Complex Dynamic Characteristics Research Articles

Spatiotemporal prediction of solidified dendrites based on convolutional long-short-term neural network

The formation and growth of dendrites during the solidification of metals are critical factors influencing material properties. Accurate prediction of dendritic growth morphology and evolution is of great significance for materials science research and industrial applications. Traditional simulation methods, such as the phase-field method, provide high-precision results but are computationally expensive and time-consuming. Recently, with the advancement of deep learning technology, data-driven approaches have shown great potential in materials science. This paper proposes the use of Convolutional Long-Short-Term Memory Network (ConvLSTM) to capture spatiotemporal dependencies for predicting the spatiotemporal evolution of dendritic solidification in materials. By training on historical data of dendritic growth, the ConvLSTM neural network model predicts the future growth morphology of the dendritic microstructure during solidification. The model accurately forecasts the future morphology and appearance of the microstructure. Case studies were conducted using a generated dataset of dendritic solidification simulations, and the results demonstrate that the ConvLSTM model effectively captures the complex dynamic characteristics of dendritic growth and provides high-precision prediction results. This offers new insights for material design and optimization.

Dynamic Analysis and Approximate Solution of Transient Stability Targeting Fault Process in Power Systems

Modern power systems are high-dimensional, strongly coupled nonlinear systems with complex and diverse dynamic characteristics. The polynomial model of the power system is a key focus in stability research. Therefore, this paper presents a study on the approximate transient stability solution targeting the fault process in power systems. Firstly, based on the inherent sinusoidal coupling characteristics of the power system swing equations, a generalized polynomial matrix description in perturbation form is presented using the Taylor expansion formula. Secondly, considering the staged characteristics of the stability process in power systems, the approximate solutions of the polynomial model during and after the fault are provided using coordinate transformation and regular perturbation techniques. Then, the structural characteristics of the approximate solutions are analyzed, revealing the mathematical basis of the stable motion patterns of the power grid, and a monotonicity rule of the system’s power angle oscillation amplitude is discovered. Finally, the effectiveness of the proposed methods and analyses is further validated by numerical examples of the IEEE 3-machine 9-bus system and IEEE 10-machine 39-bus system.

Improved whale optimization algorithm towards precise state-of-charge estimation of lithium-ion batteries via optimizing LSTM

Accurate state-of-charge (SOC) estimation is a crucial part of the battery management system (BMS). However, conventional estimation methods are unable to capture the extremely complex dynamic characteristics of lithium-ion batteries. Besides, manually setting the optimal hyperparameters of models has many drawbacks. To address the problems, an (improved whale optimization algorithm) IWOA- (long short-term memory) LSTM model is proposed in this work. Utilizing the whale optimization algorithm (WOA) improved with four enhancement strategies (Gaussian chaotic mapping initialization, Nonlinear weight update, Lévy flight mechanism, and Elite opposition-based learning) to optimize the number of hidden layer nodes, the learning rate, and the number of iterations of LSTM model. It not only overcomes the shortcomings of artificially setting LSTM hyperparameters but also further boosts the learning ability of the IWOA-LSTM model, making the model more suitable for SOC estimation under different scenarios. The evaluation results show that the MAE of the proposed model for SOC estimation results is lower than 0.8 % under different temperatures and dynamic conditions. Compared with SOTA models, all MAE, RMSE, and MAPE of the proposed model substantially decline. Furthermore, the R2 of the estimation results using the LG dataset in Experiment Ⅲ is 98.65 %, suggesting the applicability of the proposed model to Li-ion batteries from various manufacturers. The experimental results demonstrate the proposed IWOA-LSTM model is suitable for accurate SOC estimation.

Phase transitions in a heterogeneous lattice hydrodynamic model involving both communication distance and memory time duration differences

An in-depth analysis of vehicle heterogeneity characteristics in intelligent heterogeneous traffic flows, involving connected autonomous vehicles and human-driven vehicles, is essential to reveal the evolutionary mechanism of congestion and to understand its interaction among traffic factors. It has significant theoretical value in guiding the future field deployment and testing of large-scale connected autonomous vehicles through the systematic analysis of the complex dynamic characteristics of intelligent heterogeneous traffic flows from a traffic engineering perspective, as well as traffic management and control in intelligent heterogeneous traffic flow environments. In this study, the lattice hydrodynamics model is applied to construct a corresponding traffic flow model for the differences of communication capability and historical information memory capability for heterogeneous vehicles. The roles of these heterogeneous properties and their interactions are investigated. Through linear stability analysis and nonlinear analysis, we in this study explore the influence of key traffic factors involving the differences of communication capability and historical information memory capability contributing to ameliorating intelligent heterogeneous traffic dynamics, and numerical simulations are carried out to verify the accuracy and reliability of theoretical analysis. An interesting discovery is that longer memory duration gets better, but the optimizing impact of memory duration on the system being marginally decreasing.

