Search Results (1,376)

Compact and efficient absorption refrigeration systems can effectively utilize waste heat and renewable energy when operated in a boiling-desorption mode, which maximizes the desorption rate. Hydrophobic membranes play a critical role in microchannel membrane-based generators; however, limited research has addressed bubble cross-membrane removal dynamics under boiling-desorption conditions, particularly the influence of membrane hydrophobicity. In this study, a two-phase flow bubble-removal model was developed to accurately represent boiling-desorption behavior. Numerical simulations were performed to investigate the effects of membrane hydrophobicity and heating power on bubble dynamics, wall temperature, venting rate, and channel pressure drop. Results show that bubble venting proceeds through four stages: nucleation and growth, liquid-film rupture with deformation, lateral spreading, and sustained vapor removal. Hydrophobicity effects become most significant from the third stage onwards. Increased hydrophobicity reduces wall temperature, with greater reductions at higher heat fluxes, and enhances venting performance by increasing total vapor removal and reducing removal time. Channel pressure fluctuations comprise high-frequency components from bubble growth and low-frequency components from venting-induced flow interruptions, with relative contributions dependent on hydrophobicity and heat flux. These findings provide new insights into bubble-removal mechanisms and offer guidance for the design and optimization of high-performance microchannel membrane-based generators. Full article

This study proposes a hierarchical drive control system to ensure speed stability in dual-motor tracked vehicles operating under complex terrain and heavy-load conditions. The system adopts a two-layer structure. At the upper level, the sliding mode controller is designed for both longitudinal speed regulation and yaw rate control, thereby stabilizing straight line motion and the steering maneuvers. At the lower level, a synchronization mechanism aligns the velocities of the two motors, enhancing the vehicle’s robustness against speed fluctuations. Simulation results demonstrate that, across both heavy load and light load bulldozing scenarios, the deviation between the controller output and the reference command remains within 5$\%$. These findings confirm the accuracy of the control implementation and validate the effectiveness of the proposed framework. Full article

The increasing global demand for renewable energy poses significant challenges to grid stability due to the fluctuation and unpredictability of photovoltaic (PV) power generation. To enhance the accuracy of short-term PV power prediction, this study proposes an innovative integrated model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), optimized using the Starfish Optimization Algorithm (SFOA) and integrated with a multi-method data processing framework. To reduce input feature redundancy and improve prediction accuracy under different conditions, the K-means clustering algorithm is employed to classify past data into three typical weather scenarios. Empirical Mode Decomposition is utilized for multi-scale feature extraction, while Kernel Principal Component Analysis is applied to reduce data redundancy by extracting nonlinear principal components. A hybrid CNN-BiLSTM neural network is then constructed, with its hyperparameters optimized using SFOA to enhance feature extraction and sequence modeling capabilities. The experiments were carried out with historical data from a Chinese PV power station, and the results were compared with other existing prediction models. The results demonstrate that the Root Mean Square Error of PV power generation prediction for three scenarios are 9.8212, 12.4448, and 6.2017, respectively, outperforming all other comparative models. Full article

In this paper, an efficient hybrid photovoltaic (PV) power forecasting model is proposed to enhance the stability and accuracy of PV power prediction under typical weather conditions. First, the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is employed to decompose both meteorological features affecting PV power and the power output itself into intrinsic mode functions. This process enhances the stationarity and noise robustness of input data while reducing the computational complexity of subsequent model processing. To enhance the detail-capturing capability of the Bidirectional Long Short-Term Memory (BiLSTM) model and improve its dynamic response speed and prediction accuracy under abrupt irradiance fluctuations, we integrate a Temporal Convolutional Network (TCN) into the BiLSTM architecture. Finally, a Multi-head Self-Attention (MHA) mechanism is employed to dynamically weight multivariate meteorological features, enhancing the model’s adaptive focus on key meteorological factors while suppressing noise interference. The results show that the ICEEMDAN-TCN-BiLSTM-MHA combined model reduces the Mean Absolute Percentage Error (MAPE) by 78.46% and 78.59% compared to the BiLSTM model in sunny and cloudy scenarios, respectively, and by 58.44% in rainy scenarios. This validates the accuracy and stability of the ICEEMDAN-TCN-BiLSTM-MHA combined model, demonstrating its application potential and promotional value in the field of PV power forecasting. Full article

