Dynamic Parameters Research Articles

Active control of cavity buffeting noise using a DBD plasma model based on interval uncertainty correction

This study established a corrected system for a dielectric barrier discharge plasma actuator (DBD-PA) model and applied the corrected plasma model in research on the control of vehicle buffeting noise. Firstly, experiments on static ion wind characteristics under different excitation voltages were conducted, and the spatial distribution of the body force vector field during actuator operation was estimated. Subsequently, on the basis of experimental index parameters, an interval uncertainty multi-objective optimization (IUMOO) algorithm was used to reconstruct the expression of charge density and the action area boundary of the core electric field force, making them consistent with experimental results. A DBD-PA was arranged upstream of the cavity opening in the reverse direction, and experimental and numerical simulation results showed that noise reduction by this control scheme exceeded 3.67 dB at the characteristic wind speed. Finally, on the basis of the corrected plasma model, the correlation between vortex impact and pressure feedback in the buffeting phenomenon was analysed, and the influence of shear layer dynamic flow parameters on cavity buffeting noise was quantified. Results indicate that the DBD-PA acts to control the flow evolution of vortex cores and pressure clusters, suppress vortex reconnection, and weaken the intensity of vortex impact, thereby reducing wind buffeting noise.

Neutronic design and analysis of the accident-tolerant fuel (ATF) application to VVER-1000 nuclear reactor as well as evaluation of dynamic parameters

Neutronic design and analysis of the accident-tolerant fuel (ATF) application to VVER-1000 nuclear reactor as well as evaluation of dynamic parameters

Time-Varying Spatial Propagation of Brain Networks in fMRI Data.

Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel-wise network-specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated with blood oxygenation level-dependent (BOLD) signal for 6-8 s, whereas the visual network propagates much quicker. We also show that summaries of network-specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between patients with schizophrenia and controls within four large-scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia.

The outer structure of old star clusters in the Small Magellanic Cloud

Abstract We report results on the internal dynamical evolution of old star clusters located in the outer regions of the Small Magellanic Cloud (SMC). Because the SMC has been imprinted with evidence of tidal interaction with the Large Magellanic Cloud (LMC), we investigated at what extend such an interaction has produced extra tidal structures or excess of stars beyond the clusters’ tidal radii. For that purpose, we used the Survey of the Magellanic Stellar History (SMASH) DR2 data sets to build number density radial profiles of suitable star clusters, and derived their structural and internal dynamics parameters. The analysed stellar density profiles do not show any evidence of tidal effects caused by the LMC. On the contrary, the Jacobi volume of the selected SMC star clusters would seem underfilled, with a clear trend toward a smaller percentage of underfilled volume as their deprojected distance to the SMC centre increases. Moreover, the internal dynamical evolution of SMC star clusters would seem to be influenced by the SMC gravitational field, being star clusters located closer to the SMC centre in a more advanced evolutionary stage. We compared the internal dynamical evolution of SMC old star clusters with those of LMC and Milky Way globular clusters, and found that Milky Way globular clusters have dynamical evolutionary paths similar to LMC/SMC old star clusters located closer to their respective galaxy’s centres. Finally, we speculate with the possibility that globular clusters belonging to Magellanic Clouds like-mass galaxies have lived a couple of times their median relaxation times.

Dynamic structural characteristics and vibration susceptibility of Tongren loess under the influence of water content and confining pressure

