Search Results (1,452)

This study presents transistor-level simulation results for a novel memristor emulator circuit. The design incorporates an inverter and a current-mode-controlled operational transconductance amplifier to stabilize the output voltage. Transient performance is evaluated across a 20 MHz to 100 MHz frequency range. Simulations using 0.18 μm TSMC technology confirm the circuit’s functionality, demonstrating a power consumption of 0.1 mW at a 1.2 V supply. The memristor model’s reliability is verified through corner simulations, along with Monte Carlo and temperature variation tests. Furthermore, the emulator is applied in a Memristive Integrate-and-Fire neuron circuit, a CMOS-based system that replicates biological neuron behavior for spike generation, enabling ultra-low-power computing and advanced processing in retinal prosthesis applications. Full article

The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article

Millimeter-wave frequencies are crucial for meeting the high-capacity, low-latency demands of 5G communication systems, thereby driving the need for compact, high-gain antenna arrays capable of efficient beamforming. This paper presents the design, simulation, fabrication, and experimental validation of a compact, high-efficiency 1 × 6 linear series-fed microstrip patch antenna array for 5G millimeter-wave communication operating at 28 GHz. The proposed antenna is fabricated on a low-loss Rogers RO3003 substrate and incorporates an integrated symmetric two-way microstrip power divider to ensure balanced feeding and phase uniformity across elements. The antenna achieves a simulated peak gain of 11.5 dBi and a broad simulated impedance bandwidth of 30.21%, with measured results confirming strong impedance matching and a return loss better than −20 dB. The far-field radiation patterns demonstrate a narrow, highly directive beam in the E-plane, and the H-plane results reveal beam tilting behavior, validating the antenna’s capability for passive beam steering through feedline geometry and element spacing (~0.5λ). Surface current distribution analysis confirms uniform excitation and efficient radiation, further validating the design’s stability. The fabricated prototype shows excellent agreement with the simulation, with minor discrepancies attributed to fabrication tolerances. These results establish the proposed antenna as a promising candidate for applications requiring compact, high-gain, and beam-steerable solutions, such as 5G mm-wave wireless communication systems, point-to-point wireless backhaul, and automotive radar sensing. Full article

Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The IMFs are then grouped into low- and high-frequency components based on multiscale entropy (MSE) and K-Means clustering. To further alleviate mode mixing in the high-frequency components, an improved variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is applied for secondary decomposition. Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). The main contributions are the improved feature-extraction process and the innovative use of a sliding window-based SLSQP method for dynamic ensemble weight optimization. Full article

The in situ emulsification synergistic self-profile control system has wide application prospects for efficient development on offshore oil reservoirs. During water flooding in Bohai heavy oil reservoirs, random emulsification occurs with superimposed Jamin effects. Effectively utilizing this phenomenon can enhance the efficient development of offshore oilfields. This study addresses the challenges hindering water flooding development in offshore oilfields by investigating the emulsification mechanism and key influencing factors based on oil–water emulsion characteristics, thereby proposing a novel in situ emulsification flooding method. Based on a fundamental analysis of oil–water properties, key factors affecting emulsion stability were examined. Core flooding experiments clarified the impact of spontaneous oil–water emulsification on water flooding recovery. Two-dimensional T1–T2 NMR spectroscopy was employed to detect pure fluid components, innovating the method for distinguishing oil–water distribution during flooding and revealing the characteristics of in situ emulsification interactions. The results indicate that emulsions formed between crude oil and formation water under varying rheometer rotational speeds (500–2500 r/min), water cuts (30–80%), and emulsification temperatures (40–85 °C) are all water-in-oil (W/O) type. Emulsion viscosity exhibits a positive correlation with shear rate, with droplet sizes primarily ranging between 2 and 7 μm and a viscosity amplification factor up to 25.8. Emulsion stability deteriorates with increasing water cut and temperature. Prolonged shearing initially increases viscosity until stabilization. In low-permeability cores, spontaneous oil–water emulsification occurs, yielding a recovery factor of only 30%. For medium- and high-permeability cores (water cuts of 80% and 50%, respectively), recovery factors increased by 9.7% and 12%. The in situ generation of micron-scale emulsions in porous media achieved a recovery factor of approximately 50%, demonstrating significantly enhanced oil recovery (EOR) potential. During emulsification flooding, the system emulsifies oil at pore walls, intensifying water–wall interactions and stripping wall-adhered oil, leading to increased T2 signal intensity and reduced relaxation time. Oil–wall interactions and collision frequencies are lower than those of water, which appears in high-relaxation regions (T1/T2 > 5). The two-dimensional NMR spectrum clearly distinguishes oil and water distributions. Full article

