rank lower in environmental performance, highlighting a direct relationship between industrial activity and increased carbon emissions. According to PROMETHEE II rankings, Turkiye falls into the lower-middle tier among the assessed countries. In light of these findings, the study suggests that Turkiye should implement targeted, sector-specific policy measures to reduce emissions. The research aims to provide policymakers with a structured, data-driven framework that aligns with the country’s broader sustainable development goals. MEREC was selected for its ability to produce unbiased criterion weights, while PROMETHEE II was chosen for its capacity to deliver clear and meaningful comparative rankings, making both methods highly suitable for evaluating environmental performance. This study also offers a broader analysis of how selected countries compare in terms of their carbon emissions. As carbon emissions remain one of the most pressing environmental challenges in the context of global warming and climate change, ranking countries based on emission levels serves both to support scientific inquiry and to increase international awareness. By relying on recent 2022 data, the study offers a timely snapshot of the global carbon emission landscape. Alongside its contribution to public awareness, the findings are expected to support policymakers in developing effective environmental strategies. Ultimately, this research contributes to the academic literature and lays a foundation for more sustainable environmental policy development. Full article

A rather novel approach for delivery of inertia-like grid services through energy storage devices is described and validated by physical experiments and on-site measurements. In this approach, denoted “amplified inertia response”, an actual inertial response from a grid-connected synchronous machine is amplified. This inertia emulation approach is contrasted by what is called synthetic inertia, which uses a frequency-locked loop in order to extract the grid frequency. The synthetic inertia faces the usual input signal filtering challenges if the signal-to-noise ratio is low. The amplified inertia controller avoids the input filtering since it only amplifies the natural inertial response from a synchronous machine. However, rotor angle oscillations lead to filtering requirements of the amplified version as well, but on the output signal of the controller. Experimental comparisons are conducted both on the measurement output from the physical experiments in a microgrid and on analysis based on input from on-site measurements from a 55 MVA hydropower generator connected to the Nordic grid. In the specific cases compared, we observe that the amplified inertia version is the better method for smaller power systems, with large frequency fluctuations. On the other hand, the synthetic inertia method is the better in larger power systems as compared to the amplification of the inertial response from a real production unit. Full article

This study analyzes the feasibility of energy mixes composed of different shares of various types of power generation units, including photovoltaic (PV) and wind farms, hydropower, fossil fuel-based plants, and nuclear power. The analysis uses the concept of Energy Return on Investment (EROI), which is considered the most reliable indicator for comparing different technologies as it measures the energy required rather than monetary costs needed to build and operate each technology. Literature-based EROI values for individual generation technologies were used, along with the minimum EROI thresholds for the entire energy mix that are necessary to sustain developed societies and a high quality of life. The results show that, depending on the assumed minimum EROI value, which ranges from 10 to 30, the maximum share of intermittent renewable energy sources (IRESs), such as PV and wind farms, in the system cannot exceed 90% or 60%, respectively. It is important to emphasize that this EROI-based analysis does not account for power grid stability, which currently can only be maintained by the inertia of large synchronous generators. Therefore, the scenario with a 90% IRES share should be regarded as purely theoretical. Full article

Zearalenone (ZEN), a mycotoxin commonly present in maize-based feed, poses a health risk to dairy cows. While the hepatic effects of ZEN are increasingly studied, little is known about its impact on cows with altered energy metabolism. This study investigated the transcriptional response of liver cells isolated from healthy and ketotic cows to ZEN exposure using a novel in vitro model. Hepatocytes were obtained via biopsy from 12 cows, cultured under standardized conditions, and exposed to ZEN (0–100 µM) for 1, 3, and 6 h. Gene expression analysis focused on targets related to glucose and lipid metabolism. ZEN induced time- and dose-dependent changes in gene expression, with the most prominent effects observed after 1 h. Key metabolic genes were differentially regulated depending on the cow’s metabolic status. Notably, hepatocytes from healthy cows showed a stronger transcriptional response than those from ketotic cows, indicating reduced metabolic adaptability in energy-compromised animals. Significant interaction effects between ZEN dose and metabolic status were observed, especially for genes involved in glycolysis and fatty acid metabolism. This study presents a novel in vitro model and emphasizes the need to consider metabolic health when assessing the risks of mycotoxin exposure in dairy cattle. Full article

This study systematically investigates the effects of trap concentration on self-heating and terahertz (THz) responses in GaN HEMTs using Sentaurus TCAD. Traps, inherently unavoidable in semiconductors, can be strategically introduced to engineer specific energy levels that establish competitive dynamics between the electron momentum relaxation time and the carrier lifetime. A simulation-based exploration of this mechanism provides significant scientific value for enhancing device performance through self-heating mitigation and THz response optimization. An AlGaN/GaN heterojunction HEMT model was established, with trap concentrations ranging from 0 to $5\times {10}^{17} {\mathrm{c}\mathrm{m}}^{-3}$. The analysis reveals that traps significantly enhance channel current (achieving 3× gain at $1\times 10^{17} {\mathrm{c}\mathrm{m}}^{-3}$) via new energy levels that prolong carrier lifetime. However, elevated trap concentrations (>$1\times {10}^{16} {\mathrm{c}\mathrm{m}}^{-3}$) exacerbate self-heating-induced current collapse, reducing the min-to-max current ratio to 0.9158. In THz response characterization, devices exhibit a distinct DC component (${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$) under non-resonant detection ($\mathsf{\omega }\mathsf{\tau }\ll 1$). At a trap concentration of $1\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ peaks at $0.12 \mathrm{V}$ when ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-7.8 \mathrm{V}$. Compared to trap-free devices, a maximum response attenuation of 64.89% occurs at ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-4.9 \mathrm{V}$. Furthermore, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ demonstrates non-monotonic behavior with concentration, showing local maxima at $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ and $7\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, attributed to plasma wave damping and temperature-gradient-induced electric field variations. This research establishes trap engineering guidelines for GaN HEMTs: a concentration of $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ optimally enhances conductivity while minimizing adverse impacts on both self-heating and the THz response, making it particularly suitable for high-sensitivity terahertz detectors. Full article