Lithium-ion batteries (LIBs) are critical components in safety-critical systems such as electric vehicles, aerospace, and grid-scale energy storage. Their degradation over time can lead to catastrophic failures, including thermal runaway and uncontrolled combustion, posing severe threats to human safety and infrastructure. Developing a robust AI framework for degradation prognostics in safety-critical systems is essential to mitigate these risks and ensure operational safety. However, sensor noise, dynamic operating conditions, and the multi-scale nature of degradation processes complicate this task. Traditional denoising and modeling approaches often fail to preserve informative temporal features or capture both abrupt fluctuations and long-term trends simultaneously. To address these limitations, this paper proposes a hybrid data-driven framework that combines Sample Entropy-guided Variational Mode Decomposition (SE-VMD) with K-means clustering for adaptive signal preprocessing. The SE-VMD algorithm automatically determines the optimal number of decomposition modes, while K-means separates high- and low-frequency components, enabling robust feature extraction. A dual-branch architecture is designed, where Gated Recurrent Units (GRUs) extract short-term dynamics from high-frequency signals, and Transformers model long-term trends from low-frequency signals. This dual-branch approach ensures comprehensive multi-scale degradation feature learning. Additionally, experiments with varying sliding window sizes are conducted to optimize temporal modeling and enhance the framework’s robustness and generalization. Benchmark dataset evaluations demonstrate that the proposed method outperforms traditional approaches in prediction accuracy and stability under diverse conditions. The framework directly contributes to Artificial Intelligence for Security by providing a reliable solution for battery health monitoring in safety-critical applications, enabling early risk mitigation and ensuring operational safety in real-world scenarios. Full article

Magnetically levitated artificial hearts impose stringent requirements on the blood-pump motor: zero friction, minimal heat generation and full biocompatibility. Traditional mechanical-bearing motors and permanent-magnet bearingless motors fail to satisfy all of these demands simultaneously. A bearingless switched reluctance motor (BSRM), whose rotor contains no permanent magnets, offers a simple structure, high thermal tolerance, and inherent fault-tolerance, making it an ideal drive for implantable circulatory support. This paper proposes an 18/15/6-pole dual-stator BSRM (DSBSRM) that spatially separates the torque and levitation flux paths, enabling independent, high-precision control of both functions. To suppress torque ripple induced by pulsatile blood flow, a variable-overlap TSF-PWM-DITC strategy is developed that optimizes commutation angles online. In addition, a grey-wolf-optimized fast non-singular terminal sliding-mode controller (NRLTSMC) is introduced to shorten rotor displacement–error convergence time and to enhance suspension robustness against hydraulic disturbances. Co-simulation results under typical artificial heart operating conditions show noticeable reductions in torque ripple and speed fluctuation, as well as smaller rotor radial positioning error, validating the proposed motor and control scheme as a high-performance, biocompatible, and reliable drive solution for next-generation magnetically levitated artificial hearts. Full article

The high penetration of distributed generators (DGs) causes severe voltage fluctuations and voltage limit violations in distribution networks. Traditional control methods rely on precise line parameters, which are often unavailable or inaccurate, and therefore are limited in practical applications. This paper proposes a data-driven multi-mode adaptive control method with multi-region coordination to enhance the operational performance of distribution networks. First, the network is partitioned into multiple regions, each equipped with a local controller to formulate reactive power control strategies for DGs. Second, regions exchange voltage and current measurements to establish linear input–output relationships through dynamic linearization, thereby developing a multi-mode model for different control objectives. Finally, each region employs the gradient descent method to iteratively optimize its control strategy, enabling fast responses to changing operating conditions in distribution networks. Case studies on modified IEEE 33-node and 123-node test systems demonstrate that the proposed method reduces voltage deviation, load imbalance, and power loss by 31.25%, 19.17%, and 20.68%, respectively, and maintains strong scalability for application in large-scale distribution networks. Full article

The wireless charging of electric vehicles (EVs) has drawn much attention as it can ease the charging process under different charging situations and environmental conditions. However, power transfer rate and efficiency are the critical parameters for the wide adaptation of wireless charging systems. Different investigations are presented in the literature that have aimed to improve power transfer efficiency and to maintain constant power at the load side. This paper introduces a Maximum Efficiency Point Tracking (MEPT) system designed specifically for a reconfigurable S-LCC compensated wireless charging system. The reconfigurable nature of the S-LCC system supports the constant current (CC) and constant voltage (CV) mode of operation by operating S-LCC and S-SP mode. The proposed system enhances power transfer efficiency under load fluctuations, coil misalignments, and a wide range of operating conditions. The developed S-LCC compensated system inherently maintains the power transfer rate constantly under a majority of load variations. Meanwhile, the inclusion of the MEPT method with the S-LCC system provides stable and maximum output under different coupling and load variations. The proposed MEPT approach uses a feedback mechanism to track and maintain the maximum efficiency point by iteratively adjusting the DC-DC converter duty ratio and by monitoring load power. The proposed approach was designed and tested in a 3.3 kW laboratory scale prototype module at an operating frequency of 85 kHz. The simulation and hardware results show that the developed system provides stable maximum power under a wider range of load and coupling variations. Full article