Unmanned Surface Vehicle (USV) offers a cost-effective platform for high-resolution marine magnetic surveys using shipborne fluxgate magnetometers. However, platform-induced magnetic interference and electromagnetic interference (EMI) can degrade data quality, even with paramagnetic hulls. This study evaluates fluxgate magnetometer data acquired from a paramagnetic-hulled USV. Noise characterization identified EMI and maneuver-induced high-frequency noise, the latter of which was effectively reduced through low-pass filtering. We compared four different correction approaches addressing both vessel attitude and magnetization. The results demonstrate that the paramagnetic hull significantly reduces magnetic interference and shortens the duration of viscous magnetization (VM) effects caused by eddy currents in the platform, compared to conventional ferromagnetic vessels. Nonetheless, residual magnetization from onboard ferromagnetic components still requires correction. A method utilizing all nine components of the susceptibility tensor demonstrated improved accuracy and stability. Despite corrections, low-frequency VM-related noise during azimuth changes and a consistent absolute offset (~200 nT) remain when compared to towed scalar magnetometer data. These findings validate the use of paramagnetic USV for vector magnetic surveys, highlighting their benefit in VM mitigation while emphasizing the need for further development in VM correction and offset correction to achieve high-precision measurements. Full article

Enhancing the resilience of renewable energy systems in ultra-weak grids is crucial for promoting sustainable energy adoption and ensuring a reliable power supply during disturbances. Ultra-weak grids characterized by a very low Short-Circuit Ratio, less than 2, and high grid impedance significantly impair voltage and frequency stability, imposing challenging conditions for Inverter-Based Resources. To address these challenges, this paper considers a 110 KVA, three-phase, two-level Voltage Source Converter, interfacing a 700 V DC link to a 415 V AC ultra-weak grid. X/R = 1 is controlled using Sinusoidal Pulse Width Modulation, where the Grid-Connected Converter operates in Grid-Forming Mode to maintain voltage and frequency stability under a steady state. During symmetrical and asymmetrical faults, the converter transitions to Grid-Following mode with current control to safely limit fault currents and protect the system integrity. After fault clearance, the system seamlessly reverts to Grid-Forming Mode to resume voltage regulation. This paper proposes an improved control strategy that integrates voltage feedforward reactive power support and virtual capacitor-based virtual inertia using Active Disturbance Rejection Control, a robust, model-independent controller, which rapidly rejects disturbances by regulating d and q-axes currents. To test the practicality of the proposed system, real-time implementation is carried out using the OPAL-RT OP4610 platform, and the results are experimentally validated. The results demonstrate improved fault current limitation and enhanced DC link voltage stability compared to a conventional PI controller, validating the system’s robust Fault Ride-Through performance under ultra-weak grid conditions. Full article

This paper gives a thorough overview of the technological advancements in microgrid systems, focusing on the Internet of Things (IoT), predictive analytics, real-time monitoring, architectures, control strategies, benefits, and drawbacks. It highlights their importance in boosting system security, guaranteeing real-time control, and increasing energy efficiency. Accordingly, researchers have embraced the involvement of many control capacities through voltage and frequency stability, optimal power sharing, and system optimization in response to the progressively complex and expanding power systems in recent years. Advanced control techniques have garnered significant interest among these management strategies because of their high accuracy and efficiency, flexibility and adaptability, scalability, and real-time predictive skills to manage non-linear systems. This study provides insight into various facets of microgrids (MGs), literature review, and research gaps, particularly concerning their control layers. Additionally, the study discusses new developments like Supervisory Control and Data Acquisition (SCADA), blockchain-based cybersecurity, smart monitoring systems, and AI-driven control for MGs optimization. The study concludes with recommendations for future research, emphasizing the necessity of stronger control systems, cutting-edge storage systems, and improved cybersecurity to guarantee that MGs continue to be essential to the shift to a decentralized, low-carbon energy future. Full article