Loess is extensively developed on both sides of the Longwu River, a tributary of the Yellow River, Tongren County, Qinghai Province. The engineering geological characteristics are complex, and landslide disasters are highly developed. Based on field geological surveys and physical property analysis of the loess in this area, this study analyzes the influence of water content, consolidation pressure, and soil disturbance on the dynamic characteristics of loess using GDS dynamic triaxial tests. The results show that Tongren loess has strong structural properties. Its dynamic constitutive relationship conforms to the Hardin-Dinevich hyperbolic model, where the parameters a and b decrease with increasing confining pressure and increase with increasing water content. The dynamic cohesion and dynamic friction angle both decrease with increasing water content, with the dynamic cohesion significantly affected, decreasing by 77% ~ 83% when saturated. However, the dynamic friction angle is less affected by water content changes, decreasing by 18% ~ 25% when saturated. Under dynamic conditions, the dynamic friction angle of Tongren loess is only 12 ~ 16°, making slopes composed of Tongren loess highly susceptible to instability and sliding under strong earthquake conditions. The dynamic structural properties of Tongren loess are significantly influenced by water content, with increasing water content destroying the joint structural strength of intact loess. Before reaching the plastic limit water content, the joint structural strength of intact loess decreases sharply with increasing water content, and then decreases slowly. The frictional structural strength shows an opposite trend. Under the same experimental conditions, the failure dynamic stress, maximum dynamic elastic modulus, and dynamic shear strength parameters of intact loess are generally greater than those of remolded loess, and the differences between them decrease with increasing water content. This study provides insights into the dynamic structural characteristics of Tongren loess, which can serve as a reference for understanding the formation mechanisms, stability analysis, and seismic design of loess landslides in the region.

Experimental–Analytical Method for Determining the Dynamic Coefficients of Turning Tools

The article presents an analytical and experimental method for determining the dynamic coefficients of cutting tools, with particular emphasis on turning tools. The method involves aligning the acceleration profile obtained from empirical investigations with a mathematical model describing the oscillations of the cutting tool tip. The stiffness (k) and damping (c) coefficients determined using this approach enable the design of tools with desired dynamic characteristics, tailored to specific machining processes, such as machining with long overhangs. From the perspective of mechanical dynamics, selecting appropriate stiffness and damping values allows for the design of tools with optimal dynamic properties. High stiffness reduces the occurrence of deformations under external forces, while adequate damping facilitates the rapid attenuation of vibrations, thereby minimising their adverse effects on the machining process. The developed method could serve as a practical tool for identifying the dynamic parameters applicable to a wide variety of cutting tools. The analysis includes three types of turning tools: one with a steel shank, another with a carbide-core steel shank, and a third with a carbon fibre-core steel shank. The results of the tests indicate that the E-A20Q SDUCL 11 tool is best suited for operations requiring high stability and minimal vibration, owing to its favourable damping and stiffness properties.

Sea Clutter Suppression Method Based on Ocean Dynamics Using the WRF Model

Sea clutter introduces a significant amount of non-target reflections in the echo signals received by radar, complicating target detection and identification. To address the challenge of existing filter parameters being unable to adapt in real-time to the characteristics of sea clutter, this paper integrates ocean numerical models into the sea clutter spectrum estimation. By adjusting filter parameters based on the spectral characteristics of sea clutter, the accurate suppression of sea clutter is achieved. In this paper, the Weather Research and Forecasting (WRF) model is employed to simulate the ocean dynamic parameters within the radar detection area. Hydrological data are utilized to calibrate the parameterization scheme of the WRF model. Based on the simulated ocean dynamic parameters, empirical formulas are used to calculate the sea clutter spectrum. The filter coefficients are updated in real-time using the sea clutter spectral parameters, enabling precise suppression of sea clutter. The suppression algorithm is validated using X-band radar-measured sea clutter data, demonstrating an improvement factor of 17.22 after sea clutter suppression.

