Dynamic Characteristics Research Articles

Spatio-temporal dynamic evolution characteristics and driving mechanism analysis of urban pseudo human settlements

Spatio-temporal dynamic evolution characteristics and driving mechanism analysis of urban pseudo human settlements

On the study of double dispersive equation in the Murnaghan’s rod: Dynamics of diversity wave structures

This article secures the various wave structures of the fractional double dispersive equation, a significant nonlinear equation that describes the propagation of nonlinear waves within the elastic, uniform, and inhomogeneous Murnaghan’s rod. The model under discussion has a wide range of applications in science and engineering. Two recently developed analytical techniques known as the improved generalized Riccati equation mapping method and the multivariate generalized exponential rational integral function method have been applied to the proposed equation for the first time. A variety of solutions have been revealed such that dark, singular, bright-dark, bright, complex, and combined solitons. Furthermore, we include a diverse array of plots that illustrate the physical interpretation of the obtained solutions in relation to a number of significant parameters, thereby highlighting the impact of fractional derivatives. Within the context of the proposed model, these visualizations give a clear understanding of the behavior and characteristics of the solutions. This study’s results have the potential to enhance comprehension of the nonlinear dynamic characteristics exhibited by the specified system and validate the efficacy of the implemented techniques. The achieved results significantly enhance our understanding of nonlinear science and the nonlinear wave fields associated with more complex nonlinear models.

Modeling Approach of Cloud 4D Printing Service Composition Optimization Based on Non-Dominated Sorting Genetic Algorithm III

The manufacturing industry is currently experiencing a paradigm shift from traditional centralized systems to distributed, personalized, and cloud-based intelligent manufacturing ecosystems. The advent of 4-dimensional (4D) printing technology introduces dynamic characteristics to manufacturing design and functionality, necessitating the effective management of these emergent 4D printing services. This study aims to bridge the gap between the static nature of existing cloud manufacturing services and the dynamic requirements imposed by 4D printing technology. We propose a comprehensive multiobjective optimization model for cloud-based 4D printing service portfolios, incorporating the intricate complexities of 4D printing services and assessing the efficacy of the Non-Dominated Sorting Genetic Algorithm III (NSGA III) in optimizing these service portfolios to meet dynamic demands. In this research, the NSGA III algorithm is employed to develop a multiobjective optimization framework for 4D printing service portfolios, addressing critical issues such as service cost, time, quality, adaptability, and overall service optimization amidst fluctuating demand and service availability. The findings indicate that the NSGA III algorithm demonstrates superior performance in terms of generational distance (GD) and inverted generational distance (IGD), particularly excelling in convergence and diversity for high-dimensional optimization problems when compared to the comparison algorithms. The study concludes that the NSGA III algorithm exhibits significant potential in optimizing the orchestration of cloud-based 4D printing service portfolios, underscoring its effectiveness in managing the complexities associated with these services. This research provides valuable insights for the advancement of intelligent cloud-based 4D printing systems, paving the way for future developments in this field.

Comparative analysis of seismic response and vulnerability of laminated bamboo frame structure under near-field and far-field earthquake actions

The study offers an evaluation of seismic response and vulnerability for a five-story laminated bamboo frame structure, assessing the effectiveness of seismic intensity measures across various damage thresholds and the modeling uncertainties involved. A structural finite element model was developed in OpenSees software, utilizing the Pinching4 model to accurately represent the non-linear characteristics of T-shaped steel connections in beam-column joints. Two collections of earthquake records, one consisting of pulse-like near-field and the other of far-field events, were utilized to examine the structure's nonlinear behavior. Additionally, vulnerability curves for various damage states were generated using the multiple stripe analysis technique, applied to both ground motion datasets. The applicability of 24 seismic intensity measures was also analyzed across various damage limit states. The modeling uncertainty under different seismic actions was further estimated using a first-order second-moment method. Ultimately, the annual collapse probability of the structure are elucidated. Research indicates that due to the influence of joint stiffness, the overall structure demonstrates dynamic characteristics that are predominantly of the first mode shape. The spectral acceleration associated with the fundamental period achieves a reduced logarithmic standard deviation in the vulnerability analysis across various limit states. Other IMs considering higher mode effects or softening period actions no longer predominate. In most cases, the modeling uncertainty under far-field seismic action is higher than that under near-field, especially at the severe damage limit states by using an efficient intensity measure. The laminated bamboo frame structure faces a much higher collapse risk in near-field than in far-field conditions.

