Search Results (4,482)

We propose a complexity-aware extension of the Course–Speed (CS) model that integrates an AIS-derived Traffic Complexity Index (TCI) based on change in speed (ΔV) and course (Δθ) to quantify maneuvering complexity in nonlinear crossing waters. The framework consists of: (i) data preprocessing and gating to ensure navigationally valid AIS samples; (ii) CS index computation using distribution-aware statistics; (iii) TCI estimation from variability in speed and course along intersecting flows; and (iv) an integrated CS–TCI for interpretable mapping and ranking. Using one year of AIS data from a high-density crossing area near the Korean coast, we show that the integrated index reveals crossing hotspots and small-vessel maneuvering burdens that are not captured by spatial regularity metrics alone. The results remain robust across reasonable parameter ranges (e.g., speed filter and σ-based weighting), and they align with operational observations in vessel traffic services (VTS). The proposed CS–TCI offers actionable decision support for port and coastal operations by jointly reflecting traffic smoothness and complexity; it can complement collision-risk screening and efficiency-oriented planning (e.g., energy and emission considerations). The approach is readily transferable to other crossing waterways and can be integrated with real-time monitoring to prioritize control actions in complex marine traffic environments. Full article

Particle histories critically influence product quality in spray drying processes, encompassing statistical data on particle dynamics and behavior inside the chamber, including temperatures, moisture levels, wall impacts, and residence times. This study presents the first systematic parametric assessment of how chamber geometry influences particle histories in spray drying, extending previous work on airflow dynamics. A design of experiments (DOE) methodology combined with cost-efficient CFD simulations was employed to establish quantitative parameter–response relationships. The results reveal two distinct classes of particle responses: (i) residence time, moisture content, and wall temperature, which are primarily governed by chamber aspect ratio and drying air flow rate, and (ii) particle–wall impact behavior, which is dominated by chamber topology. Inlet swirl modulates all particle histories, differentially impacting final product quality and energy efficiency. These findings provide predictive guidelines for chamber design and operation, while the methodology offers a general framework for scale-up analyses and parametric CFD studies of particle-laden multiphase processes. Full article

This study systematically investigates the velocity characteristics and coupling mechanisms of tunnel flow fields under the interactions of natural wind, traffic wind, mechanical ventilation, and structural factors (such as transverse passages and relative positions between vehicles and fans). Using CFD simulations combined with turbulence model analyses, the flow behaviors under different coupling scenarios are explored. The results show that: (1) Under natural wind conditions, transverse passages act as key pressure boundaries, reshaping the longitudinal wind speed distribution into a segmented structure of “disturbance zones (near passages) and stable zones (mid-regions)”, with disturbances near passages showing “amplitude enhancement and range contraction” as natural wind speed increases. (2) The coupling of natural wind and traffic wind (induced by moving vehicles) generates complex turbulent structures; vehicle motion forms typical flow patterns including stagnation zones, high-speed bypass flows, and wake vortices, while natural wind modulates the wake structure through momentum exchange, affecting pollutant dispersion. (3) When natural wind, traffic wind, and mechanical ventilation are coupled, the flow field is dominated by momentum superposition and competition; adjusting fan output can regulate coupling ranges and turbulence intensity, balancing energy efficiency and safety. (4) The relative positions of vehicles and fans significantly affect flow stability: forward positioning leads to synergistic momentum superposition with high stability, while reverse positioning induces strong turbulence, compressing jet effectiveness and increasing energy dissipation. This study reveals the intrinsic laws of tunnel flow field evolution under multi-factor coupling, providing theoretical support for optimizing tunnel ventilation system design and dynamic operation strategies. Full article