Hydrogen production through electrolysis of water can achieve efficient, stable and diversified utilization of renewable energy. To this end, a hybrid electrolyzer system for hydrogen production based on bi-layer optimization is constructed. Firstly, the wind and photovoltaic power is decomposed into high-frequency and low-frequency components by an adaptive wavelet packet. The low-frequency power is allocated to the alkaline electrolyzers (AWE) to ensure its stability, and the high-frequency power is allocated to the proton exchange membrane electrolyzers (PEM) with a faster response characteristic, thereby improving the energy utilization rate. This paper proposes a bi-layer optimization model, in which the upper-layer objective is to minimize the cost of mixed hydrogen production, and the lower-layer optimization objective is to maximize the utilization rate of renewable energy. The differential evolution algorithm optimizes the upper-layer objective, with results sent to the lower layer. Then, the YALMIP toolbox is used to solve the lower-layer objective. Through case analysis, the optimal proportion of AWE and PEM hydrogen electrolyzers obtained by this optimization method is 89.5 and 10.5, respectively. Compared with a single type of electrolyzer, the method proposed in this paper effectively improves the energy utilization efficiency and reduces the cost of hydrogen production. Full article

This paper presents a 17–38 GHz wideband low-noise amplifier (LNA) designed in a 150-nm GaAs pHEMT process. The proposed amplifier adopts a cascode topology with an interstage inductor between the common-source (CS) and common-gate (CG) stages, and a series inductor at the source node of the CS stage for source degeneration. By incorporating these inductors in the amplification stage, simultaneous noise and input matching is facilitated, while achieving flat gain characteristics over a broad frequency range and ensuring stability. In addition, the amplification stage with inductors achieves input matching using only a shunt component in the DC bias path, without any series matching elements. This approach allows the amplifier to achieve simultaneous noise and input matching (SNIM), ensuring low-noise performance over a wide bandwidth. The simulation results show a flat gain of 20–23 dB and a low noise figure of 1.1–2.1 dB over the 17–38 GHz band. Full article

This article uses FLUENT to construct a two-dimensional axisymmetric numerical model of a cryogenic hydrogen charging pipeline. By loading with initial temperature gradient and transient initial pressure disturbance, the basic characteristics of low-temperature hydrogen Taconis thermoacoustic oscillation are calculated, including temperature, heat flux density distribution, pressure amplitude, and frequency. The instability boundary of hydrogen TAO is also obtained. The results show that (1) the temperature distribution and flow characteristics of the gas inside the pipeline exhibit significant periodic changes. In the first half of the oscillation period, the cold-end gas moves towards the end of the pipeline. Low-viscosity cold hydrogen is easily heated and rapidly expands. In the second half of the cycle, the expanding cold gas pushes the hot-end gas to move towards the cold end, forming a low-pressure zone and causing gas backflow. (2) Thermoacoustic oscillation can also cause additional thermal leakage on the pipeline wall. The average heat flux during one cycle is 1150.1 W/m² for inflow and 1087.7 W/m² for outflow, with a net inflow heat flux of 62.4 W/m². (3) The instability boundary of the system is mainly determined by the temperature ratio of the cold and hot ends α, temperature gradient β, and length ratio of the cold and hot ends ξ. Increasing the pipe diameter and minimizing the pipe length can effectively weaken the amplitude of thermoacoustic oscillations. This study provides theoretical support for predicting thermoacoustic oscillations in low-temperature hydrogen transport pipeline systems and offers insights for system stability control and design verification. Full article

The increasing penetration of renewable energy sources (RESs) has significantly altered the operational characteristics of modern power systems, resulting in reduced system inertia and fault current capacity. These developments introduce new challenges for maintaining frequency and voltage stability, particularly in low-inertia grids that are dominated by inverter-based resources (IBRs). This paper presents a hierarchical control framework that integrates synchronous condensers (SCs) and grid-forming (GFM) inverters through a leader–follower consensus control architecture to address these issues. In this approach, selected GFMs act as leaders to restore nominal voltage and frequency, while follower GFMs and SCs collaboratively share active and reactive power. The primary control employs droop-based regulation, and a distributed secondary layer enables proportional power sharing via peer-to-peer communication. A modified IEEE 14-bus test system is implemented in PSCAD to validate the proposed strategy under scenarios including load disturbances, reactive demand variations, and plug-and-play operations. Compared to conventional droop-based control, the proposed framework reduces frequency nadir by up to 0.3 Hz and voltage deviation by 1.1%, achieving optimised sharing indices. Results demonstrate that consensus-based coordination enhances dynamic stability and power-sharing fairness and supports the flexible integration of heterogeneous assets without requiring centralised control. Full article