Nonlinear seismic performance of offshore wind turbines on hybrid pile-bucket foundation in sand: Combined earthquake and wind-wave loads

Offshore wind turbines (OWTs) are gaining prominence worldwide, and the hybrid pile-bucket foundation, which combines a monopole and a bucket, has emerged as a noteworthy development. In this study, a 3-D numerical model for the 5-MW OWT was constructed utilizing the OpenSees platform. The dynamic characteristics of the sand was modeled with the PDMY02 constitutive model and the soil was discretized using brick u-p elements. To investigate the dynamic behavior of the OWT in an actual marine environment, the coupled model was subjected to dynamic loadings, encompassing waves, wind, and earthquake. Two seismic motions with different frequency components were considered, respectively. The study focused on exploring the impacts of key influencing factors on the OWT rotation, tower-top acceleration development and spatiotemporal distribution of excess pore water pressure ratio (EPWPR). These factors include dynamic load combinations, earthquake intensity, soil relative density, wind speed, angle between load directions, and pile length. It is revealed that the inclination angle of offshore wind turbines (OWTs) may exceed the allowable threshold under specific conditions of load combinations, seismic motion inputs, and seabed conditions. Thus, it is suggested to appropriately consider the effects of wind and wave actions in the seismic analysis of OWTS.

Motion Planning for a Legged Robot with Dynamic Characteristics.

Legged soccer robots present a significant challenge in robotics owing to the need for seamless integration of perception, manipulation, and dynamic movement. While existing models often depend on external perception or static techniques, our study aims to develop a robot with dynamic and untethered capabilities. We have introduced a motion planner that allows the robot to excel in dynamic shooting and dribbling. Initially, it identifies and predicts the position of the ball using a rolling model. The robot then pursues the ball, using a novel optimization-based cycle planner, continuously adjusting its gait cycle. This enables the robot to kick without stopping its forward motion near the ball. Each leg is assigned a specific role (stance, swing, pre-kick, or kick), as determined by a gait scheduler. Different leg controllers were used for tailored tiptoe trajectory planning and control. We validated our approach using real-world penalty shot experiments (5 out of 12 successful), cycle adjustment tests (11 out of 12 successful), and dynamic dribbling assessments. The results demonstrate that legged robots can overcome onboard capability limitations and achieve dynamic mobility and manipulation.

Coordinated Control of Differential Drive-Assist Steering and Direct Yaw Moment Control for Distributed-Drive Electric Vehicles

Direct yaw moment control (DYC) and differential drive-assist steering (DDAS) for distributed-drive vehicles are both realized by allocating the in-wheel motor torque. To address the interference caused by overlapping control objectives, this paper proposes a multilayer control strategy that integrates DYC and DDAS, consisting of an upper controller, a coordinated decision layer, and a torque distribution layer. The upper controller, designed based on the vehicle’s dynamic characteristics, incorporates an adaptive fuzzy control DYC system and a dual PID control DDAS system. The coordinated decision layer is developed utilizing a phase-plane dynamic weighting method, delineating region boundaries by applying the double-line and limit cycle methods. The torque distribution strategy is formulated considering motor peak torque and road adhesion conditions. Multi-condition joint simulation experiments indicate that the proposed multilayer control strategy, integrating the advantages of DYC and DDAS, reduces peak steering wheel torque by approximately 10%, peak yaw rate by around 25%, peak sideslip angle by roughly 29%, and peak sideslip angle rate by about 19%, significantly improving driving stability and maneuvering flexibility.

Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China

Abstract Debris flow is one of the most common types of geological disasters in China. Owing to the influence of topography, geomorphology, geological conditions, human activity, and rainfall debris flow disasters frequently occur in the mountainous areas of Beijing. The research on debris flow in the Beijing area focuses on rainfall and risk evaluation, material sources, and early warning and prevention of debris flow. However, there are few studies on the development characteristics of single-gully debris flow and the prediction of hazardous areas in the Beijing area. Therefore, we chose the debris flow of Bangou Village in Yanqing District of Beijing as the research object. We analyzed the recharge conditions in the ditch domain and predicted the extent of the hazardous area around the gully, providing suggestions for control measures. The dynamic reserves of the loose deposits in the debris flow gully, currently in the development stage, were estimated as 15.48 × 104 m3, representing four supply sources: artificial deposits, alluvium and diluvium, residual slope deposits, and collapse. The peak flow is 24.49 m3/s for a 10-year rainfall event, 27.64 m3/s for a 20-year rainfall event, 31.79 m3/s for a 50-year rainfall event, and 34.93 m3/s for a 100-year rainfall event. The total amounts of solids washed out by a debris flow from the preceding events are 0.70 × 104 m3, 0.79 × 104 m3, 0.91 × 104 m3, and 1.00 × 104 m3, respectively. The size of the debris flow is small, with a maximum hazardous area of 0.2810 km2. We conclude that a small debris flow outbreak in the Bangou Village gully is possible. We expect that the results of this study will provide basic information and help improve debris flow research in Beijing.

Road network traffic flow prediction: A personalized federated learning method based on client reputation

Accurate traffic flow prediction can provide effective decision-making support for traffic management, alleviate traffic congestion, and improve road traffic efficiency. Traffic flow data contains personal privacy information, such as vehicle trajectories, driving speed, etc. However, most existing research focuses on using all local data to jointly construct prediction models, facing data security and privacy issues. In response to these challenges, this paper presents a Personalized Federated Learning method based on Client Reputation for traffic flow prediction. This method avoids direct sharing of original data by performing model training and information aggregation locally, thereby better protecting user privacy. We initially constructed the foundational model by integrating Graph Neural Networks (GCNs) and Gated Recurrent Units (GRUs) for federated learning. GCNs are capable of capturing the spatial relationships between nodes in a road network, thereby extracting the spatial features of traffic flow. On the other hand, GRUs has advantages at handling sequential data, effectively capturing the dynamic temporal characteristics of traffic flow. Consequently, by combining GCNs and GRUs, we are able to build a model that captures both the spatial and temporal features of traffic flow. Subsequently, we employed a federated learning framework to train the model. In the training process, we propose two evaluation algorithms to evaluate the reputation of all clients participating in federated learning. Then, different weights are customized for each client through a personalized algorithm based on the client's reputation to improve its personalization. Finally, we validated the method on the License Plate Recognition (LPR) datasets collected in Changsha city, China. The experimental results indicate that the method can achieve good prediction performance and stability while protecting the privacy of all participants.

Spatiotemporal interactive dynamic adaptive adversarial graph convolution network for traffic flow forecasting

Accurate traffic flow forecasting is important for intelligent traffic management and control. To address the inability of existing methods to simultaneously capture the spatiotemporal dependence of traffic flows and the significant trend differences between predicted and real values, a traffic flow forecasting method based on a Spatiotemporal Interactive Dynamic Adaptive Convolutional Network (STIDAAG) is proposed. First, an interactive learning structure is designed to dynamically aggregate the spatiotemporal characteristics of the hidden nodes in the traffic network. Second, a dynamic adaptive graph generation network is designed based on the current and historical state to further capture the dynamic spatiotemporal characteristics. Finally, the adversarial graph convolutional network is used to optimize the loss for adversarial training to reduce the trend difference between the predicted and true values. The results of the experiment on four publicly available datasets indicate that STIDAAG outperforms both typical and advanced methods in terms of predictive performance.

