Search Results (9,297)

Photosynthetic biogas upgrading (PBU) using microalgae is a promising biological approach for converting raw biogas into biomethane while recovering nutrients and fixing part of the biogenic CO₂ into algal biomass. Unlike conventional physicochemical technologies, which mainly separate CO₂ from CH₄, PBU can combine gas upgrading with wastewater or digestate treatment, nutrient recycling, and biomass production. This review assesses the current state of PBU technology, with particular emphasis on high-rate algal ponds, absorption columns, and closed photobioreactors. It examines the main operating parameters that control gas–liquid mass transfer, carbonate buffering, and photosynthetic activity, including the liquid-to-gas ratio, pH, alkalinity, temperature, light regime, light intensity, and gas retention time. Special attention is given to the combined effects of the L/G ratio, pH, and alkalinity, as these parameters strongly influence CO₂ absorption, CH₄ enrichment, and O₂ contamination of the upgraded gas. The use of wastewater or anaerobic digestate instead of synthetic growth media is identified as an important sustainability advantage, particularly at wastewater treatment plants with existing anaerobic digestion and nutrient-rich side streams. However, digestate use may also create operational challenges related to turbidity, ammonium inhibition, solids, and variable composition. Available studies indicate that PBU may reduce operating costs and greenhouse gas emissions under favorable conditions while creating additional value from algal biomass. Nevertheless, wider deployment is still limited by high land requirements, seasonal variability, O₂ contamination, biomass harvesting, and limited evidence from large-scale systems. Future development should therefore focus on improved oxygen management, more efficient reactor designs, nanoparticle-assisted enhancement of photosynthetic activity, better integration with wastewater treatment, and AI-supported monitoring and control to improve process stability and support scale-up. Full article

To clarify the spreading law of river crab pellet feed in a centrifugal spreading mechanism and provide a physical basis for the path planning of automatic feeding boats, this study took 4.0 mm sinking extruded river crab feed as the research object. A systematic research method combining physical experiments and Discrete Element Method (DEM) simulation was established. Physical experiments were conducted to calibrate the intrinsic parameters (density, Poisson’s ratio, elastic modulus) and contact parameters (friction coefficients and restitution coefficients between feed and 304 stainless steel/ABS plastic, as well as between feed particles) of the pellet feed. On this basis, a DEM simulation model of a vibration blanking-dual disc centrifugal spreading mechanism was constructed using the multi-sphere aggregation method and the Hertz-Mindlin (no-slip) contact model. A Central Composite Design (CCD) response surface experiment was employed to investigate the spreading law, with boat speed (0.5–1.5 m/s) and spreading disc rotation speed (800–1000 rpm) as independent variables, and unilateral spreading width (W), track superposition uniformity (ω), and transverse coefficient of variation ($Cv$) as response indicators to characterize spreading range and particle distribution. The results showed that the spreading disc rotation speed had an extremely significant effect (p < 0.0001) on all three response indicators, while boat speed had no significant effect. The feed exhibited a characteristic double fan-shaped superposition distribution pattern. Through multi-objective optimization, the optimal operational parameters were determined as a boat speed of 1.0 m/s and a spreading disc rotation speed of 879 rpm, yielding a unilateral spreading width of 2.9 m, a track superposition uniformity of 88.31%, and a transverse coefficient of variation of 8.33%. This study establishes a quantitative method for analyzing feed spreading characteristics and clarifies the spreading range and particle distribution law, providing a reliable physical basis for full-coverage path planning of crab pond feeding boats. Full article

A fuel cell (FC)/supercapacitor (SC) hybrid powertrain is proposed for rubber tire gantry (RTG) cranes, aiming to address their characteristics of high peak/low average power demand and huge potential energy recovery. Unlike conventional design methods that neglect the coupling effects of energy management strategies (EMSs), this paper adopts a joint optimization (JO) for the powertrain parameters’ design. Parameters are preliminarily sized based on routine container handling tasks, then refined via a dynamic programming (DP)-based EMS for secondary optimization to minimize the total crane operation costs that cover hydrogen consumption as well as FC degradation. Iterations of the optimization process continue until targets are met. The results indicate that the JO framework achieves dual energy-economic goals, exhibiting a 57.33% enhancement in fuel economy compared to diesel-powered cranes through port validation while concurrently decreasing the SC’s capacity redundancy by 12.7%. These findings aid FC/SC RTG crane configuration design in ports. Additionally, the theoretical optimal operation cost obtained by the DP-based EMS can be used as a benchmark for evaluating other EMSs. Full article