Redox flow batteries (RFBs) are an emerging class of large-scale energy storage devices, yet the commercial benchmark—vanadium redox flow batteries (VRFBs)—is highly constrained by a modest open-circuit potential (1.26 V) while posing an expensive and volatile material procurement costs. This review focuses on recent progress in diversifying redox-active species to overcome these limits, highlighting chemistries that increase overall cell voltage, energy density, and efficiency while maintaining long cycle life and safety. The study dwells deeper into manganese-based systems (e.g., Mn/Ti, Mn/V, Mn/S, M/Zn) that leverage Mn’s high positive potential while addressing Mn(III) disproportionation reactions; iron-based hybrids (Fe/Cr, Fe/Zn, Fe/Pb, Fe/V, Fe/S, Fe/Cd) that exploit the low cost, and its abundance, along with membrane and electrolyte strategies to prevent the potential issue involving crossover; cerium-anchored catholytes (Ce/Pb, V/Ce, Eu/Ce, Ce/S, Ce/Zn) that deliver high operational voltage by implementing an acid-base media, along with selective zeolite membranes; and halide systems (Zn–I, Zn–Br, Sn–Br, polysulfide–bromine/iodide) that combine fast redox kinetics and high solubility with advances such as carbon-coated membranes, bromine complexation, and ambipolar electrolytes. Across these various families of RFBs, the review highlights the modifications made to the flow-fields, membranes, and electrodes by utilizing a zero-gap serpentine flow field, sulfonated poly(ether ether ketone) (SPEEK) membranes, carbon-modified and zeolite separators, electrolyte additives to enhance the voltage (VE%), and thereby energy (EE%) efficiency, while reducing the overall system cost. These modifications to the existing RFB technology offer a promising alternative to traditional approaches, paving the way for improved performance and widespread adoption of RFB technology in large-scale grid-based energy storage solutions. Full article

Charging electric vehicles (EVs) and integrating renewable energy sources (RESs) are becoming key aspects of residential energy systems. However, the variability of RES generation, combined with uncontrolled EV charging, poses challenges for reliability, power quality, and supply-demand balancing within communities. The challenges only grow when considering other electrified building loads as well. Accurate forecasting of power demand and renewable generation is essential for efficient and sustainable grid operation, optimal use of RESs, and effective energy trading within communities. Deep learning (DL), including supervised, unsupervised, and reinforcement learning (RL), has emerged as a promising solution for predicting consumer demand, renewable generation, and managing energy flows in residential environments. This paper provides a comprehensive review of the development and application of these methods for forecasting and energy management in residential communities. Evaluation metrics across studies indicate that supervised learning can achieve highly accurate forecasting results, especially when integrated with unsupervised K-means clustering and data decomposition. These methods help uncover patterns and relationships within the data while reducing noise, thereby enhancing prediction accuracy. RL shows significant potential in control applications, particularly for charging strategies. Similarly to how V2G-simulators model individual EV usage and simulate large fleets to generate grid-scale predictions, RL can be applied to various aspects of EV fleet management, including vehicle dispatching, smart scheduling, and charging coordination. Traditional methods are also used across different applications and help utilities with planning. However, these methods have limitations and may not always be completely accurate. Our review suggests that integrating hybrid supervised-unsupervised learning methods with RL can significantly improve the sustainability and resilience of energy systems. This approach can improve demand and generation forecasting while enabling smart charging coordination and scheduling for scalable EV fleets integrated with building electrification measures. Furthermore, the review introduces a unifying conceptual framework that links forecasting, optimization, and policy coupling through hierarchical deep learning layers, enabling scalable coordination of EV charging, renewable generation, and building energy management. Despite methodological advances, real-world deployment of hybrid and deep learning frameworks remains constrained by data-privacy restrictions, interoperability issues, and computational demands, highlighting the need for explainable, privacy-preserving, and standardized modeling approaches. To be effective in practice, these methods require robust data acquisition, optimized forecasting and control models, and integrated consideration of transport, building, and grid domains. Furthermore, deployment must account for data privacy regulations, cybersecurity safeguards, model interpretability, and economic feasibility to ensure resilient, scalable, and socially acceptable solutions. Full article