Dynamic response characteristics of optical beam deflection in liquid crystal optical phased array

The beam deflector based on liquid crystal optical phased array (LCOPA) is a crucial component of space laser communication systems. Understanding and mastering the beam deflection characteristics under the dynamic response of LCOPA is essential for achieving real-time acquisition and tracking in space laser communication. This paper thoroughly explores the beam deflection characteristics during the dynamic response process by analyzing the dynamic response and far-field diffraction models of LCOPA. It presents the far-field diffraction patterns under the dynamic response of LCOPA and validates the analysis through experiments. This study not only enhances the understanding of the dynamic performance of LCOPA but also provides a theoretical basis for its control in space laser communication systems.

Effect of powder activated carbon (PAC) addition on the performance of anaerobic dynamic membrane bioreactor (AnDMBR): Stratification analysis of dynamic membrane characteristics

Powdered activated carbon (PAC), proved to be an effective performance enhancer in membrane bioreactor studies, has yet been applied in anaerobic dynamic membrane bioreactors (AnDMBRs). A better understanding of dynamic membrane (DM) characteristics is important for optimizing AnDMBR performance. In this study, three AnDMBRs were operated for synthetic municipal wastewater treatment with PAC dosages of 0, 1, and 3 g/L. Particular efforts were devoted to understanding the DM characteristics in layers. It was found that PAC addition increased COD removal efficiency (10 %) and decreased DM filtration resistance (60 %). The extent of performance enhancement, particularly the decrease of DM filtration resistance, appears to be much more substantial in AnDMBR than aerobic DMBR. PAC addition significantly reduced the fouling potential of suspended sludge and consequently the filtration resistance of DM. Compared with inner DM layer, the outer DM layer exhibited relatively higher fouling characteristics, such as less porous structure, higher EPS content (mainly proteins), hydrophobicity, and relative abundance of biofouling-related bacteria. The positive effect of PAC addition on reducing DM fouling characteristics turned out to be more significant for outer DM layer, mainly because of its higher PAC content associated with the higher amount of small particles.

Geometric optimization of Kenics-type static mixers: numerical analysis of thermal and dynamic performance

Static mixers play an important role in industry by facilitating mixing and heat transfer processes. This study investigates the effect of geometry on the thermal and dynamic performance of a Kenics-type static mixer by examining four different configurations. Numerical simulations using Fluent.20 software were used to analyze parameters such as Reynolds number (ranging from 0.1 to 80), pressure drop, shear rate, fluid temperature, and Nusselt number. The results highlight the critical importance of geometry in fluid dynamics, which affects thermal performance. Among the configurations studied, the third case, characterized by three cavities occupying the entire length of the helix, proved to be the most efficient. This research thus provides valuable insights into the dynamic and thermal characteristics of kinetic static mixers, underscoring the critical importance of geometry in optimizing overall performance. What's more, this performance improvement can be observed at different Reynolds numbers, resulting in improved mixing efficiency and reduced pressure losses. This study shows that the third geometric configuration outperforms the other proposed mixers, with a performance improvement of 99%.

Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint

Abstract Quantitative evaluation of rock dynamic disaster intensity is a challenging and difficult task in the field of earth science. In this article, the dynamic compression tests of granite under different impact velocities are carried out, and the energy evolution characteristics of the dynamic failure process of granite are analyzed. The dynamic fracture characteristics of granite were studied by using computed tomography and section scanning equipment. The results show that the energy dissipation and energy accumulation mechanism of granite under dynamic loading are fundamentally changed with the increase of impact disturbance strength, which leads to the transformation of the failure mode from the tension-type sheet crack under low-speed impact to the complex network crack coupled by tension and shear under high-speed impact. At the same time, the greater the impact velocity, the larger the macroscopic crack density, the more uneven the particle distribution on the fracture section, the greater the roughness, and the higher the rock mass fracture degree. In accordance with the results, the energy distribution ratio is proposed to quantitatively characterize the coupling effect of energy dissipation and energy accumulation in the dynamic failure process of granite and to reflect its dynamic failure intensity. The higher the energy distribution ratio, the stronger the energy coupling effect and the more intense the dynamic failure of the rock mass.