Research on shale dynamic and static elastic modulus and anisotropy based on pressurization history

The dynamic and static elastic parameters of rocks exhibit differences. It is of great practical significance to carry out experiments on dynamic and static elastic parameters of rocks under reservoir conditions and determine the conversion relationship between dynamic and static elastic parameters. In this study, shale oil samples from the second member of Kongdong sag in Dagang Oilfield were analyzed by triaxial compression experiments at different bedding angles and longitudinal and shear wave velocity tests. Dynamic and static stiffness coefficient, elastic modulus and acoustic wave velocity change under different directions of pressure and pressure relief. The results indicate that the P-wave velocity, fast shear wave velocity, slow shear wave velocity, dynamic and static Young’s modulus exhibit an increase as the confining pressure rises, and the parameters are greater during the unloading process than during loading process. At identical confining pressures, the dynamic Young’s modulus measured by cores with parallel bedding plane is greater than that measured by cores with vertical bedding plane. The dynamic and static elastic mechanical parameters of different bedding angles can be transformed under varying pressures, and the dynamic elastic mechanical parameters measured under varying levels of confining pressure can be transformed into static elastic mechanical parameters under equivalent confining pressures, which offer fundamental parameters for examining rock mechanics properties and serving as a reference for developing fracturing construction plans for oil and gas reservoirs.

Global sensitivity analysis of high-speed train dynamics system with high-dimensional inputs and multiple outputs

A high-speed train dynamics system involves numerous uncertainty parameters, and there are multiple evaluation indices for its dynamic performance. In this paper, we conduct high-dimensional and multi-output global sensitivity analysis for the dynamic performance of high-speed trains by comprehensively considering these dynamics parameters and indices. Firstly, the dynamics simulation model of the high-speed train is established and six dynamics indices for evaluating the safety and comfort of the train are obtained under operating conditions. Subsequently, considering the multi-output and high-dimensional characteristics of train sensitivity analysis, we propose a multi-output global sensitivity analysis method based on the correlation-analysis-guided Kriging model (CA-K). Finally, global sensitivity analysis of 96 uncertainty parameters on six dynamics indices is conducted under three scenarios utilising the proposed method. The results show that (1) CA-K achieves higher accuracy in approximating high-dimensional train dynamics indices than existing conventional surrogate models. (2) The multi-output global sensitivity analysis method can simultaneously identify all important dynamics parameters and exclude unimportant parameters.

AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization

This research paper presents an advanced AI-driven hybrid power quality management system for electrical railways that addresses critical challenges in 25 kV AC traction networks through a novel integration of single-phase PV-UPQC with ANN-Lyapunov control architecture. The system effectively manages voltage unbalance exceeding 2%, high THD, voltage variations of ± 10%, and poor power factor through a dual-approach methodology combining ANN-based reference signal generation with Lyapunov optimization, enabling dynamic parameter tuning and real-time load adaptation. MATLAB/Simulink simulations validate the system’s superior performance, demonstrating significant improvements, including voltage unbalance reduction from 1.5 to 0.8%, THD reduction below 1%, unity power factor correction, 40% faster dynamic response, and DC link voltage regulation within ± 2%, while maintaining 95% overall system efficiency. Integrating ANN-based shunt and series APF control, Lyapunov optimization, and PV integration establishes a robust framework for enhanced energy efficiency and power quality management in modern railway systems.

Development of a Simplified Human Body Model for Movement Simulations

The main goal of this paper is to develop a simplified model of the human body motion system that enables its application for movement simulations and the analysis of the kinematic and dynamic parameters occurring during the performance of activities. The model is established on the basis of the modified Hanavan model and consists of rigid solids with simple geometry that are connected mostly with spherical joints. Based on anthropometric data from the literature, a complete set of equations parameterizing the dimensions and mass of each segment was formulated. The equations depend on only two body measurements (height and mass). The model is built in the Adams system as a 3D numerical dynamic model and tested using data gathered with an IMU sensors system. A volunteer lifting an object with a bent spine from the ground with both hands is used for this purpose. Three angles (from the IMUs) are applied to each model’s joint to best simulate human movement and to analyze the angular displacements, velocities, and torques. These results are consistent with theoretical expectations and assumptions, thus proving that reproducing human movements with the developed model is possible and that it also allows various parameters of the human body to be obtained.

The Effect of Flexible Flatfoot on the Running Function in School-Age Children.