In light of the rapid development of the electric vehicle market, permanent magnet synchronous motors (PMSMs) are becoming essential components of propulsion systems. This is due to their high efficiency, remarkable power density, and ability to deliver high torque over a wide speed range. However, the optimal control of these motors under dynamic conditions remains a major challenge due to system nonlinearities, parameter variations, and external disturbances. Conventional strategies such as field-oriented control (FOC), direct torque control (DTC), and fuzzy logic control (FLC) show variable performance in terms of current quality, robustness, and energy efficiency. To overcome these limitations, this study proposes an intelligent control strategy based on artificial neural networks (ANNs), which ensures efficient operation and high control performance under various operating conditions. This approach leverages the learning capabilities of deep neural networks to improve control accuracy, system stability, and overall energy performance. The results obtained show a significant reduction in the current’s total harmonic distortion (THD) as well as an improvement in the stator’s current quality and the electromagnetic torque’s dynamic behavior compared to conventional methods. This improvement reduces overall losses in the electric drive system, thereby contributing to increased vehicle energy efficiency. As a result, the electric vehicle’s range is extended, and the dynamic performance of the PMSM is optimized. These results confirm the potential of artificial intelligence techniques for developing intelligent, robust, and adaptive control systems designed for modern electric propulsion applications. Full article

Smart cities increasingly rely on distributed sensing and edge intelligence to support urban planning, mobility management, environmental monitoring, and critical infrastructure operation. However, large-scale urban Internet-of-Things deployments are constrained by heterogeneous device capabilities, limited energy availability, variable communication conditions, and data-governance requirements. Federated learning offers a data-locality-preserving alternative to centralized model training, but conventional federated learning strategies often assume full, random, or fixed client participation, which can lead to unnecessary energy consumption, communication overhead, or client starvation in resource-constrained urban environments. This paper proposes an Energy- and Communication-Aware Federated Learning strategy, termed ECA-FL, for smart city sensing systems. The main novelty of the work lies in the joint use of residual device energy and communication conditions to guide adaptive client participation and local training effort, providing a tunable resource–performance trade-off rather than an accuracy-maximizing strategy alone. The framework is evaluated through a controlled simulation-based study using a synthetic multi-class urban sensing proxy task distributed across 100 federated clients under strongly non-IID conditions. Compared with full-participation FedAvg, ECA-FL reduces cumulative energy consumption by 82.9% and communication overhead by 64.7%, while maintaining a final accuracy of 0.8124 compared with 0.8319 for FedAvg-full. Compared with rigid fixed-participation strategies, ECA-FL avoids severe learning degradation by adapting participation dynamically instead of excluding clients according to a static rule. A sensitivity analysis further shows that the trade-off parameter controls the balance between learning performance and resource conservation, allowing the framework to be adjusted according to different deployment priorities. The results support the hypothesis that adaptive energy- and communication-aware participation can substantially reduce operational cost while preserving acceptable learning performance within the adopted simulation setting. The study provides practical design insights for sustainable, communication-conscious, and data-locality-preserving federated learning in smart city sensing infrastructures. Full article

When photovoltaic (PV) power plants are connected to weak alternating current (AC) grids, the interaction between the plant and grid may induce wideband oscillation, posing a serious threat to the stability of grid-connected PV systems. To address this problem, this paper proposes an oscillation suppression method based on adaptive supplementary damping control of a Static Var Generator (SVG). First, a sequence impedance model of a PV power plant integrated with an SVG is established, and the Nyquist criterion is employed to analyze the mechanism underlying wideband oscillations. Then, a supplementary damping controller implemented in the SVG is designed to reshape the impedance characteristics of the PV power plant and enhance system damping. Furthermore, a Variational Mode Decomposition–Prony modal identification algorithm is introduced to extract oscillation mode information in real time. Based on the identified oscillation frequency, the parameters of the damping controller are adaptively adjusted, thereby improving the suppression capability for wideband oscillations with varying frequencies. Finally, a grid-connected PV power plant model with an SVG is developed, and the performance of the proposed adaptive suppression strategy is compared with that of conventional supplementary damping control. The results demonstrate that the proposed strategy provides stronger robustness and adaptability, effectively suppresses wideband oscillations under different operating conditions, and improves the stability of grid-connected PV systems. Full article

