Search Results (5,117)

Wind-assisted propulsion using Flettner rotors has gained attention as the shipping sector faces stricter decarbonization regulations. This study compares conventional Fixed Speed Control with Adaptive Speed Control for a 100 m coastal vessel. The proposed Adaptive Speed Control selectively activates the rotor based on relative wind conditions and adjusts rotor speed according to the surge-direction projection of Magnus force. A simulation framework based on the MMG maneuvering model evaluates path-following performance, fuel consumption, and annual performance indicators. Results show that Adaptive Speed Control achieves 18.84% reduction in fuel consumption, corresponding to annual savings of 212.02 tons of fuel, USD 190,823 in OPEX, and 679.76 tons of CO₂ emissions. Selective rotor operation reduces the Fatigue Damage Index by approximately 89%, resulting in 84.48% reduction in annual maintenance costs. Unwanted lateral forces and yaw disturbances are mitigated, improving path-following and maneuvering stability. These findings demonstrate that situationally aware Adaptive Speed Control improves energy efficiency and operational characteristics of Flettner-rotor-assisted propulsion systems while maintaining maneuvering performance, providing practical guidance for wind-assisted ship operation under realistic coastal conditions. Full article

Linear array detector-based infrared push-broom imaging systems are widely employed in remote sensing and security surveillance due to their high spatial resolution, wide swath coverage, and low cost. However, the massive data volume generated during continuous scanning presents substantial storage and transmission challenges. To mitigate this issue, we propose a lossless compression algorithm based on pixel-adaptive prediction and hierarchical decomposition of residuals. The algorithm first performs pixel-wise adaptive noise compensation according to local image characteristics and achieves efficient prediction by exploiting the strong inter-pixel correlation along the scanning direction. Subsequently, hierarchical decomposition is applied to high-energy residual blocks to further eliminate spatial redundancy. Finally, the Golomb–Rice coding parameters are adaptively adjusted based on the neighborhood residual energy, optimizing the overall code length distribution. The experimental results demonstrate that our method significantly outperforms most state-of-the-art approaches in terms of both the compression ratio (CR) and bits per pixel (BPP). Moreover, while maintaining a CR comparable to H.265-Intra, our method achieves a 21-fold reduction in time complexity, confirming its superiority for large-format image compression. Full article

With the continuous and rapid growth of global photovoltaic (PV) installed capacity, the fluctuation, intermittence, and randomness of its output aggravate the inertia loss of traditional power systems, which poses severe challenges to grid voltage stability, frequency regulation, and safe operation of equipment. Stability control of PV power stations has become a necessary aspect of technical support for the construction of new power systems (NPSs). In this paper, a technical analysis framework of stability control of photovoltaic power stations is systematically constructed. First, the core stability problems of photovoltaic systems are sorted out. Then, a technical review of the three control levels, namely the equipment, system, and grid, is carried out. At the same time, the application potential of emerging technologies such as data-driven and AI control, digital twin predictive control, and advanced grid-forming (GFM) inverters is described. Based on existing reviews, this paper proposes an equipment–system–grid hierarchical analysis framework and explicitly integrates emerging technologies with classical methods. This framework provides references for the selection, engineering deployment, and future research directions of stability control technologies for photovoltaic power plants, while also offering technical support for the safe and efficient operation of high-penetration renewable energy power grids. Full article