Flexible flatfoot is common among school-age children and significantly affects walking efficiency, balance stability, and joint-movement coordination in children. The demands on the skeletal structure and muscle function are increased during running; however, the impact of a flexible flatfoot on children's running capabilities is unclear. In this study, we aimed to investigate the effects of flexible flatfoot on the running function of school-age children. Participants with flat feet (n = 28) and typical feet (n = 27) ran on a flat surface at their chosen maximum pace. At the same time, the kinematic and dynamic parameters of their lower limb joints were monitored. A two-sample statistical analysis assessed the differences in the lower limbs' three-dimensional kinematic and dynamic parameters during running. The findings revealed a significant reduction in running velocity, stride length, and frequency, and an increased proportion in the support phase (p < 0.05) in children with flexible flat feet. The navicular drop time decreased, whereas the dynamic navicular drop height increased (p < 0.05). A notable decrease in the maximum plantar flexion and eversion torque, power, and power absorption of the ankle joint was observed (p < 0.01). Furthermore, the maximum flexion torque of the knee and hip joints and hip joint power absorption decreased (p < 0.05). The peak ground reaction force in the anteroposterior directions was reduced (p < 0.01). These results indicate that flexible flatfoot can impair the running efficiency of school-age children and lead to diminished motor stability and reduced propulsive and braking capabilities.

Investigation on the mechanical responses of shallow coral reef limestones under dynamic loads

Coral reef limestone (CRL) is found only in ocean reef islands. Deepening the investigation of the dynamic performances of CRL helps expand its utilization in reef engineering. This study studied the dynamic response of shallow weakly-cemented CRL (WCRL) using experimental and numerical methods. First, the impact performance of the WCRL was tested using the Split Hopkinson Pressure Bar apparatus. The dynamic peak stress and peak strain of WCRL exhibited rate-sensitivity. Subsequently, the impact process of the WCRL was calculated in the numerical software LS_DYNA, with key dynamic parameters of WCRL calibrated based on the Holmquist-Johnson-Cook (HJC) model for the first time. The accuracy of the numerical results was verified with the experimental results. Thereafter, the dynamic failure process and energy dissipation mechanism of the WCRL were numerically investigated. As the strain rate increased, the failure of the WCRL specimens intensified. The energy analysis demonstrated that the destruction of WCRL specimens consumed more energy at high strain rates, though the distribution proportion of energy was rate-independent. Finally, the sensitivity analysis revealed that the certain parameters (A, B, C, N, p c, and μc) of the HJC model played a crucial role in describing the material’s dynamic response.

Monitoring Sea Surface Temperature and Sea Surface Salinity Around the Maltese Islands Using Sentinel-2 Imagery and the Random Forest Algorithm

Marine regions are undergoing rapid evolution, primarily driven by natural and anthropogenic activities. Safeguarding these ecosystems necessitates the ability to observe their physical features and control processes with precision in both space and time. This demands the acquisition of precise and up-to-date information regarding several marine parameters. Thus, to gain a comprehensive understanding of these ecosystems, this study employs remote sensing techniques, Machine Learning algorithms and traditional in situ approaches. Together, these serve as valuable tools to help comprehend the distinctive parametric characteristics and mechanisms occurring within these regions of the Maltese archipelago. An empirical workflow was implemented to predict the spatial and temporal variations in sea surface salinity and sea surface temperature from 2022 to 2024. This was achieved by leveraging Sentinel-2 satellite platforms, the random forest Machine Learning algorithm, and in situ data collected from sea gliders and floats. Subsequently, the numerical data generated by the random forest algorithm were validated with different error metrics and converted into visual representations to illustrate the sea surface salinity and sea surface temperature variations across the Maltese Islands. The random forest algorithm demonstrated strong performance in predicting sea surface salinity and sea surface temperature, indicating its capability to handle dynamic parameters effectively. Additionally, the parametric maps generated for all three years provided a clear understanding of both the spatial and temporal changes for these two parameters.