This study investigates the optimization of crushing and screening efficiency in hammer mill systems through aerodynamic analysis. The research focuses on cucumber vine stalks characterized by high moisture content, elevated cellulose concentration, and pronounced mechanical toughness. Using key operating parameters that significantly influence the gas flow field as the starting point, single-phase gas flow field numerical simulations and characteristic simulations were conducted using the computational fluid dynamics (CFD) software Fluent. A two-way coupling method combining Fluent and the discrete element method (DEM) software EDEM was employed to perform gas–solid coupled numerical simulations and operational characteristic simulations of the pulverizer’s grinding and screening process. This revealed the influence patterns of gas flow disturbances on the grinding and screening process and the mechanism for performance enhancement. Finally, field testing was conducted. Based on experimental results, the optimized operating parameters were determined as follows: rotor speed of 2569 r/min, fan opening of 62.55%, and feed rate of 7.64 kg/min. Under these optimized conditions, the crushing productivity of cucumber vine stalks reached 337 kg/h, with an energy consumption of 5.59 kW·h/t. The deviation between the actual and theoretical values for productivity was less than 6%, while the deviation for energy consumption per ton was less than 3%. These findings provide a theoretical foundation and experimental basis for further research into the mechanism of external airflow disturbance in the crushing and screening process, aiming to enhance crushing efficiency and reduce energy consumption. Full article

Phase-change materials (PCMs) are integral to the thermal energy storage devices used in phase-change storage air-conditioning systems, but their adoption is hindered by slow heat transfer rates and suboptimal energy storage efficiency. In this study, we design and analyze a flat-panel thermal energy storage device based on PCM, using both numerical simulations and experimental testing to evaluate performance under various operating conditions. The simulations, conducted using computational fluid dynamics (CFD) in a steady-state environment with an inlet temperature of 12 °C, demonstrate that the phase-change completion time for cooling storage is 8331 s, while the cooling release process is completed in 3883 s. The fluid distribution within the device is found to be uniform, and the positioning of the inlet and outlet has a minimal effect on performance metrics. However, the lateral stacking configuration of PCM units significantly improves heat transfer efficiency, increasing it by 15% compared to vertical stacking arrangements. Experimental tests confirm that increasing the inlet flow rate accelerates the phase transition process but has a marginal impact on overall energy utilization efficiency. These results provide valuable quantitative insights into optimizing the design of phase-change thermal storage devices, particularly in terms of enhancing heat transfer and overall energy efficiency. Full article

This study introduces Internal Induction Heating (In-IH) as an efficient method for local mold temperature control in thin-walled polypropylene (PP) injection molding. Unlike conventional systems that are slow and energy-intensive, the insert is integrated directly into the induction circuit in the In-IH system, generating eddy currents for rapid and localized heating. Numerical and experimental analyses were performed to examine the effects of insert geometry and heating parameters; it was found that thinner inserts achieved higher surface temperatures—the 0.5 mm insert reached ~550 °C, while the 2.0 mm insert reached only ~80 °C—confirming an inverse relationship between thickness and temperature. Narrower inserts (25 mm) concentrated heat more effectively, whereas wider ones yielded better temperature uniformity. The cooling conditions strongly affected the temperature gradients. Mold-filling experiments demonstrated that In-IH significantly improved the flowability of PP: at 180 °C, the 0.4 mm specimen achieved a flow length of 85.33 mm, compared with 43.66 mm for the 0.2 mm specimen. At 250–300 °C, all samples approached full filling (~100 mm). The simulation and experimental results agreed, with a maximum deviation of 10%, confirming that In-IH provides rapid, energy-efficient, and precise temperature control, thus enhancing melt flow and product quality for thin-walled PP components. Full article