Soluble-boron-free designs for water-cooled small modular reactors offer advantages such as reduced corrosion and simplified systems. However, the absence of soluble boron necessitates higher total control rod worth for reactivity control and the shutdown margin, leading to excessive individual control rod worth, which can lead to severe power excursions during a rod ejection accident (REA), potentially threatening the fuel integrity and core-cooling capability. The analysis of a hypothetical REA for an equilibrium core design showed that the fuel rod cladding failed due to the high reactivity worth of the ejected control rod. To enlarge the safety margins of this design under accidental conditions, two strategies were adopted: implementing a hybrid control rod configuration to decrease the local reactivity worth within single fuel assembly and re-arranging the refuelling loading pattern to prevent fresh fuel clustering. Using an in-house CoreOptimizer tool, the CASMO5 and SIMULATE5 simulations were automatized to find out an optimized equilibrium core design. The results demonstrated that all safety parameters of the optimized equilibrium core designs are within regulatory limits during normal operation and under REA conditions. By reducing the individual control rod worth, power spikes are considerably mitigated, thereby ensuring fuel integrity during an REA. Full article

This study investigates a polymorphic underwater vehicle designed to combine long-range cruising with stable underwater operation, reducing dependence on surface support vessels. By introducing a foldable polymorphic structure, the vehicle can switch configurations, including serial and parallel. However, underwater environments often contain obstacles, and the vehicle may collide with them during the folding process. To prevent collisions between the vehicle and surrounding obstacles during the folding process, this paper investigates the motion envelope of the vehicle and examines how motion parameters and mass distribution influence the motion envelope. In this work, the polymorphic underwater vehicle is modeled as a multibody system operating under a neutrally buoyant condition. Based on space robot modeling methodologies and the linear and angular momentum theorems, the equations of motion of the polymorphic underwater vehicle are derived and verified using the Adams software 2020. In summary, the present study establishes a clear relationship between motion parameters, mass distribution, hydrodynamic effects, and the resulting motion envelope of a polymorphic underwater vehicle. The results show that the attitude of the vehicle during the folding process is uniquely determined by the joint angles, and a larger relative speed between the outer and inner folding motions produces a more compact attitude during the folding process. Mass distribution further influences the motion envelope of the vehicle: concentrating mass toward the center of the vehicle shifts the overall motion envelope upward, whereas concentrating mass toward both ends of the vehicle shifts it downward. In addition, hydrodynamic forces introduce an upward velocity component of the vehicle in the vertical direction during the folding process, which leads to an upward shift in the overall center of mass of the vehicle. Full article

The integration of collaborative robots into industrial environments requires rigorous safety validation under the Power and Force Limiting (PFL) regime. This review article systematically maps the technological and normative development of certified Pressure and Force Measurement Devices (PFMDs) and experimental biofidelic instruments for Physical Human–Robot Interaction (pHRI) between the years 2011 and 2026. A quantitative screening of 68 studies revealed a publication peak in impact metrology in 2021. This peak occurred with a five-year latency after the release of the ISO/TS 15066 technical specification. Although global interest in collaborative robotics steadily grows, the publication trend indicates a gradual shift in scientific focus from reactive testing toward proactive prevention. A methodological deconstruction of four Research Questions (RQs) identifies persistent limitations in safety evaluation. The findings demonstrate that the internal structure of conventional sensors induces nonlinear shock filtering and parasitic oscillations (RQ1). Furthermore, the rigid fixation of test stands generates unrealistic pressure spikes. This physical limitation forces a transition to flexible and pendulum-based configurations (RQ2). Commercial flat films physically fail due to sensor saturation and introduced stiffness. Such failures accelerate the development of conformable electronic skins (e-skins) and multimodal test manikins (RQ3). To ensure interlaboratory reproducibility within the current ISO 10218-2:2025 standard, the text defines imperative metrological parameters. These parameters strictly include frequency response, calibration protocols, and volumetric mapping of inertial masses (RQ4). Furthermore, the analysed publications were systematically stratified into distinct technological categories, strictly reflecting their primary engineering domains, ranging from empirical metrological evaluation and sensor hardware design to advanced numerical modeling. Finally, the vision for future research anticipates a definitive shift toward proactive anti-collision technologies, encompassing Artificial Intelligence (AI), machine vision, and Augmented Reality/Virtual Reality/Mixed reality (AR/VR/MR). Future methodologies must also consider demographic anisotropies and the cognitive fatigue of the human operator. Full article

