Search Results (18,155)

Aiming at multi-agent interest demands and environmental benefits, a distributionally robust game-theoretic optimization algorithm based on a green certificate–carbon trading mechanism is proposed for uncertain microgrids. At first, correlated wind–solar scenarios are generated using Kernel Density Estimation and copula theory and the probability distribution ambiguity set is constructed combining 1-norm and $\infty$-norm metrics. Subsequently, with gas turbines, renewable energy power producers, and an energy storage unit as game participants, a two-stage distributionally robust game-theoretic optimization scheduling model is established for microgrids considering wind and solar correlation. The algorithm is constructed by integrating a non-cooperative dynamic game with complete information and distributionally robust optimization. It minimizes a linear objective subject to linear matrix inequality (LMI) constraints and adopts the column and constraint generation (C&CG) algorithm to determine the optimal output for each device within the microgrid to enhance its overall system performance. This method ultimately yields a scheduling solution that achieves both equilibrium among multiple stakeholders’ interests and robustness. The simulation result verifies the effectiveness of the proposed method. Full article

The large-scale integration of doubly fed wind turbines reduces the inertia level of power systems and increases the risk of frequency instability. This paper analyzes the performance characteristics and application ranges of different types of energy storage technologies and addresses the limitations of conventional control methods, which cannot adjust energy storage power output in real time according to frequency variations and may hinder frequency recovery during the restoration stage. Based on a grid-forming doubly fed wind turbine model, this study adopts a hybrid ultracapacitor energy storage system as the auxiliary storage device. The hybrid configuration increases energy density and extends the effective support duration of the storage system, thereby meeting the requirements of longer-term frequency regulation. Furthermore, the paper proposes an adaptive inertia control strategy that combines an improved variable-K droop control with adaptive virtual inertia control to enhance the stability of doubly fed wind turbines under load fluctuations. Simulation studies conducted in MATLAB 2022/Simulink demonstrate that the proposed method significantly improves frequency stability in load disturbance scenarios. The strategy not only strengthens the frequency support capability of grid-connected wind turbine units but also accelerates frequency recovery, which plays an important role in maintaining power system frequency stability. Full article

Advancing rapidly in modern bridge engineering technology, through concrete-filled steel tubular (CFST) arch bridges have achieved widespread application in transportation infrastructure development. Nevertheless, vehicle fires occurring in complicated operational settings may rapidly escalate into major disasters. Fires in oil tankers are particularly dangerous for the safety of bridges. This study examines the fire resistance of through concrete-filled steel tubular (CFST) arch bridges exposed to tanker truck fires. The study formulates a detailed model utilizing Fire Dynamics Simulator (FDS) to simulate fire scenarios, elucidating the spatial temperature distribution characteristics within arch bridge structures. A three-dimensional finite element model established in ABAQUS (Abaqus 2024, Dassault Systèmes Simulia Corp, Providence, RI, USA) is employed to simulate structural responses by analyzing the mechanical behavior of key components under different fire conditions. Practical fire resistance design recommendations for extreme tanker truck fire scenarios are ultimately proposed. Numerical results demonstrate that structural components near the fire source (such as transverse bracings, hangers, and fire-exposed arch surfaces) experience significantly higher temperatures than other regions. Notable temperature gradients developing along hangers and arch ribs in fire-affected zones are observed, while substantial cross-sectional temperature gradients occurring in these components under tanker truck fires reveal their damage evolution mechanisms. The fire exposure scenario at the quarter-point of the midspan is identified as the most critical fire exposure scenario for through CFST arch bridges under tanker truck fires. Under this extreme scenario, the deflection on the fire-exposed side of the global structure exhibits a significant three-stage distribution characteristic: an initial ascending phase around 0–800 s, followed by a sharp descending phase during 800–1100 s, and then a stabilization trend. A fire resistance limit criterion based on component failure (t_f3 = 853.43 s) is established, and a global fire resistance limit assessment methodology for through CFST arch bridges under extreme tanker truck scenarios is proposed. Full article