Impact of Diluents on Flame Stability With Blends of Natural Gas and Hydrogen

Abstract Two potential decarbonization pathways for natural gas (NG)-fueled gas turbine engines include blending hydrogen (H2) into NG and postcombustion carbon capture. H2 blending changes several combustion properties, including flame speed and stretch sensitivity. The use of post-combustion carbon capture systems is typically facilitated by the implementation of exhaust gas recirculation (EGR), where exhaust gases are injected into the inlet of the engine, increasing carbon dioxide (CO2) concentration at the outlet and, hence, increasing the efficiency of carbon capture technologies. In this work, we explore the impact of H2 blending and EGR on the stability of a swirl-stabilized, central-piloted flame. Mixtures of NG and H2 are tested at a range of different diluent compositions, with oxygen varied from 21% to 15% by volume in the oxidizer. In all cases, a constant adiabatic flame temperature is maintained to mimic the operation of a gas turbine at a given turbine inlet temperature. A variable-length combustor is used for testing, where combustor length is varied to understand the dynamic stability characteristics of the system. Results show that EGR and H2 work in opposition to each other, where higher levels of EGR result in poor flame holding and higher levels of H2 result in better flame holding. Increasing H2 generally increases the amplitude of thermoacoustic instability at each condition, a result of the change in flame position in this particular combustor. Importantly, H2 can be added to NG to improve flame holding without significantly decreasing CO2 levels in the products, showing that H2 blending can be a method for counteracting combustor operability issues that arise from high levels of EGR necessary to improve the efficiency of typical carbon capture systems.

Nusselt and Reynolds numbers correlation for oscillatory flow of thermoacoustics over heated tube banks

The back-and-forth motion characteristic of the oscillatory flow brings complexity in predicting the heat transfer nature for thermoacoustic wave conditions. Investigating this is crucial as the flow forms the backbone for the thermodynamic cycles of its operation. Often, the steady approximation in calculating heat transfer leads to ambiguity in designing the system. This concern is addressed in this paper via an in-depth analysis of the fluid dynamic behavior and heat transfer characteristics of oscillatory flow within a thermoacoustic framework, spanning experimental works for Reynolds numbers ranging from 300 to 24,000. The investigation focused on a tube bank heat exchanger composed of nine tubes arranged in inline and staggered configurations. Experiments were conducted at constant tube surface temperatures treated at 40 °C and 80 °C, respectively. The distinguishing difference in heat transfer behaviors for oscillatory flow is visualized by the Computational Fluid Dynamics (CFD) models, employing the Reynolds-Averaged Navier Stokes equation with the k-omega turbulence shear stress transport model. The flow never leaves the system, and it cyclically crosses the tubes back and forth with a travel distance that depends on the drive ratio of the flow. The unique nature of the flow forms the foundation for the experimental findings that show a linear relationship between the Nusselt and Reynolds numbers regardless of the configuration of tube banks and tube bank temperature. The Pearson ruler regression analysis was conducted using Matlab R2022b and a Nusselt correlation, Nu = 0.000853RePr⅓, is proposed with a confidence level of 95 %. Notably, the correlation aligns with the constant value known as the Colburn-j factor, with a value of 0.00083. The small Colburn-j value is shown to be the influence of the log-mean temperature difference characteristic of oscillatory flow. It shows a consistent heat transfer process between the heated tubes and surrounding fluid, which is important to sustain the thermoacoustic effect in the system. For future thermoacoustic design, the use of steady heat transfer correlation should be avoided or at least used with caution as the comparative analysis with published works concluded that the Nusselt and Reynolds correlation for steady flow tends to overpredict the heat transfer by two to threefold.