This study presents a straightforward strategy for producing novel, effective and inexpensive functional non-noble metal-supported carbon materials made from abundant natural biomass. These materials offer a cost-effective alternative to noble metals for the oxidation of hydrazine (HzOR) and demonstrate the potential for widespread adoption of green, energy-saving hydrazine-based technologies in energy applications. Highly efficient and cost-effective iron (Fe) and manganese–iron (MnFe)-supported nitrogen-doped carbon (N–C) materials were developed using hydrothermal synthesis. Meanwhile, the N–C material was obtained from biomass—birch-wood chips—using hydrothermal carbonisation (HTC), followed by activation and nitrogen doping of the resulting hydrochar. The morphology, structure, and composition of the MnFe, MnFe/N–C, and Fe/N–C catalysts were determined using scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersive X-ray spectroscopy (EDS). The activity of the catalysts for HzOR in an alkaline medium was evaluated using cyclic voltammetry (CV). Depositing MnFe particles onto N–C was shown to significantly enhance electrocatalytic activity for HzOR compared to the Fe/N–C catalyst and especially to the MnFe particles catalyst in terms of highly developed porous structure, which offers the largest surface area, lowest onset potential, and highest current density response, resulting in the strongest catalytic activity. These results suggest that the MnFe/N–C catalyst could be a highly promising anode material for HzOR in direct hydrazine fuel cells (DHFCs). Full article

The transition toward sustainable energy systems necessitates innovative solutions that reduce greenhouse gas emissions while improving fuel efficiency in existing combustion technologies. Hydrogen has emerged as a promising clean energy carrier; however, its widespread deployment is limited by challenges associated with large-scale transportation and storage. This study investigates a practical alternative in which hydrogen-rich syngas produced via steam methane reforming (SMR) is directly integrated into the diesel engine intake, thereby eliminating the need for fuel transport, storage, and separation while supporting a more sustainable fuel pathway. A validated computational fluid dynamics (CFD) model was developed to examine the effects of varying SMR gas mixture ratios (0–20%) on engine combustion, performance, and emissions. The findings reveal that increasing the SMR fraction enhances in-cylinder pressure by up to 15.7%, heat release rate by 100%, and engine power output by 102.5% compared to conventional diesel operation. Additionally, under SMR20 conditions, CO₂ emissions are reduced by approximately 12%, demonstrating the potential contribution of this approach to decarbonization and climate mitigation efforts. However, the rise in in-cylinder temperatures was found to increase NO_x formation, indicating the necessity for complementary emission control strategies. Overall, the results suggest that direct SMR syngas integration offers a promising pathway to improve the environmental and performance characteristics of conventional diesel engines while supporting cleaner energy transitions. Full article

Petroleum coke quality strongly influences refinery economics and downstream energy use, yet real-time control is constrained by slow quality assays and a 24–48 h lag in laboratory results. This study introduces a physics-informed combinatorial digital twin for value-optimized coking, aimed at improving energy efficiency and environmental performance through adaptive quality forecasting. The approach builds a modular library of 32 candidate equations grouped into eight quality parameters and links them via cross-parameter dependencies. A two-level optimization scheme is applied: a genetic algorithm selects the best model combination, while a secondary loop tunes parameters under a multi-objective fitness function balancing accuracy, interpretability, and computational cost. Validation on five clustered operating regimes (industrial patterns augmented with noise-perturbed synthetic data) shows that optimal model ensembles outperform single best models, achieving typical cluster errors of ~7–13% NMAE. The developed digital twin framework enables accurate prediction of coke quality parameters that are critical for its energy applications, such as volatile matter and sulfur content, which serve as direct proxies for estimating the net calorific value and environmental footprint of coke as a fuel. Full article