Transonic shock buffet, characterized by large-amplitude self-sustained shock oscillations arising from shock wave/boundary layer interactions, poses significant challenges to aircraft handling quality and structural integrity. Conventional control strategies for buffet suppression typically require prior knowledge of unstable steady-state solutions or time-averaged flow fields and are only applicable to fixed-flow conditions, rendering them inadequate for realistic flight scenarios involving time-varying parameters. This study proposes a data-driven adaptive control framework for transonic buffet suppression utilizing localized morphing skin as the actuation mechanism. The control system employs a Multi-Layer Perceptron neural network that dynamically adjusts the local skin height based on lift coefficient feedback, with the target lift coefficient determined through a moving average method. Numerical simulations on the NACA0012 airfoil demonstrate that the optimal actuator configuration—a skin length of 0.2c with maximum deformation positioned at 0.65c—achieves effective buffet suppression with minimal settling time. Beyond this baseline case, the proposed method exhibits robust performance across different flow conditions. Furthermore, the controller successfully suppresses buffet under time-varying flow conditions, including simultaneous variations in Mach number and angle of attack. These results demonstrate the potential of the proposed framework for practical aerospace applications. Full article

Buildings account for nearly 32% of global energy consumption and serve as key demand-side flexibility resources in power systems with high renewable penetration. However, their utilization is constrained by the lack of an integrated framework that can jointly quantify energy-adjustable margin (BAM) and response duration (RD) under realistic operational and thermal comfort constraints. This study presents a coupled data–physical simulation framework integrating a Particle Swarm Optimization–Long Short-Term Memory–Random Forest (PSO-LSTM-RF) hybrid load forecasting model with EnergyPlus(24.1.0)-based building simulation. The PSO-LSTM-RF model achieves high-accuracy short-term load prediction, with an average R² of 0.985 and mean absolute percentage errors of 1.92–5.75%. Predicted load profiles are mapped to physically consistent baseline and demand-response scenarios using a similar-day matching mechanism, enabling joint quantification of BAM and RD under explicit thermal comfort constraints. Case studies on offices, shopping malls, and hotels reveal significant heterogeneity: hotels exhibit the largest BAM (up to 579.27 kWh) and longest RD (up to 135 min), shopping malls maintain stable high flexibility, and offices show moderate BAM with minimal operational disruption. The framework establishes a closed-loop link between data-driven prediction and physics-based simulation, providing interpretable flexibility indicators to support demand-response planning, virtual power plant aggregation, and coordinated optimization of source–grid–load interactions. Full article

Traditional position-based fluid (PBF) methods often suffer from interpolation inaccuracies and limited computational efficiency due to their fixed smoothing length. To address these limitations, this paper proposes an adaptive smoothing length model and implements full-pipeline parallel acceleration on GPUs. By incorporating both local neighbor count and density variation, the model dynamically adjusts particle smoothing length. This adaptation effectively mitigates two issues: surface distortion due to insufficient interpolation in sparse regions, and performance degradation caused by computational redundancy in dense regions. To resolve neighbor search asymmetry introduced by dynamic smoothing lengths, we designed a symmetry handling technique based on maximum smoothing length and an efficient spatial hashing search algorithm. Experimental results across multiple scenarios (including dam break and droplet impact) demonstrate that our method maintains simulation stability comparable to the fixed smoothing length approach while improving computational efficiency and enhancing local particle distribution uniformity. The improved uniformity is evidenced by a significant reduction in the variance of neighbor particle counts. Visually, the method yields more natural results for dynamic details such as splashing and fragmentation, thereby ensuring the visual realism of the simulations. Full article

Improving the energy efficiency of rolling bearings requires a component-resolved understanding of loss mechanisms. While analytical models capture load-dependent losses, load-independent hydraulic losses demand a physics-based approach. This paper presents a computational fluid dynamics (CFD) methodology for the qualification of individual hydraulic loss contributions and to assess their sensitivity to operating conditions. The approach decomposes the total hydraulic loss of the spherical roller bearing 22320 into component-level shares and is benchmarked against dedicated experiments. The simulated results show good agreement with experimental measurements, supporting the validity of the methodology. The discrepancy between the measured and simulated friction torque values averaged at 2–7%, with a single outlier. Furthermore, CFD methods have been demonstrated to be capable of predicting trends in hydraulic losses resulting from variations in speed and temperature. A consistent finding across all investigated conditions is that the rolling elements dominate the hydraulic losses. Churning-induced losses of the rolling elements contribute for more than 50% of the hydraulic losses of the hole bearing in every test. The proposed methodology offers a reproducible way to assign losses individually, compare operating scenarios and guide targeted design measures for loss reduction in rolling bearings. Furthermore, dynamic kinematic simulations of rolling bearings can be equipped with component-resolved hydraulic losses. This is enabling more accurate predictive modelling of the bearing kinematics and detecting effects such as slippage. Full article