This study extends the application and validation of the Gibbs Free Energy Gradient Method (GMM) for modelling gasification processes, focusing on scaling up from pilot to commercial operations. The model simulates syngas production using poplar wood and olive pomace briquettes under various gasification conditions. Experimental data from a downdraft gasifier were employed to refine the model parameters, achieving accurate predictions of syngas composition with an average error below 8%. Sensitivity analyses highlight the impact of operating conditions, particularly temperature and air flow rates, on the syngas calorific value, hydrogen yield, and energy efficiency. These results emphasize the potential of biomass residues, such as olive pomace, for sustainable energy production in a circular economy context. The findings demonstrate the robustness of the GMM for predicting gasification performance and offer practical guidance for scaling up biomass-to-energy systems while optimizing efficiency and reducing waste. Full article

As a key type of hydro-turbine unit in China, the Kaplan turbine plays a significant role in the national energy portfolio. Nevertheless, being a rotating machine, it unavoidably leaves a tip clearance between the runner blade tip and the chamber wall, which strongly disturbs the internal flow field. Based on experimental data, this study conducts numerical simulations to investigate how the blade-tip clearance affects the runner flow under various guide-vane openings and operating conditions. The results reveal that the leakage flow markedly modifies both the pressure and velocity distributions on the blades; however, its influence is weakest at the on-cam (combined) operating point. Frequency analysis shows that the pressure pulsation in the clearance region is dominated by 1 f_n and 2 f_n components, indicating that leakage vortices and runner rotation are the main sources of blade-tip pressure fluctuations. The findings provide valuable guidance for improving the economic efficiency, safety, and stable operation of Kaplan turbine units. Full article

The rapid proliferation of heterogeneous applications, from latency-critical video delivery to bandwidth-intensive file transfers, poses increasing challenges for modern communication networks. Traditional traffic engineering approaches often fall short in meeting diverse Quality of Experience (QoE) requirements under such conditions. To overcome these limitations, this study proposes a QoE-driven distribution framework for mixed traffic in Software-Defined Networking (SDN) environments. The framework integrates flow categorization, adaptive path selection, and feedback-based optimization to dynamically allocate resources in alignment with application-level QoE metrics. By prioritizing delay-sensitive flows while ensuring efficient handling of high-volume traffic, the approach achieves balanced performance across heterogeneous service demands. In our 15-RSU Mininet tests under service number = 1 and offered demand = 10 ms, JOGAF attains max end-to-end delays of 415.74 ms, close to the 399.64 ms achieved by DOGA, while reducing the number of active hosts from 5 to 3 compared with DOGA. By contrast, HNOGA exhibits delayed growth of up to 7716.16 ms with 2 working hosts, indicating poorer suitability for latency-sensitive flows. These results indicate that JOGAF achieves near-DOGA latency with substantially lower host activation, offering a practical energy-aware alternative for mixed traffic SDN deployments. Beyond generic communication scenarios, the framework also shows strong potential in Intelligent Transportation Systems (ITS), where SDN-enabled vehicular networks require adaptive, user-centric service quality management. This work highlights the necessity of coupling classical traffic engineering concepts with SDN programmability to address the multifaceted challenges of next-generation networking. Moreover, it establishes a foundation for scalable, adaptive data distribution strategies capable of enhancing user experience while maintaining robustness across dynamic traffic environments. Full article

Parabolic trough collectors (PTCs) are effective solar thermal systems, but their performance can be significantly enhanced through surface treatments. This research investigates the enhancement of thermal performance in parabolic trough collectors (PTCs) by experimentally evaluating the results of surface coating on the absorber tube surface. To achieve this objective, a closed-loop PTC system was fabricated to conduct an experimental comparison between a conventional simple copper tube and a black-painted copper tube. The experimental setup was placed in Islamabad, Pakistan, operated under both laminar and turbulent flow conditions to measure key performance metrics, of temperature difference (ΔT) between the inlet and outlet. The results demonstrate a significant performance advantage for the black-painted tube. In laminar flow, the black-painted tube achieved an average ΔT of 3.54 °C, compared to 2.11 °C for the simple copper tube. Similarly, in turbulent flow, the black-painted tube’s ΔT was 2.1 °C, surpassing the simple copper tube’s 1.57 °C. This superior performance is primarily attributed to the black surface’s high solar absorptivity, which more effectively captures and converts solar radiation into thermal energy. The findings highlight the critical role of surface treatment in optimizing PTC efficiency and provide a practical method for improving solar thermal energy systems. Full article

