Search Results (61,203)

This paper discusses the operation and control of a low-voltage DC (LVDC) isolated distribution network powered by distributed generation (DG) from a variable-speed wind turbine induction generator (WTIG) to supply unbalanced AC loads. The system incorporates a DC-DC storage converter to regulate network voltages and interconnect battery energy storage with the DC network. The wind turbines are equipped with a squirrel cage induction generator (IG) to connect a DC network via individual power inverters (WTIG inverters). Loads are unbalanced ACs and are interfaced using transformerless power inverters, referred to as load inverters. The DC-DC converter is equipped with a novel control strategy, utilizing a droop regulator for the DC voltage to stabilize network operation. The control system is modeled based on Clark and Park transformations and is developed for the load inverters to provide balanced AC voltage despite unbalanced load conditions. The system employs the perturbation and observation (P&O) method for maximum power point tracking (MPPT) to optimize wind energy utilization, while blade angle controllers maintain generator performance within rated power and speed limits under high wind conditions. System operation is analyzed under two scenarios: normal operation with varying wind speeds and the effects of load variations. Simulation results using PSCAD/EMTDC demonstrate that the proposed LVDC isolated distribution network (DC) achieves a stable DC bus voltage within ±5% of the nominal value, efficiently delivers balanced AC voltages with unbalanced levels below 2%, and operates with over 90% wind energy utilization during varying wind speeds, confirming LVDC network reliability and robustness. Full article

This study introduces a novel discrete element method (DEM) model for compacted loess, incorporating a bond rate parameter within a linear contact bond model to simulate constitutive damage behavior. This enhancement significantly improves the characterization of structural damage from repeated wet–dry cycles, offering a quantitative method for predicting damage progression. Unlike existing DEM models, our model directly uses a bond rate parameter to quantitatively describe inter-particle bond deterioration, reflecting reduced bonding strength due to pore structure development and the weakening effect of water. Rigorous calibration and validation were performed using comparative experiments. A key innovation is the systematic analysis of microscopic parameters (contact stiffness, friction coefficient, contact strength, and bond rate) and their impact on macroscopic mechanical behavior. Our findings show that decreasing the bond rate significantly reduces the macroscopic mechanical properties, providing valuable insights into the micro–macro relationship. We comprehensively evaluated prediction sensitivity to these parameters. This methodology offers a new perspective on using DEM for predicting crucial civil engineering material properties, providing a valuable reference for incorporating bond rate parameters into future modeling, particularly for long-term geotechnical material behavior under environmental degradation. The model’s accurate representation of wet–dry cycle effects on loess strength improves earth structure design and safety. Full article

Current transient stability analysis of power systems with doubly fed induction generators (DFIGs) draws upon the assumption that nodal electromagnetic power equals to zero during the fault period. The omission of electromagnetic power degrades the fidelity of transient stability analysis and renders the analytical impact of fault location on stability indiscernible. To address this limitation, a DFIG-integrated power system-oriented transient stability mechanism analysis method is proposed involving fault location factors. Firstly, a foundational analysis model is established by integrating a simplified DFIG representation with the improved DC power flow corrected by the fault’s instantaneous short-circuit voltage. Secondly, the non-metallic symmetrical short-circuit fault is equivalently treated as a power injection source, and the faulted electromagnetic power of generators is derived. The proposed equivalence is roughly correct, as its faulted energy integral is validated to approximate ground-truth. Lastly, combining the above analytical formulas and extended equal area criterion (EEAC), a time-domain simulation-free explicit transient stability criterion incorporating fault location factor is settled. Simulation results in a double-generator system with DFIG integration confirm that, compared to existing transient stability criterion, the proposed criterion can expand the stability assessable area by approximately 10% while maintaining accuracy. Full article

In this paper, a data-driven system identification method is presented based on the data fusion of a dynamic model and flight test data. The dynamic model is built by a combination of nonlinear auto-regressive networks (NARX) and the steady-state model. In such a combination, NARX can calibrate the dynamic characteristics of high-pressure and low-pressure rotor speed based on automatic control system steady-state models. As such, the calibrated engine model’s output speed is able to meet the requirements of simulation test tolerance accuracy. To enhance the robustness of the dynamic model against measurement noise, the Kalman filter is used to fuse the model prediction and the measurement data with noise. As such, the fused model can efficiently remove the influence of measurement noise and improve prediction accuracy. The proposed method supports the construction of reliable and environment-adaptive platforms for simulation application verification and provides high-fidelity simulation incentives for the realization of simulation test scenarios in the aviation industry. Full article