This study aims to investigate the spatiotemporal evolution of cavitating flow and the associated acoustic responses around a NACA0015 hydrofoil. A coupled fluid–acoustic interaction model is developed by integrating a nonlinear cavitation model with vortex–sound coupling theory. Numerical simulations are conducted within a computational domain established for the hydrofoil to capture the interactions between cavitation dynamics and acoustic radiation. The results indicate that the temporal variations in cavity evolution and pressure fluctuations agree well with experimental observations. The simulations predict a dominant pressure fluctuation frequency of 30.15 Hz, consistent with the cavitation shedding frequency, revealing that the evolution of leading-edge vortex structures governs the periodic variations in the lift-to-drag ratio. Cavitation significantly modifies the development of vortex structures, with vortex stretching effects mainly concentrated near cavitation regions. The dilation–contraction term is closely associated with cavity formation, while the pressure–torque tilting term predominantly affects cloud cavitation collapse. Dynamic mode decomposition (DMD) shows that the coherent structures of the leading modes exhibit morphological similarity to multiscale cavitation and vortex structures. Furthermore, hydrofoil cavitation noise consists mainly of loading noise and cavitation-induced pulsating radiation noise, with surface acoustic sources concentrated in cloud cavitation shedding regions. The dominant frequency of cavitation-induced radiation noise is highly consistent with experimental measurements. Full article

Nitrogen compression requires centrifugal compressors to operate under relatively high ambient pressure. However, the internal instability characteristics of compressors handling high-density working fluids remain unclear. Therefore, this study employs Dynamic Mode Decomposition (DMD) to investigate unsteady flow fluctuations within an isolated centrifugal impeller under both best efficiency and near-stall conditions at high ambient pressure. Results show that as the throttling process progresses, distinct unsteady phenomena emerge within the impeller. Under near-stall conditions, the frequency of the instability is 0.44 times the blade passage frequency (BPF), manifesting as periodic pressure fluctuations throughout the entire blade passage. This instability originates from periodic passage blockages caused by fluctuations in tip leakage flow. Additionally, the pressure fluctuations at the impeller inlet exhibit a noticeable lag compared to those in the latter half of the passage. Through DMD analysis, it is found that after the tip leakage vortex exits the blade, it interacts with the pressure surface of the adjacent blade, affecting the tip loading of the neighboring blade and forming a dynamic cycle. However, this vortex is not the primary flow structure responsible for the instability. These insights into the nature of unsteady disturbances provide valuable implications for future stall warning and instability prediction technologies. Full article

This paper proposes a flow battery-lithium-ion battery hybrid energy storage system (HESS) bi-level optimization planning method to address flexibility supply-demand balance challenges in regional power grids with high renewable penetration at 220 kV and above voltage levels. The method establishes a planning-operation coordination framework: Upper-level planning minimizes total lifecycle investment and operation-maintenance costs; Lower-level operation incorporates multiple constraints including flexibility gap penalties, voltage fluctuations, and line losses, overcoming single-timescale limitations. The approach enhances global search capability through the Improved Weighted Average Algorithm (IWAA) and optimizes power allocation accuracy using adaptive Variational Mode Decomposition (VMD). Validation using grid data from Southwest China demonstrates significant improvements across five comparative schemes. Results show substantial reductions in total investment costs, penalty costs, voltage fluctuations, and line losses compared to benchmark solutions, enhancing grid power supply stability and verifying the effectiveness of the model and algorithm. Full article