A pilot-scale reactor prototype was designed to produce hydrocarbons through the catalytic pyrolysis process of low-density polyethylene, thereby extending its life cycle and contributing to energy efficiency and sustainability. The reactor consists of a stainless-steel tank encased in a ceramic jacket with refractory cement and clay bricks. The tank, made of 304 stainless steel, ensures mechanical strength and efficient heat transfer to the reactor core. A spiral condenser was incorporated into a water tank to cool the vapors and recover the liquid oil. The insulating materials, ceramic, refractory cement and clay brick, demonstrated a high combined thermal resistance of 0.159 m²·K/W. Simulations and energy flow calculations demonstrated that heat is efficiently directed to the reactor core, reaching 350 °C with only 3000–3800 W, while the outside of the jacket remained close to 32 °C. These results confirm that the proposed design improves thermal efficiency and optimizes energy use for catalytic pyrolysis. The novelty of this design lies in its energy-efficient configuration, which can be replicated in rural regions worldwide due to the accessibility of its construction materials. This reactor was developed based on a smaller-scale model that previously yielded excellent results. Full article

Autonomous navigation in GPS-denied, unstructured environments such as murky waters or complex seabeds remains a formidable challenge for robotic systems, primarily due to sensory degradation and the computational inefficiency of conventional algorithms. Drawing inspiration from the robust navigation strategies of marine species such as the sea turtle’s quantum-assisted magnetoreception, the octopus’s tactile-chemotactic integration, and the jellyfish’s energy-efficient flow sensing this study introduces a novel neuromorphic framework for resilient robotic navigation, fundamentally based on the co-design of marine-inspired sensors and event-based neuromorphic processors. Current systems lack the dynamic, context-aware multisensory fusion observed in these animals, leading to heightened susceptibility to sensor failures and environmental perturbations, as well as high power consumption. This work directly bridges this gap. Our primary contribution is a hybrid sensor fusion model that co-designs advanced sensing replicating the distributed neural processing of cephalopods and the quantum coherence mechanisms of migratory marine fauna with a neuromorphic processing backbone. Enabling real-time, energy-efficient path integration and cognitive mapping without reliance on traditional methods. This proposed framework has the potential to significantly enhance navigational robustness by overcoming the limitations of state-of-the-art solutions. The findings suggest the potential of marine bio-inspired design for advancing autonomous systems in critical applications such as deep-sea exploration, environmental monitoring, and underwater infrastructure inspection. Full article

As a zero-carbon energy carrier, hydrogen is playing an increasingly vital role in the decarbonization of maritime transportation. The hydrogen pressure reducing valve (PRV) is a core component of ship-borne hydrogen storage systems, directly influencing the safety, efficiency, and reliability of hydrogen-powered vessels. However, the marine environment—characterized by persistent vibrations, salt spray corrosion, and temperature fluctuations—poses significant challenges to PRV performance, including material degradation, flow instability, and reduced operational lifespan. This review comprehensively summarizes and analyzes recent advances in the study of high-pressure hydrogen PRVs for marine applications, with a focus on transient flow dynamics, turbulence and compressible flow characteristics, multi-stage throttling strategies, and valve core geometric optimization. Through a systematic review of theoretical modeling, numerical simulations, and experimental studies, we identify key bottlenecks such as multi-physics coupling effects under extreme conditions and the lack of marine-adapted validation frameworks. Finally, we conducted a preliminary discussion on future research directions, covering aspects such as the construction of coupled multi-physics field models, the development of marine environment simulation experimental platforms, the research on new materials resistant to vibration and corrosion, and the establishment of a standardized testing system. This review aims to provide fundamental references and technical development ideas for the research and development of high-performance marine hydrogen pressure reducing valves, with the expectation of facilitating the safe and efficient application and promotion of hydrogen-powered shipping technology worldwide. Full article