As the lifespan of many anchored slope reinforcements in China approaches its end, there is an increased need for secondary reinforcement. This study addresses the interaction between new and existing anchors, a critical but challenging aspect. The study introduces the Anchor Rod Modified Newmark Sliding Block Method (AMNB), which enhances the traditional Newmark Sliding Block Method (NSBM) by incorporating maximum anchor rod tension, dynamic tension changes, and group anchor effects. This improvement enhances the prediction of seismic displacement in slopes with multiple anchors. The AMNB method represents an innovation in slope stability analysis, as it is the first to integrate these dynamic and interactive factors into a unified framework. Validation through comparison with established seismic permanent displacement calculations and the analysis of three typical seismic motions show the AMNB’s effectiveness in capturing dynamic behaviors under seismic excitation. Additionally, numerical simulations using FLAC3D were conducted to validate the proposed method. The results indicate that considering group anchor effects and dynamic tension changes reduces the predicted seismic permanent displacement by up to 10% compared to traditional methods. The proposed AMNB method aligns more closely with the numerical simulation results. The findings indicate that group anchor effects negatively impact anchor forces, dynamic yield accelerations, and seismic displacements, leading to lower anchor tensions and dynamic yield accelerations. This, in turn, results in larger final slope permanent displacements under the same conditions. Full article

Efficiently mapping hourly air quality at a fine scale (25 m) remains a computational challenge. This difficulty is heightened when aiming to accurately capture industrial plumes and time-varying traffic emissions. This paper presents a method for generating hourly pollutant concentration maps across an entire region for operational applications. Our approach assumes that concentration maps can be decomposed into three components: traffic concentrations, industrial concentrations and a residual “background” concentrations component. The background concentration is estimated using established fine-scale mapping methods involving ADMS-Urban dispersion simulations. Meanwhile, the traffic and industrial layers are derived using a KNN-based approach applied to a sample of hourly ADMS-Urban simulations. This method enhances the representation of industrial plumes and the temporal variability in traffic emissions while maintaining computational efficiency, making it suitable for the operational production of hourly air quality maps in the Hauts-de-France region (France). Full article

In an active magnetic bearing (AMB) rotor system, the mass imbalance of the rotor is inevitable due to uneven materials, machining errors, assembly errors and other factors. When the rotor rotates, the unbalanced mass generates centrifugal force at the same frequency as the rotational speed, which causes vibration and affects the smooth operation of the rotor. Aiming at the mass imbalance of AMB rotor, a new method based on an adaptive notch filter (ANF) and the influence coefficient method (ICM) is proposed. Firstly, the improved ANF is used to track the rotor displacement signal, and the amplitude and phase information of the displacement signal are calculated. Then, according to the amplitude and phase information calculated by ANF, the ICM is used to calculate the counterweight information of the rotor dynamic balance, which includes the counterweight mass and counterweight position. Finally, the dynamic balance correction of the AMB rotor is realized by adding the calculated counterweight mass to both sides of the rotor. This paper validates the feasibility of the proposed method for the dynamic balance correction of the AMB rotor through simulation and experiment. The four radial displacement unbalances of the rotor were reduced by 56.6%, 62.8%, 49.2% and 63.7%, respectively. Full article

The Alternating Direction Method of Multipliers (ADMM) has proven to be an efficient approach for implementing linear programming (LP) decoding of low-density parity-check (LDPC) codes. By introducing penalty terms into the LP decoding model’s objective function, ADMM-based variable node penalized decoding effectively mitigates non-integral solutions, thereby improving frame error rate (FER) performance, especially in the low signal-to-noise ratio (SNR) region. In this paper, we leverage the ADMM framework to derive explicit iterative steps for solving the LP decoding problem for LDPC codes with penalty functions. To further enhance decoding efficiency and accuracy, We propose an LDPC code decoding algorithm based on the symmetric ADMM (S-ADMM). We also establish some contraction properties satisfied by the iterative sequence of the algorithm. Through simulation experiments, we evaluate the proposed S-ADMM decoder using three standard LDPC codes and three representative fifth-generation (5G) codes. The results show that the S-ADMM decoder consistently outperforms conventional ADMM penalized decoders, offering significant improvements in decoding performance. Full article