We present a rapid, energy-efficient, and ecofriendly route for the synthesis of alkaline earth titanate perovskites—CaTiO₃, SrTiO₃, and BaTiO₃—using an affordable, commercially available CO₂ laser engraver, commonly found in makerspaces and small-scale workshops. The method involves direct laser irradiation of compacted pellets composed of low-cost, abundant, and non-toxic precursors: TiO₂ and alkaline earth carbonates (CaCO₃, SrCO₃, BaCO₃). CaTiO₃ and BaTiO₃ were synthesized with phase purities exceeding 97%, eliminating the need for conventional high-temperature furnaces or prolonged thermal treatments. X-ray diffraction (XRD) coupled with Rietveld refinement confirmed the formation of orthorhombic CaTiO₃ (Pbnm), cubic SrTiO₃ (Pm3⁻m), and tetragonal BaTiO₃ (P4mm). Raman spectroscopy independently corroborated the perovskite structures, revealing vibrational fingerprints consistent with the expected crystal symmetries and Ti–O bonding environments. All samples contained only small amounts of unreacted anatase TiO₂, while BaTiO₃ exhibited a partially amorphous fraction, attributed to the sluggish crystallization kinetics of the Ba–Ti system and the rapid quenching inherent to laser processing. Transmission electron microscopy (TEM) revealed nanoparticles with average sizes of 50–150 nm, indicative of localized melting followed by ultrafast solidification. This solvent-free, low-energy, and highly accessible approach, enabled by widely available desktop laser systems, demonstrates exceptional simplicity, scalability, and sustainability. It offers a compelling alternative to conventional ceramic processing, with broad potential for the fabrication of functional oxides in applications ranging from electronics to photocatalysis. Full article

This work investigates methods for predicting low-cycle fatigue life by employing new energy-based fatigue damage measures. The primary goal of this research is to evaluate whether fatigue life can be predicted based on an energy accumulation graph, proposed as a generalization of the isodamage lines concept. The efficiency of fatigue life predictions using this approach, derived from the empirical linear Palmgren–Miner hypothesis, is compared against the physically grounded Unified Mechanics Theory thermodynamic approach, which allows for general understanding of material degradation, in contrast to empirical approaches. The study also accounts for the influence of anisotropy resulting from the sheet rolling process on the fatigue response of S420M steel. Samples were tested in orientations both parallel to the rolling direction and perpendicular to the sheet surface. Microstructural analysis revealed a visible banded structure in the perpendicular samples, which is a consequence of anisotropy. The fatigue life of samples taken perpendicular to the sheet surface was lower than that of parallel samples. Verification of the linear Palmgren–Miner damage summation hypothesis, using both the classical fatigue chart and the cumulative energy chart, resulted in calculated fatigue life consistently higher than the experimental fatigue life in all cases. The reduction in fatigue life ranged from 40% (for total strain amplitude equal to 1.0%) to almost 290% for a strain amplitude of 0.25%. A comparative analysis of the unit loop energy shows that at all tested levels of strain amplitude, the unit loop energy of parallel samples is higher than that of samples perpendicular to the surface. Full article

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

Decarbonization of compression-ignition engines requires evaluation of carbon-free and low-carbon fuel alternatives. Ammonia (${NH}_3$) offers zero direct carbon emissions but faces combustion challenges including low flame speed (7 $\mathrm{c}\mathrm{m}/\mathrm{s}$) and high auto-ignition temperature (657 °$\mathrm{C}$). Methanol provides improved reactivity and bound oxygen content that can enhance ignition characteristics. This computational study investigates diesel–ammonia–methanol ternary fuel blends using validated three-dimensional CFD simulations (ANSYS Forte 2023 R2; ANSYS, Inc., Canonsburg, PA, USA) with merged chemical kinetic mechanisms (247 species, 2431 reactions). The model was validated against experimental in-cylinder pressure data with deviations below 5% on a single-cylinder diesel engine (510 cm³, 17.5:1 compression ratio, 1500 rpm). Ammonia energy ratios were systematically varied (10–50%) with methanol substitution levels (0–90%). Fuel preheating at 530 K was employed for high-alcohol compositions exhibiting ignition failure at standard temperature. Results demonstrate that peak cylinder pressures of 130–145 bar are achievable at 10–30% ammonia with M30K–M60K configurations, comparable to baseline diesel (140 bar). Indicated thermal efficiency reaches 38–42% at 30% ammonia-representing 5–8 percentage point improvements over diesel baseline (31%)-but declines to 30–32% at 50% ammonia due to fundamental combustion limitations. ${CO}_2$ reductions scale approximately linearly with ammonia content: 35–55% at 30% ammonia and 75–78% at 50% ammonia. ${NO}_X$ emissions demonstrate 30–60% reductions at efficiency-optimal configurations. Multi-objective optimization analysis identifies the A30M60K configuration (30% ammonia, 60% methanol, 530 K preheating) as optimal, achieving 42% thermal efficiency, 58% ${CO}_2$ reduction, 51% ${NO}_X$ reduction, and 11% power enhancement versus diesel. This configuration occupies the Pareto frontier “knee point” with cross-scenario robustness. Full article