To solve the problems of carbon fiber (CF) electrodes, including poor frequency response and large potential drift, CFs were subjected to a roughening pretreatment process combining thermal oxidation and electrochemical anodic oxidation and then modified with Ag nanoparticles (AgNPs) using electroplating to prepare a CF electric field sensor. The surface morphology of the as-prepared AgNP-CF electric field sensor was characterized via optical microscopy, scanning electron microscopy, XPS, and energy-dispersive spectroscopy, and its impedance, polarization drift, self-noise, and temperature drift values were determined. Results show that the surface modification of the AgNP-CF electric field sensor is uniform, and its specific surface area is considerably increased. The electrode potential drift, characteristic impedance, self-noise, and temperature drift are 52.1 µV/24 h, 3.6 Ω, 2.993 nV/√Hz@1 Hz, and less than 70 µV/°C, respectively. Additionally, the AgNP-CF electric field sensor demonstrates low polarization and high stability. In field and simulated ocean tests, the AgNP-CF electrode exhibits excellent performance in the field and underwater environments, which renders it promising for the measurement of the ocean and geoelectric fields owing to its advantages, such as low noise and high stability. Full article

Rotor–stator interaction and the coupling between the clearance flow and main flow amplify the flow complexity in small-scale, high-speed automotive pumps. This degrades the pressure fluctuations, compromising the operational stability of these pumps. To better understand the pressure fluctuation distribution characteristics within such a pump, the Reynolds-averaged Navier–Stokes equations and the shear stress transport k-ω turbulence model were applied to numerically compute the pump. The simulation results were compared with experimental data, and good agreement was achieved. The results show that pressure fluctuations in the main flow region are mainly dominated by the blade passing frequency, and the intensity of pressure fluctuations in the near-field area of the tongue reaches its peak value, showing significant fluctuation characteristics. Significant peak signals are captured in the low-frequency band of pressure fluctuations in the clearance region. The pressure fluctuation characteristics are also affected by the rotor–stator interaction between the impeller front shroud and the volute casing, while the dominant frequency is still the blade passing frequency. In addition, the dominant frequencies of pressure fluctuations in the main and clearance flows show a similar distribution to the flow rate, but the minimum amplitude corresponds to different flow rates. Full article

Accelerating climate change poses significant risks to water security and ecological stability in arid regions due to the increasing frequency and intensity of extreme precipitation events. As a climate-sensitive area, the inland river basin (IRB) of Northwest China—a critical water source for local ecosystems and socioeconomic activities—remains insufficiently studied in terms of future extreme precipitation dynamics. This study evaluated the spatiotemporal evolution of extreme precipitation in the IRB under a new low radiative forcing scenario (SSP1-1.9) by employing four global climate models (GCMs: GFDL-ESM4, MRI-ESM2, MIROC6, and IPSL-CM6A-LR). Eight core extreme precipitation indices were analyzed to quantify changes during the near future (NF: 2021–2050) and far future (FF: 2071–2100) periods. Our research demonstrated that all four models were capable of capturing seasonal patterns and exhibited inherent uncertainty. The annual total precipitation (PRCPTOT) in mountainous regions showed minimal variation, while desert areas were projected to experience a 2-6-fold increase in precipitation in the NF and FF. The Precipitation Intensity Index (SDII) weakened by approximately −10% in mountainous areas but strengthened by around +10% in desert regions. Most mountainous areas showed an increase in the maximum consecutive dry days (CDD), whereas desert regions exhibited extended maximum consecutive wet days (CWD). Moderate rainfall (P1025) variations primarily ranged between −5% and +20%, with greater fluctuations in desert areas. Heavy rainfall (PG25) fluctuated between −40% and +40%, reflecting stark contrasts in extreme precipitation between arid basins and mountainous zones. The maximum 1-day precipitation (Rx1day) and maximum 5-day precipitation (Rx5day) both showed significant increases, which indicated heightened risks from extreme rainfall events in the future. Moreover, the IRB region experienced increased total precipitation, enhanced rainfall intensity, more frequent alternations between drought and precipitation, more frequent moderate-to-heavy rainfall days, and higher daily precipitation extremes in both the NF and FF periods. These findings provide critical data for regional development planning and emergency response strategy formulation. Full article