Research on Multiplication Routine Based on Reconfigurable Four-Valued Logic Processor

Despite the indispensable role of traditional electronic computers in modern society, their limitations in parallel processing capabilities, bit-width constraints, and processor bit-width are becoming increasingly apparent, especially when handling large-scale datasets and complex computational tasks. Although hardware technology and algorithm optimization continue to advance, the arithmetic units of traditional computers—adders—remain constrained by carry delay and bit-width limitations. This bottleneck is particularly pronounced in multiplication operations, mainly when adders are used for partial product accumulation. However, since 2018, the emergence of a new type of Reconfigurable Four-Valued Logic Electronic Processor (RFLEP) has provided a potential solution to these traditional limitations. With its large processor bit-width, flexible bit grouping capabilities, and dynamic hardware function reconfiguration features, this processor has brought revolutionary changes to the field of computing. In this context, this paper proposes and implements a Reconfigurable Four-Valued Logic Multiplication Routine (RFLMR) tailored explicitly for the RFLEP. The RFLMR utilizes the Modified Signed-Digit (MSD) representation method in multi-valued logic combined with the M transformation in four-valued logic to generate partial products. These partial products are then efficiently summed in parallel using the JW-MSD parallel adder, achieving the rapid execution of multiplication operations. Experimental results demonstrate that the multiplication routine based on the RFLEP performs multiplication operations accurately and meets theoretical expectations regarding implementation efficiency and performance. This research not only provides new ideas for developing next-generation high-performance computing systems but also paves the way for exploring more efficient and powerful computing models, heralding a profound transformation in future computing technology.

A photovoltaic cell defect detection model capable of topological knowledge extraction

As the global transition towards clean energy accelerates, the demand for the widespread adoption of solar energy continues to rise. However, traditional object detection models prove inadequate for handling photovoltaic cell electroluminescence (EL) images, which are characterized by high levels of noise. To address this challenge, we developed an advanced defect detection model specifically designed for photovoltaic cells, which integrates topological knowledge extraction. Our approach begins with the introduction of a multi-scale dynamic context-based feature extraction method, capable of generating static context by thoroughly capturing the local texture and structural information of multi-scale defects. This static context is then combined with dynamic context to produce fine-grained local features. Subsequently, we developed a centralized feature pyramid structure, enhanced by spatial semantics, which models the explicit visual center. This structure effectively elucidates the relationship between local and global features in defect images, thereby improving the representation of defect characteristics. Finally, we implemented a feature enhancement strategy grounded in spatial semantic knowledge extraction. This strategy uncovers potential correlations among defect targets by constructing a spatial semantic topology of features, mapping these features to a higher-order representation, and ultimately delivering precise defect detection results.

An effective strategy for stacking and reclaiming iron ore piles

ABSTRACT This study examines the impact of the size of homogenised ore stockpiles on the feed rate of concentration-ore plants and the inter-pile grade variability. An integrated simulation optimisation tool is used to incorporate the dynamic characteristics of this process. Applying the discrete event simulation technique in this study made it possible to analyse the losses due to the lack of homogenised piles in the ore yard. Three scenarios from a Brazilian mine were explored. The results report a production gain of 3.85% in the scenario with the smallest ore pile size compared to the scenario with the largest pile size.

Dynamic performance analysis of proton exchange membrane fuel cell in marine applications

To investigate the dynamic characteristics of proton exchange membrane fuel cells (PEMFCs) on maritime vessels, a lumped-parameter model for the PEMFC systems was developed based on a hybrid vessel propulsion systems model. Simulations and analyses of the dynamic characteristics of PEMFCs were conducted under typical sailing conditions. The results reveal a noticeable hysteresis in the operating temperature of PEMFC stacks during vessel voyages, with a delay of about 25 s, leading to significant overvoltage in activation, ohmic, and concentration differences. Significant variations in vessel loads can cause large fluctuations in the component gas pressures in the cathode and anode flow paths within 16.6–19.8 kPa and 78.57–93.06 kPa, respectively. A comparison of different humidification levels for cathode and anode gases demonstrates that, at the same moment, as the humidification of cathode and anode gases increases from 20 % to 100 %, the water content in the proton membrane increases from 2.29 to 13.47, the ohmic impedance decreases from 1.35 mΩ/cm2 to 0.174 mΩ/cm2, and the overshoot of the fuel cell voltage decreases. The output delay decreases from 25 s to 8 s, enhancing overall fuel cell performance. These findings are significant for optimizing the design, performance, and real-time control of marine PEMFC systems.