This paper introduces an adaptive fixed-time position controller (AFxTPC) designed for the lifting axis servo mechanism of a computer numerical control (CNC) plasma machine. It integrates a permanent magnet synchronous motor, gearbox, and ball screw into a unified electromechanical model. The proposed AFxTPC combines a fixed-time terminal sliding surface function with adaptive fixed-time sliding mode control to achieve fixed-time convergence, precise tracking, and robustness in the presence of parameter uncertainties. A specially designed reaching law guarantees accurate trajectory tracking, while the fixed-time terminal sliding surface function effectively minimizes chattering near the sliding manifold. Importantly, a novel fixed-time nonlinear disturbance observer is developed to simultaneously estimate the unmeasured system states and lumped disturbances in real time within a guaranteed initial-state-independent settling time. These estimated values are explicitly fed back into controller for active disturbance compensation. The stability of the overall closed-loop system is rigorously established using Lyapunov stability theory. Simulation results demonstrate that the proposed observer-based controller achieves superior performance compared with conventional proportional–integral–derivative (PID) and standard sliding mode controllers. It exhibits zero steady-state error, reduced overshoot, minimal chattering, and strong robustness over a wide range of operating conditions. Full article

Offshore wind turbine blades in cold marine environments are exposed to low-temperature, high-humidity, and saline-droplet conditions, under which the melting behavior, interfacial sliding, and de-icing energy demand of saline ice differ from those of freshwater ice. Existing studies on combined phase-change coating–electrothermal de-icing have mainly focused on freshwater icing. Here, a glass-fiber-reinforced polymer (GFRP) NACA0018 airfoil was tested in a recirculating low-temperature icing wind tunnel to evaluate an n-tetradecane phase-change microcapsule/polyurethane (PCMS-PUR) coating combined with electrothermal heating at a salinity of 3%. Operating parameters, including heat flux density (8, 10, and 12 kW/m²), ambient temperature (−5, −10, and −15 °C), and incoming wind speed (3, 6, and 9 m/s), were systematically varied under a constant water flow rate (60 mL/min) and spray pressure (0.3 MPa) to characterize the evolution of ice morphology, temperature response, and de-icing energy consumption. During electrothermal de-icing, saline ice was more prone to interfacial softening and lubricating meltwater-layer formation, resulting in a dominant whole-block sliding detachment mode rather than gradual local melting. The PCMS-PUR coating further promoted interfacial melting and advanced ice destabilization through latent-heat release and thermal buffering. When the heat flux density increased from 8 to 12 kW/m², the de-icing energy consumption of the uncoated and coated blades decreased by 45.08% and 42.53%, respectively. The maximum energy-saving efficiency of the combined system reached 16.27% at 9 m/s. These findings clarify the de-icing behavior and energy-saving potential of combined phase-change coating–electrothermal systems under saline icing and provide guidance for the design of low-energy de-icing systems for offshore wind turbine blades. Full article

One of the bottlenecks in realizing all-optical computing is the lack of on-chip all-optical logic devices that combine compactness, low loss, and high robustness. Valley photonic crystals (VPCs) have become an important solution for realizing such devices, relying on the excellent transmission characteristics of topological valley states. However, existing structures still face issues such as limited design flexibility. In this paper, a high-performance topological all-optical logic device based on VPCs consisting of circular ring dielectric columns is designed and demonstrated. By introducing the inner radius as an independent design parameter, we construct a new type of VPC and systematically investigate its influence on the photonic band gap. Based on this, we design a beam splitter with high operational bandwidth and low insertion loss (<0.5 dB) and then realize fundamental OR and XOR logic gates, achieving extinction ratios of 18.9 dB for the OR gate and up to 44 dB for the XOR gate at an operating frequency of 193.5 THz. The platform also supports the NOT gate and, through cascading, can implement more logic functions such as the AND gate. Full article