The high-temperature rutting performance of asphalt mixtures is strongly dependent on the aggregate skeleton and particle movement under loading. Such mechanisms were addressed in the present study by a combined experimental and simulation approach based on the triaxial strength test. A single type of asphalt with two different aggregate gradations (dense and gap) was incorporated to highlight the role of gradation in resisting shear dilation. The simulation was carried out by coupling the discrete and finite element methods considering the realistic three-dimensional aggregate shapes and gradations as well as the flexible boundary prescribed by latex membranes as routinely employed in triaxial testing. In order to represent contact failure-induced cracks within the virtual specimens, the linear parallel bond model was mixed with the Burgers or linear model through random distribution at contacts involving the mortar units. Model verification was achieved by comparing the resulting stress–strain data against those from the laboratory. The calibrated model provided a platform for systematic investigation from the perspectives of particle movement, crack development and distribution, and interparticle contacts. The results showed that the gap-graded mixture yielded lower triaxial strengths and yet softened at a lower rate and exhibited smaller volumetric expansion in the post-peak region. A faster loss of internal cohesion was inferred in the dense-graded mixture based on the higher accumulation rate of cracks that were concentrated at the middle height towards the perimeter of the virtual specimen. Contact analysis indicated that aggregate skeleton was more influential in the strength and stability of gap-graded mixtures. Full article

Background: Hypertension is a major risk factor for cerebrovascular diseases due to its damaging effects on the blood–brain barrier (BBB) and associated pathologies. Oxidative stress-induced endothelial damage plays a critical role in BBB disruption, potentially leading to cognitive impairment and neurodegeneration. In this study, we investigated the protective effects of two essential trace elements, zinc (Zn) and selenium (Se), against oxidative stress in human brain endothelial cells (HBCE5i) exposed to hypertensive shear stress. Using an innovative millifluidic system (LiveBox2), which allows for the precise simulation of continuous flow conditions, we replicated the hemodynamic forces associated with hypertension. Methods: Cells were treated with ZnCl₂ (5–50 µM) or Na₂SeO₃ (50–500 nM) at concentrations selected based on previous studies and confirmed by cytotoxicity assays. Results: Our results demonstrated that shear stress significantly altered the localization of the tight junction protein zonula occludens-1 (ZO-1) and induced the nuclear translocation of the transcription factor NRF2, a hallmark of oxidative stress. Importantly, treatment with 10 µM ZnCl₂ preserved ZO-1 membrane localization and prevented NRF2 translocation, as confirmed by quantitative image analysis. In contrast, Na₂SeO₃ did not provide comparable protection, although modest improvements in ZO-1 localization were observed in some replicates. Discussion: We discuss potential reasons for selenium’s limited efficacy, including differences in bioavailability and cellular uptake. Our findings underscore zinc’s promising role as a neurovascular protector and suggest that further investigation into more complex in vitro models and in vivo studies is warranted. Full article

Extreme flood occurrences are becoming increasingly common due to global climate change, with coastal cities being more vulnerable to compound flood disasters. In addition to excessive precipitation and upstream river discharge, storm surge can complicate the flood disaster process and increase the hazard of urban flooding. This study proposed an integrated trivariate-dimensional statistical and hydrodynamic modeling approach for assessing the compound flood hazard associated with extreme storm surges, precipitation events, and upstream river discharge. An innovative trivariate copula joint modeling and the frequency amplification method were used to simulate designed values under different return periods (RPs), considering the correlation of the three factors. The results show remarkable differences between the inundated areas of flood events with trivariate drivers and a single driver under the same RPs, indicating that univariate frequency values are insufficient to manage flood threats in compound flood events. The proposed method provides guidelines for comprehending the compound flood process and designing flood control projects in coastal cities. Full article