This paper proposes a data-driven model-free robust predictive control strategy for parallel three-level NPC inverters based on finite control set model predictive control (FCS-MPC), focusing on the zero-sequence circulating current (ZSCC) problem under parameter mismatch conditions. A set of virtual voltage vectors with zero average common-mode voltage (CMV) is introduced to effectively suppress ZSCC without adding additional constraints to the cost function. Meanwhile, an Integral Sliding Mode Observer (ISMO) is integrated into the predictive control framework to enhance robustness and enable reliable control using only input–output data. Unlike existing studies that primarily consider ZSCC suppression under an ideal system, this work specifically addresses the practical scenario in which system parameters deviate from their nominal values. Even when ZSCC suppression strategies are employed, parameter mismatch can still lead to noticeable circulating currents, motivating the need for a more robust solution. Simulation and experimental results validate that the proposed approach achieves excellent current tracking, neutral-point voltage balance, and effective ZSCC suppression under parameter variations, demonstrating strong robustness and feasibility for practical applications. Full article

Anomalous IP packet fragmentation, whether caused by evasion attacks, misconfigurations, or network policy interference, presents a measurable threat to network integrity and intrusion detection systems. This paper introduces a lightweight, rule-based framework for detecting and classifying fragmented IP traffic. Unlike complex machine learning models that operate as “black boxes,” our model leverages the deterministic semantics of RFC 791 to inspect structural packet characteristics—such as offset alignment, Time-to-Live (TTL) consistency, and payload regularity—and classifies traffic into three transparent categories: normal (NONE), misconfigured (MISCONFIG), and adversarial (ATTACK). We generate an open-source and synthetic dataset of 10,000 packets, meticulously engineered to simulate a wide spectrum of benign and malicious fragmentation scenarios. Evaluation demonstrates high accuracy (99.23% overall) on this controlled dataset. Crucially, validation on the CIC-IDS-2017 real-world dataset confirms the model’s practical utility, showing a low false-positive rate (0.8%) on normal traffic and a significant increase in detectable anomalies during attack periods. This work provides a reproducible, interpretable, and efficient tool for forensic analysis and intrusion detection, enabling the precise diagnostics of packet-level fragmentation anomalies in operational networks. Full article

Hub motor-driven vehicles can control vehicle attitude by regulating the speed and torque of four wheels, supporting safe and stable lane changing and obstacle avoidance. However, under high-speed scenarios, these vehicles often suffer from poor stability, limited comfort, and inadequate trajectory tracking accuracy during lane changing and obstacle avoidance operations. To address these challenges, this study proposes a lane changing obstacle avoidance control strategy for hub motor-driven vehicles based on collision risk prediction. A fuzzy controller featuring a variable weight objective function is designed to balance lane changing efficiency and ride comfort, thereby generating an optimal lane changing and obstacle avoidance trajectory. Furthermore, a linear time-varying model predictive controller (LTV-MPC) is developed, which adaptively adjusts both the weighting coefficient of lateral displacement error in the objective function and the prediction horizon of the controller, enabling dynamic tuning of vehicle trajectory tracking accuracy. A dSPACE hardware-in-the-loop (HIL) platform was established to conduct simulations under typical obstacle avoidance scenarios. The simulation results show that under two easily destabilized conditions—high-adhesion, high-speed, large-curvature, and low-adhesion, medium-speed, large-curvature maneuvers—the proposed optimized control strategy limits the maximum lateral trajectory tracking error to 0.116 m and 0.143 m, representing reductions of 58.6% and 79.6% compared with the baseline control strategy. These results demonstrate that the proposed method enhances trajectory tracking accuracy and stability during lane changing and obstacle avoidance maneuvers. Full article

Global Navigation Satellite System (GNSS) signal acquisition in high-dynamic environments faces significant challenges due to large Doppler frequency offsets and stringent computational constraints. This paper proposes a frequency-domain compressed acquisition algorithm that reformulates the conventional two-dimensional code-phase/Doppler search as a set of independent one-dimensional sparse recovery problems. Doppler uncertainty is modeled as sparsity in a discretized frequency dictionary, and a low-coherence measurement matrix is designed offline via projected gradient descent with a two-stage annealing strategy. The resulting matrix significantly reduces maximum coherence and supports reliable sparse recovery from a small number of compressed measurements. During online operation, the receiver forms compressed observations for all code phases through efficient matrix operations and recovers sparse Doppler spectra using lightweight orthogonal matching pursuit. Simulation results show that the proposed method achieves a several-fold reduction in computational cost compared with classical parallel code-phase search while maintaining high detection probability at low carrier-to-noise density ratios and under large Doppler offsets, providing an effective solution for resource-constrained GNSS receivers in high-dynamic scenarios. Full article