Quantifying spatial and dynamic lung abnormalities with 3D PREFUL FLORET UTE imaging: A feasibility study.

Pulmonary MRI faces challenges due to low proton density, rapid transverse magnetization decay, and cardiac and respiratory motion. The fermat-looped orthogonally encoded trajectories (FLORET) sequence addresses these issues with high sampling efficiency, strong signal, and motion robustness, but has not yet been applied to phase-resolved functional lung (PREFUL) MRI-a contrast-free method for assessing pulmonary ventilation during free breathing. This study aims to develop a reconstruction pipeline for FLORET UTE, enhancing spatial resolution for three-dimensional (3D) PREFUL ventilation analysis. The FLORET sequence was used to continuously acquire data over 7 ± 2 min in 36 participants, including healthy subjects (N = 7) and patients with various pulmonary conditions (N = 29). Data were reconstructed into respiratory images using motion-compensated low-rank reconstruction, and a 3D PREFUL algorithm was adapted to quantify static and dynamic ventilation surrogates. Image sharpness and signal-to-noise ratio were evaluated across different motion states. PREFUL ventilation metrics were compared with static 129Xe ventilation MRI. Optimal image sharpness and accurate ventilation dynamics were achieved using 24 respiratory bins, leading to their use in the study. A strong correlation was found between 3D PREFUL FLORET UTE ventilation defect percentages (VDPs) and 129Xe VDPs (r ≥ 0.61, p < 0.0001), although PREFUL FLORET static VDPs were significantly higher (mean bias = -10.1%, p < 0.0001). In diseased patients, dynamic ventilation parameters showed greater heterogeneity and better alignment with 129Xe VDPs. The proposed reconstruction pipeline for FLORET UTE MRI offers improved spatial resolution and strong correlation with 129Xe MRI, enabling dynamic ventilation quantification that may reveal airflow abnormalities in lung disease.

Application of simulated annealing algorithm in multi-objective cooperative scheduling of load and storage of source network for load side of new power system

To improve the adaptability of grid load collaborative scheduling, a multi-objective collaborative scheduling method based on a simulated annealing algorithm for the load storage of grid loads on the load side of a new power system is proposed. Local bus transmission technology is adopted to collect the dynamic parameters of energy network load energy storage on the load side of the new power system. The collected load dynamic parameters are fused with energy distribution state parameters to extract the state characteristics of energy network load storage. The simulated annealing algorithm is adopted to realize the load characteristics fusion and adaptive scheduling processing of energy network on the load side of the power system, and the spectral characteristics of the load dynamic parameters are extracted. The dynamic scheduling method of simulated annealing is used to realize the multi-objective optimization of dynamic load of energy network. Based on the co-optimization results of simulated annealing, the optimization application of the simulated annealing algorithm in the multi-objective co-scheduling of loads and energy storage in a new power system is realized. The experimental results show that after 400 iterations, the control convergence accuracy of the proposed method reaches 0.980, which is significantly better than that of the comparison method, and performs well in terms of scheduling efficiency improvement, load scheduling stability, scheduling time and energy waste ratio, proving that the method has good multi-objective integration and strong optimization ability in the scheduling process, and improves the load balanced scheduling and adaptive control ability of the power system.

A State Estimation of Dynamic Parameters of Electric Drive Articulated Vehicles Based on the Forgetting Factor of Unscented Kalman Filter with Singular Value Decomposition