Medium- to long-term wind power output scenarios are crucial for power system planning and operational simulations. This paper proposes a two-stage hidden Markov model-based approach for modeling the time series output of multiple wind farms. First, based on the key features of the wind power output sequence, the daily typical patterns of wind power output are extracted. Then, the process of simulating the wind power output time-series is modeled as a two-layer temporal model. The upper layer uses a discrete hidden Markov model to describe the day-to-day transition process of wind power output patterns and the lower layer uses a Gaussian mixture hidden Markov model to describe the fluctuation process of wind power output values within each output pattern. Finally, the upper models corresponding to each quarter and the lower models corresponding to each pattern are trained respectively and the time-series scenarios of wind power output for multiple wind farms are generated quarter-by-quarter and day-by-day through Monte Carlo sampling. Validation using real-world wind power data demonstrates that the proposed method can effectively generate medium- to long-term output scenarios for multiple wind farms. Compared to traditional methods, the proposed method shows improvements in terms of accuracy, statistical characteristics, temporal correlation, and mutual correlation. Full article

Malaria is a persistent public health problem, particularly in sub-Saharan Africa where its transmission is intricately linked to climatic factors. Climate change threatens malaria elimination efforts in limited resource settings, such as in the Mberengwa district. However, the role of climate change in malaria transmission and management has not been adequately quantified to inform interventions. This protocol employs a multi-method quantitative study design in four steps, starting with a scoping review of the literature, followed by a multi-method quantitative approach using geospatial analysis, a quantitative survey, and the development of a predictive Susceptible-Exposed-Infected-Recovered-Susceptible-Geographic Information System model to explore the link between climate change and malaria transmission in the Mberengwa district. Geospatial overlay, Getis–Ord Gi* spatial autocorrelation, and spatial linear regression will be applied to climate (temperature, rainfall, and humidity), environmental (Land Use–Land Cover, elevations, proximity to water bodies, and Normalised Difference Vegetation Index), and socio-economic (Poverty Levels and Population Density) data to provide a comprehensive understanding of the spatial distribution of malaria in Mberengwa District. The predictive model will utilise historical data from two decades (2003–2023) to simulate near- and mid-century malaria transmission patterns. The findings of this study will be used to inform policies and optimise the management of malaria in the context of climate change. Full article

The paper presents two approaches to generating load cycles for electrical energy storage systems. A load cycle is described as the operation of an energy storage system. The cycles can include different metrics depending on the storage application. Load cycle analysis using the rainflow counting method is employed to understand and validate the metrics of the load cycles generated. Current load cycle generation can involve clustering methods, random microtrip methods, and machine learning techniques. The study includes a random microtrip method that utilises the Random Pulse Method (RPM) and enhances it to develop an improved version called the Gradient Random Pulse Method (gradRPM), which includes the control of stress factors such as the gradient of the state of charge (SOC). This method is relatively simple but, in many cases, it fulfills its purpose. Another more sophisticated method to control stress factors has been proposed, namely the Information Maximising-Recurrent Conditional Generative Adversarial Network (Info-RCGAN). It uses a deep learning algorithm to follow a machine learning-based, data-driven load cycle generation approach. Both approaches use the measurement dataset of a BMW i3 over multiple years to generate new synthetic load cycles. After generating the load cycles using both approaches, they are applied in a laboratory environment to evaluate the stress factors and validate how similar the synthetic data are to a real measurement. The results provide insights into generating simulation or testing data for electrical energy storage applications. Full article

This study investigates the mechanical behavior and failure mechanisms of SFHCPs under low-velocity impact and compression after impact (CAI) conditions. Symmetric foam-filled hat-stiffened composite panels (SFHCPs) are widely used in critical load-bearing structures such as vessels and aircraft due to their high strength-to-weight ratio and integrated stiffener design. However, due to the material’s high sensitivity to impact, it is necessary to conduct a systematic evaluation of its application reliability. By integrating experimental testing and numerical simulation, the buckling modes characterized by symmetry and envelope number were adopted as key performance indicators. The integration of an optical buckling measurement method with iterative finite element model (FEM) updates significantly enhances model accuracy and computational efficiency. Experimental results indicate that for specimens impacted at the mid-section of the stiffener the residual compressive strength drops sharply from 106 kN to 40.6 kN (a reduction of 61.7%), with the buckling mode changing from a symmetric four-wave pattern in the undamaged state to localized buckling in the impact region, leading to brittle failure. The integration of FPP data improved the accuracy of the FEM, highlighting the critical influence of the symmetry of the buckling mode in optimizing impact-resistant composite structures. Full article