This paper addresses the issues of target unreachability and local optima in traditional artificial potential field (APF) methods for UAV swarm path planning by proposing an improved collaborative obstacle avoidance algorithm. By introducing a virtual target position function to reconstruct the repulsive field model, the repulsive force exponentially decays as the UAV approaches the target, effectively resolving the problem where excessive obstacle repulsion prevents UAVs from reaching the goal. Additionally, we design a dynamic virtual target point generation mechanism based on mechanical state detection to automatically create temporary target points when UAVs are trapped in local optima, thereby breaking force equilibrium. For multi-UAV collaboration, intra-formation UAVs are treated as dynamic obstacles, and a 3D repulsive field model is established to avoid local optima in planar scenarios. Combined with a leader–follower control strategy, a hybrid potential field position controller is designed to enable rapid formation reconfiguration post-obstacle avoidance. Simulation results demonstrate that the proposed improved APF method ensures safe obstacle avoidance and formation maintenance for UAV swarms in complex environments, significantly enhancing path planning reliability and effectiveness. Full article

The existing state of charge (SOC) balancing scheme of the lithium battery energy storage system (LBESS) does not consider the state of health (SOH) of LBESS in the process of energy distribution, which results in an inability to reduce SOH balancing errors and increases maintenance costs for LBESS. To solve this problem, an SOC balancing scheme for LBESS of microgrids considering SOH is proposed. In this scheme, SOC equalization factor and health status factor (HSF) are introduced into droop control, and the power output of LBESS inverter is adjusted according to SOC and SOH status so as to achieve SOC balancing and reduce SOH imbalance errors. Simulation and experimental results demonstrate that the proposed SOC balancing factor and HSF can maintain SOC balancing and reduce SOH balancing difference even under load fluctuations by adjusting the output active power of LBESS. With the implementation of SOC balancing, its SOC balancing factor becomes zero, thereby achieving a frequency stabilization effect. In addition, the proposed solution has good effects in multiple LBESS scenarios and LBESS charging processes. Full article

Search and rescue (SAR) operations are often hindered by limited coverage, slow response times, and operational risks, making rapid and reliable victim detection a critical challenge. To address these limitations, this study presents an AI-driven UAV framework that integrates a simulated multi-UAV routing with a YOLOv8-based human detection model. A region-specific aerial dataset consisting of 2430 images and 2831 annotated human instances was collected across diverse terrains in Jordan in collaboration with the Jordan Design and Development Bureau (JODDB). After preprocessing and mosaic augmentation, the dataset expanded to nearly 6000 training samples, enabling robust model fine-tuning. YOLOv8, initialized with VisDrone weights, achieved 97.0% precision, 97.6% recall, and 98.4% mAP@0.50. A multi-UAV routing algorithm based on a lawnmower pattern ensured 100% coverage of a 17.6 km² pilot area using 16 UAVs with balanced mission durations. The results demonstrate that combining UAV clusters with AI-based detection significantly enhances scalability, coverage efficiency, and recall, reducing the risk of life-critical false negatives. While the system shows strong potential, challenges remain regarding communication constraints, latency, and environmental robustness. Overall, this work provides a validated framework for AI-supported UAV SAR operations and offers a foundation adaptable to broader disaster-response scenarios worldwide. Full article

With the increasing frequency of global climate change and natural disasters, the resilience and stability of port power grids have become crucial for ensuring continuous port operations. This study proposes a three-stage resilience optimization method for port power grids under disaster scenarios, aiming to enhance their supply capacity and operational flexibility across the pre-disaster, during-disaster, and post-disaster phases. In the pre-disaster stage, the model considers the uncertainty of photovoltaic (PV) generation and the reconfigurability of the grid, optimizing the quantity and spatial layout of mobile energy storage systems with the objective of minimizing configuration and load-shedding risk costs, thereby improving system disturbance resistance. During the disaster, the model integrates the dynamic coordination of distributed generators, PV units, and storage systems to minimize load-shedding costs and achieve staged restoration and multi-source energy coordination. In the post-disaster stage, considering the failure of lines and nodes caused by disasters, a topology reconstruction and source-load coordination optimization strategy is developed to ensure rapid power restoration and critical load supply. Simulation studies based on an improved IEEE 33-bus system demonstrate that the proposed robust optimization model in the pre-disaster phase significantly enhances risk resistance and system resilience, while the incorporation of mobile energy storage further improves the system’s flexibility and black-start capability. This research provides an effective theoretical foundation and practical framework for post-disaster recovery and resilience enhancement of port power grids. Full article