In this paper, a state estimation method of distributed electric drive articulated vehicle dynamics parameters based on the forgetting factor unscented Kalman filter with singular value decomposition (SVD-UKF) is proposed. The 7DOF nonlinear dynamics model of a distributed electric drive articulated vehicle is established. The unscented Kalman filter algorithm is the foundation, with singular value decomposition replacing the Cholesky decomposition. A forgetting factor is introduced to dynamically adapt the observation noise covariance matrix in real time, resulting in a centralized parameter state estimator for the articulated vehicle. The proposed parameter state estimation method based on the forgetting factor SVD-UKF is simulated and compared with the unscented Kalman filter (UKF) estimation method. Key dynamic parameters are estimated, such as the lateral and longitudinal velocities and accelerations, angular velocity, articulated angle, wheel speeds, and longitudinal and lateral tire forces of both the front and rear vehicle bodies. The results show that the proposed forgetting factor SVD-UKF method outperforms the traditional UKF method. Furthermore, a prototype vehicle test is conducted to compare the estimated values of various dynamic parameters with the actual values, demonstrating minimal errors. This verifies the effectiveness of the proposed dynamic parameter estimation method for articulated vehicles.

Global long-term trends in the total electron content

Abstract. Total electron content (TEC) is an important parameter for ionospheric dynamics, global navigation satellite system/Global Positioning System (GNSS/GPS) signal propagation and related applications of GNSS/GPS signals. Despite this fact, the long-term trends in TEC have been studied only a little. Here, we analyze the JPL-35 (Jet Propulsion Laboratory-35) homogeneous series of global TEC data for 1994–2014 for selection of the optimum solar activity proxy for TEC analyses and the UPC (Universitat Politècnica de Catalunya) TEC data over 2003–2023 for estimating long-term trends in TEC. TEC trends are predominantly negative. TEC trends reveal a clear wavenumber 2 longitudinal structure in low/equatorial latitudes with strong negative trends in belts 0–60 and 180–240° E and weak trends in 90–150 and 270–330° E. For more detailed information on TEC trends, a longer series of reliable TEC data is required.

Experimental Study on the Photothermal Properties of Thermochromic Glass

Reducing energy consumption in buildings is critical to reducing CO2 emissions and mitigating global warming. Studies have shown that heating and cooling loads account for more than 40% of building energy consumption, and thermochromic glass (TCG) with dynamically adjustable solar transmittance is an excellent way to reduce this load. Although a large number of studies have tested the spectral parameters of TCG in totally transparent and totally turbid states, the impact of dynamic changes in optical properties on the simulation accuracy of building energy consumption has been neglected. In this study, a method is proposed for a hydrogel-type TCG to dynamically test its spectral parameters based on spectrophotometry. The method uses a spectrophotometer and a PID heater to achieve the dynamic optical parameter testing of TCGs at different temperatures. In this paper, the transmission and reflection spectra of the two TCGs at 20~25 °C, 30~35 °C, 40 °C, 45 °C, 50 °C, and 55 °C were obtained, and the regression segmentation functions of visible transmittance and solar transmittance were established. The R2 of the function model is 0.99. In addition, the test results show that the thermochromic glass selected in this paper can selectively transmit different wavelengths of light, and its transmission mainly occurs in the visible and near-infrared wavelengths from 320 to 1420 nm, while the transmission rate of other wavelengths is very low. As the temperature increases, the visible, solar, and ultraviolet transmittances decrease at a similar rate. In addition, the higher the temperature acting on the thermochromic (TC) layer, the greater its haze.

Framework Using Multicriteria Analysis for Evaluating the Risk of Musculoskeletal Disorders.

This study includes musculoskeletal disorder (MSD) risk evaluation based on the IMU sensor data gathered from patient-lifting movement performed by healthcare specialists. This is a continuation of previous research focusing on a novel multicriteria statistical model integrating experimental and large-scale statistical datasets. The proposed model estimates MSD probabilities over 5, 10, and 15 years for the neck (0.537 ± 0.156), shoulder (0.449 ± 0.084), and elbows (0.277 ± 0.221). The model enables individual risk profiling, influenced by dynamic parameters that can reduce the long-term risk by up to 70.49%. The model is in its early development stages, i.e., it is the proof of concept that offers a new approach to assessing MSD risk at work using motion tracking data in combination with statistics. Further studies with larger sample sizes and validated criterion weights are needed to refine and validate this approach.