As shallow coal resources become increasingly depleted, coal mining is extending to greater depths, making mine thermal hazards an increasingly prominent issue. This paper proposes a novel system for synergistic geothermal energy extraction from deep coal mine aquifers and coal seam cooling, aimed at achieving integrated geothermal exploitation and mine thermal hazard control. Based on a high-temperature mine in the Yuanyanghu Mining Area of Ningxia, a dual-stage, single-branch three-dimensional numerical model was established to simulate the effects of water injection pressure, water injection temperature, and level spacing on the system’s cooling performance and geothermal energy extraction efficiency. The results indicate that increasing injection pressure enhances early-stage geothermal energy extraction capacity and coal seam cooling rate, but the heat extraction power declines over long-term operation as the produced water temperature approaches the injection temperature. Lowering injection temperature significantly improves water–rock heat exchange efficiency, accelerates coal seam cooling, and increases geothermal energy extraction. Increasing level spacing helps improve geothermal energy extraction power but weakens the direct cooling effect on the coal seam. Considering the influence patterns of each parameter, the optimal combination was determined as water injection pressure of 10 MPa, water injection temperature of 10 °C, and level spacing of 80 m, which delivers the best overall performance by enabling rapid coal seam cooling and sustained geothermal energy extraction, with a cumulative geothermal output reaching 129.45 MW after 10 years of operation. This study provides a theoretical basis and technical reference for the integrated management of thermal hazards and geothermal resource development in deep coal mines. Full article

This work presents the design, modeling, and experimental validation of an innovative, highly autonomous, and economically viable photovoltaic solar cooker, integrating a robust battery storage system. The system combines 1200 Wp photovoltaic panels, a control block with DC/DC power converters and digital control for intelligent energy management, and a thermally insulated heating plate equipped with two resistors. The objective of the system is to reduce dependence on conventional fuels while overcoming the limitations of existing solar cookers, particularly insufficient cooking temperatures, the need for continuous solar orientation, and significant thermal losses. The optimization of thermal insulation using a ceramic fiber and glass wool configuration significantly reduces heat losses and increases the thermal efficiency to 64%, nearly double that of the non-insulated case (34%). This improvement enables cooking temperatures of 100–122 °C, heating element surface temperatures of 185–464 °C, and fast cooking times ranging from 20 to 58 min, depending on the prepared dish. Thermal modeling takes into account sheet metal, strengths, and food. The experimental results show excellent agreement between simulation and measurements (deviation < 5%), and high converter efficiencies (84–97%). The integration of the batteries guarantees an autonomy of 6 to 12 days and a very low depth of discharge (1–3%), allowing continuous cooking even without direct solar radiation. Crucially, the techno-economic analysis confirmed the system’s strong market competitiveness. Despite an Initial Investment Cost (CAPEX) of USD 1141.2, the high performance and low operational expenditure lead to a highly favorable Return on Investment (ROI) of only 4.31 years. Compared to existing conventional and solar cookers, the developed system offers superior energy efficiency and optimized cooking times, and demonstrates rapid profitability. This makes it a sustainable, reliable, and energy-efficient home solution, representing a major technological leap for domestic cooking in rural areas. Full article