The rapid scaling of large language model (LLM) training has intensified demand for Graphics Processing Unit (GPU) clusters balancing throughput with energy efficiency. While NVIDIA’s H100 and B200 architectures are increasingly deployed in production datacenters, their comparative behavior under distributed training remains insufficiently characterized beyond vendor specifications, leaving datacenter operators without empirical guidance on metrics such as TFLOPs/kW and tokens-per-kilojoule. This work presents a system-level evaluation of single-node 8× H100 and 8× B200 configurations using Distributed Data Parallel (DDP) training across LLMs and vision–language models (VLMs) ranging from 7B to 32B parameters, spanning various real AI workload scenarios. We benchmark end-to-end throughput, utilization, power, energy, TFLOPs/kW, and tokens-per-kilojoule, complemented by architectural analysis explaining observed behavioral differences. Across LLM workloads, B200 achieves higher utilization (1–6%), faster training (up to 15%), and greater compute efficiency (up to 32% higher TFLOPs/GPU), attributable to higher memory bandwidth and large streaming multiprocessor (SM) count. However, B200 exhibits lower TFLOPs/kW and tokens-per-kilojoule, revealing a fundamental trade-off: throughput gains come at a measurable energy cost per useful token. VLM results further expose model-dependent asymmetries, with B200 consuming disproportionately more energy for lighter compute kernels due to elevated baseline power draw. These findings provide an empirical framework distinguishing compute efficiency from energy efficiency across next-generation GPU nodes, offering practical guidance for energy-aware AI datacenter design. Full article

To protect human health and the environment, it is necessary to reduce the number of solid particles and harmful gases in the air or to minimize such pollution. Filtration and separation devices are intended for various industrial operations to capture pollutants from various technological processes. In the introduction, this article points out the use of cyclone filters in individual operations, names the most frequently occurring elements of pollution, and suggests the most suitable method of separation. In paint shops, grinding shops, welding workplaces, machining lines, and when handling powder materials, particles with very different properties are created. An important advantage of using cyclone filters is not only their simple construction but also their usability at high temperatures and pressures. Furthermore, this article highlights that cyclones are easy to maintain, typically contain no moving parts, are simple to manufacture, and are cost-effective, particularly as pre-filtration devices. Their efficiency generally ranges from 50% to 99% and is strongly influenced by design and operating parameters, especially cyclone geometry, which affects pressure drop, flow structure, cut diameter, and fractional collection efficiency. The article also summarizes that various modifications of the inlet, vortex finder, outlet pipe, and cyclone body have been proposed to enhance separation performance, particularly for smaller particles. Nevertheless, due to the centrifugal and inertial nature of cyclone separation, fine and submicrometric particulate matter remains difficult to remove using cyclones alone. Fabric filters are also analyzed as a possible solution, but high loading by coarse particles may cause clogging, increased pressure drop, and higher maintenance costs. In the end, the combination of a cyclone with an electrostatic precipitator is presented as a staged separation approach, enabling efficient removal of both coarse particles and fine particulate matter from the gas stream. Full article

Predicting thermal breakthrough and optimizing injection-production parameters are essential for sustainable geothermal development. Traditional hydrothermal coupled simulations in porous media entail substantial computational costs, which limits their use in dense multi-parameter screening. This study develops a physics-constrained surrogate workflow for the Qihe geothermal doublet system by using COMSOL to generate hydrothermal simulation data and a deep neural network (DNN) to emulate the simulator response within a predefined operating domain. The DNN was trained on physics-driven synthetic outputs rather than independent field observations, and a 2.0 °C decrease in production temperature was used as the thermal breakthrough criterion. Under scenario-wise validation, the surrogate model achieved a test-set R² of 0.9995 and an RMSE of 0.0351 °C, indicating accurate approximation of the deterministic simulator response within the bounded parameter space. The surrogate-based global scan identified a favorable operating region near a well spacing of 462 m, a reinjection temperature of 20 °C, and a reinjection rate of 150 m³/h. To evaluate whether this result was affected by sparse well-spacing sampling, additional COMSOL simulations were performed at 430, 440, 450, 460, 462, 470, 480, 490, and 500 m under the same reinjection temperature and rate. These simulator-based validation cases showed a continuous thermal response with increasing well spacing. The 2.0 °C thermal breakthrough time increased from 46 yr at 430 m to 61 yr at 500 m, while the 50-year cumulative heat extraction increased from 6594.2 to 6722.9 TJ. The 430 and 440 m cases experienced thermal breakthrough before the 50-year design life, whereas the 450 m case was close to the design boundary. The 460 and 462 m cases did not reach the 2.0 °C decline threshold within the 50-year design life and retained relatively high heat-extraction efficiency per unit well spacing. Therefore, the engineering recommendation is revised from a single precise optimum to a locally validated spacing interval of approximately 460–462 m under the present equivalent-porous-medium assumption. The proposed workflow does not replace hydrothermal simulation; instead, it provides a rapid screening tool that narrows the design space before targeted simulator verification and field calibration. Full article