Push–pull switch-mode power supplies are widely employed due to their high efficiency and power density. However, traditional designs typically depend on multiple auxiliary circuits to achieve functions such as power-up control, voltage regulation, and system protection, resulting in structural complexity and difficulty in debugging. Additionally, dual-power high-voltage amplifier systems often suffer from voltage deviations caused by supply imbalances or load fluctuations, potentially leading to equipment failure and significant economic losses. To overcome these limitations, we propose a novel digital signal-controlled push–pull driver power supply topology in this paper. Specifically, this design utilizes digital pulse-width modulation (PWM) signals to control multi-stage metal-oxide-semiconductor field-effect transistors (MOSFETs), incorporating adjustable duty-cycle drives, multi-channel current sensing, and fault protection mechanisms. Experimental validation was performed on a ±220 V, 20 kHz, 180 W power supply prototype. The results demonstrate excellent performance, notably enhancing stability and reliability in dual-side synchronous power supply scenarios. Thus, this digital-control topology effectively addresses the drawbacks of conventional push–pull designs and offers potential applications in nondestructive testing and high-voltage driving systems. Full article

This study experimentally investigates the dynamic behavior and energy conversion characteristics of a single contaminated bubble (d_eq = 2.49–3.54 mm, Re_b = 470–830) rising near a vertical wall (S* = 1.41–2.02) in a linear shear flow (the conditions of average flow rate 0.1 m/s and shear rate 0.5 s⁻¹) using a vertical water tunnel and varying sodium dodecyl sulfate (SDS) concentrations (0–50 ppm) and bubble sizes (via needle nozzles). High-speed imaging with orthogonal shadowgraphy captures bubble trajectories, rotation, deformation, and oscillation modes (2, 0) and (2, 2), revealing that an increasing SDS concentration suppresses deformation and the inclination amplitude while enhancing the oscillation frequency, particularly for smaller bubbles. Velocity analysis shows that vertical components remain steady, whereas wall-normal and spanwise fluctuations diminish with surfactant concentration, indicating stabilized trajectories. Additional mass force coefficients are larger for bigger bubbles and decrease with contamination level. Energy analysis demonstrates that surface energy dominates the total energy budget, with vertical kinetic energy comprising over 70% of the total kinetic energy under high SDS concentrations. The results highlight strong scale dependence and Marangoni effects in controlling near-wall bubble motion and energy transfer, providing insights for optimizing gas–liquid two-phase flow processes in chemical and environmental engineering applications. Full article

To enhance the energy efficiency of liquefied natural gas (LNG) terminals, this study developed a full-process dynamic simulation model using Aspen HYSYS (hereinafter referred to as HYSYS) to accurately replicate the time-varying energy consumption characteristics of key processes, including unloading, tank boil-off gas (BOG) management, recondensation, and vaporization for send-out. Through dynamic analysis of the impact of different operating conditions on the energy consumption of critical equipment, methane content and compressor outlet pressure were identified as sensitive factors, and multivariable interaction effects were quantified. Combining the Particle Swarm Optimization (PSO) algorithm to optimize equipment operating parameters and incorporating constraints such as equipment start-stop frequency and flare emissions, process improvements were achieved, including intelligent pre-cooling during unloading, multi-mode vaporization coupling, and model predictive control for storage tanks. Safety response logic under extreme conditions was also enhanced. Field validation results show that the optimized system reduces total energy consumption by 18.5%, with a relative error between simulated and field data of ≤13%. Daily equipment start-stop cycles decreased from five to two times, and flare emissions were reduced from 25 kg/h to 12 kg/h. Within a 95% confidence interval, the total energy consumption prediction fluctuated by ±4.2%, demonstrating good model stability. This study provides reliable technical support for energy-efficient operation of LNG terminals. The proposed multivariable interaction analysis and safety control strategies under extreme conditions further enhance the engineering applicability of the optimization framework. Full article

Accurate prediction of photovoltaic and wind power generation is essential for maintaining stable energy supply in integrated energy systems. However, the strong stochasticity and complex fluctuations in these energy sources pose significant challenges to forecasting. Traditional methods often fail to handle the non-stationary characteristics of the generation series effectively. To address this, we propose a novel hybrid prediction framework that integrates variational mode decomposition, the Pearson correlation coefficient, and a benchmark prediction model. Experimental results demonstrate the outstanding performance of the proposed method, achieving an R² value exceeding 0.995 along with minimal MAE and RMSE. The approach effectively mitigates hysteresis issues during prediction. Furthermore, the model shows strong adaptability; even when substituting different benchmark models, it maintains an R² above 0.99. When applied in a building heating system, accurate predictions help reduce indoor temperature fluctuations, enhance energy supply stability, and lower energy consumption, highlighting its practical value for improving energy efficiency and operational reliability. Full article