Hydraulic manipulators are vital for heavy-duty applications such as rescue robotics due to their high power density, yet these scenarios increasingly demand safe and compliant physical interaction. Impedance control is a key enabling technology for such capabilities. However, a significant challenge arises when implementing impedance control on Independent Metering Systems (IMS), which are widely adopted for their energy efficiency. The inherent multi-mode operation of IMS relies on discrete switching logic. Crucially, when mode switching occurs during physical interaction with the environment, the unpredictable external forces can trigger frequent and abrupt switching between operating modes (e.g., resistive and overrunning), leading to severe chattering. This phenomenon not only undermines the smooth interaction that impedance control aims to achieve but also jeopardizes overall system stability. To address this critical issue, this paper proposes a seamless control framework based on a Takagi–Sugeno (T-S) fuzzy model. Two premise variables based on the physical characteristics of the system are innovatively designed to make the rule division highly consistent with the dynamic nature of the system. Asymmetric membership functions are introduced to handle direction-dependent switching, with orthogonal functions ensuring logical exclusivity between extension and retraction, and smooth complementary functions enabling seamless transitions between resistance and overrunning modes. Experimental validation on a small hydraulic manipulator validates the effectiveness of the proposed method. The controller eliminates switching-induced instability and smooths velocity transitions, even under dynamic external force disturbances. This work provides a crucial solution for high-performance, stable hydraulic interaction control, paving the way for the application of hydraulic robots in complex and dynamic environments. Full article

A treatment process was developed for effluents from direct physical lithium-ion battery (LIB) recycling with a focus on the removal of organic contaminants. The high chemical oxygen demand to biological oxygen demand ratio (COD/BOD₅) of 3.9–4.6 indicates that biological treatment is not feasible. Therefore, three advanced oxidation processes were evaluated: UV/H₂O₂ oxidation, the Fenton process and electrochemical oxidation. Two target scenarios were considered, namely compliance with the limit for discharge into the sewer system (COD = 800 mg/L) and compliance with the stricter limit for direct discharge into surface waters (COD = 200 mg/L). Under the investigated conditions, UV/H₂O₂ oxidation and the Fenton process did not meet the required discharge limits and exhibited high chemical consumption. In contrast, electrochemical oxidation achieved both discharge criteria with a lower energy demand, requiring 32.8 kWh/kgCOD_removed for sewer discharge and 95.3 kWh/kgCOD_removed for direct discharge. An economic assessment further identified electrochemical oxidation as the most cost-effective option, with treatment costs of EUR 6.63/m³, compared to EUR 17.31/m³ for UV/H₂O₂ oxidation and EUR 28.66/m³ for the Fenton process. Overall, electrochemical oxidation proved to be the most efficient and environmentally favorable technology for treating wastewater from LIB recycling, enabling compliance with strict discharge regulations while minimizing the chemical and energy demand. Full article

Water scarcity constitutes a major global challenge. Biomimetic water collection materials, which mimic the efficient water capture and transport mechanisms, offer a crucial approach to addressing the water crisis. This review summarizes the research progress on biomimetic water collection materials, focusing on biological prototypes, operational mechanisms, and core aspects of biomimetic design. Typical water-collecting biological surfaces in nature exhibit distinctive structure–function synergy: spider silk achieves directional droplet transport via periodic spindle-knot structures, utilizing Laplace pressure difference and surface energy gradient; the desert beetle’s back features hydrophilic microstructures and a hydrophobic waxy coating, forming a fog-water collection system based on heterogeneous wettability; cactus spines enhance droplet transport efficiency through the synergy of gradient grooves and barbs; and shorebird beaks enable rapid water convergence via liquid bridge effects. These biological prototypes provide vital inspiration for the design of biomimetic water collection materials. Drawing on biological mechanisms, researchers have developed diverse biomimetic water collection materials. This review offers a theoretical reference for their structural design and performance enhancement, highlighting bio-inspiration’s core value in high-efficiency water collection material development. Additionally, this paper discusses challenges and opportunities of these materials, providing insights for advancing the engineering application of next-generation high-efficiency biomimetic water collection materials. Full article