KTz Neuron in Electromagnetic Radiation with a Novel Flux-Controlled Memristor

In this paper, a novel discrete flux-controlled generalized memristor is proposed to be used in Electromagnetic Radiation (EMR) KTz neuron. The introduction of flux-controlled memristor can simulate the behavior changes of the neuron under EMR. The stability of flux-controlled memristor EMR KTz Neuron (EKN) map is discussed, and the fixed point of the system is characterized. With the change of parameters, the complex and rich dynamical characteristics of the EKN map are demonstrated. The firing patterns under different initial values are also investigated, and discharge waveforms similar in general shape and structure to human heart and brain disease phenomena are analyzed. Finally, the hardware implementation is verified on the Digital Signal Processing (DSP) platform. The results show that these findings are of great value for the practical applications of neural networks, with a theoretical basis provided.

Simulink Modeling and Analysis of a Three-Dimensional Discrete Memristor Map

The memristor, a novel device, has been widely utilized due to its small size, low power consumption, and memory characteristics. In this paper, we propose a new three-dimensional discrete memristor map based on coupling a one-dimensional chaotic map amplifier with a memristor. Firstly, we analyzed the memristor model to understand its characteristics. Then, a Simulink model for this three-dimensional discrete memristor map was developed. Lastly, the complex dynamical characteristics of the system were analyzed via equilibrium points, bifurcation diagrams, Lyapunov exponent spectra, complexity, and multistability. This study revealed the phenomena of coexisting attractors and hyperchaotic attractors. Simulink modeling confirmed that the discrete memristors effectively enhanced the chaos complexity in the three-dimensional discrete memristor map. This approach addresses the shortcomings of randomness, the lack of ergodicity, and the small key space in a one-dimensional chaotic map, thereby enriching the theoretical analysis and circuit implementation of chaos.

Dynamic characteristics of the internal flow field of a rotary centrifugal air classifier and pressure prediction through attention mechanism-enhanced CNN-LSTM

The flow field in an air classifier has complex dynamic characteristics, making it extremely unstable and difficult to monitor. Here, the time-domain pressure signals in an air classifier were measured at different rotational speeds and spatial locations using micro differential pressure dynamic sensors. The pressure signals were analyzed using the probability density function, standard deviation, and power spectral density. Moreover, a new type of predictive model combining the convolutional neural network, long and short-term memory, and the attention mechanism was proposed. The results show that, regardless of whether a screen cage is present, the pressure signals at different spatial locations in the gas-phase flow field have only one main frequency, which is caused by the quasi-forced vortex motion with the most intense fluctuations. However, PSD has only one main frequency under both load and no-load conditions, its value under load is 1.5 times higher, and the distribution of main frequencies is unaffected by solid loading at high rotational speeds (>40 Hz). Meanwhile, PSD and coherence analyses revealed that large eddies at the outlet induced low-amplitude pressure fluctuations in the high-frequency range. Finally, the proposed CNN-LSTM-A model outperformed traditional deep learning models (SVM and BPNN) in predicting the pressure fluctuations.

Frequency regulation feasible region assessment and optimization of wind farm based on data-driven model predictive control

With the integration of large-scale wind turbines (WTs) into grids via electronic interfaces, power system operators have necessitated frequency support from wind farms. Due to the large number of WTs and their complex dynamic characteristics, it is necessary to assess the primary frequency regulation (PFR) capability and construct feasible region of wind farms. In order to cope with the problems of incomplete parameters, analytical solving complexity and the coupling influence of power system regulation characteristics, this paper develops a data-driven state space mapping linear model predictive control (MPC) to assess the maximum PFR capability of wind farms and reasonably distribute coefficients to WTs. Besides, a coordinated iteration framework between dispatching center and wind farms is proposed to further optimize the wind farm regulation feasible region. The simulation results verify that the proposed method has the advantages of independence from physical parameters, fast analytical solution, and lower requirements of training samples on limited scenarios.

Butterfly valve erosion prediction based on LSTM network

When valves and pipelines are used, erosion wear is a major concern. Erosion wear can lead to equipment downtime, material replacement, and other issues, as well as seal surface failure. The research delves into the phenomenon of erosion in butterfly valves, crucial components utilized across diverse industrial sectors. Erosion primarily affects the valve's disc and seat regions, leading to premature wear and compromised performance. To address this issue, the research employs computational fluid dynamics (CFD) and machine learning techniques, including long short-term memory (LSTM) and BP neural networks, to predict erosion rates under various conditions (different valve openings and particle diameters). Results indicate that erosion escalates with time and larger valve openings, highlighting the need for proactive maintenance strategies. The LSTM model demonstrates superior predictive capabilities compared to the BP neural network, offering valuable insights for improving butterfly valve design and operational efficiency in industrial settings. Furthermore, to seek a more efficient network configuration, the intelligent search capabilities of the particle swarm optimization (PSO) algorithm have been utilized to systematically explore the optimal network structure parameters. The prediction results highlight the advantages of LSTM in handling time series data, particularly in predicting erosion rates with complex dynamic characteristics.

Research on the Application of Neural Network PID in Quadcopter Aircraft

As a highly maneuverable and flexible unmanned aerial vehicle, quadcopters have broad application prospects in both civilian and military fields. However, due to their complex dynamic characteristics, nonlinearity, and strong coupling, achieving stable and precise control of quadcopters is a challenging task. Traditional control methods often fail to meet the control performance requirements for such complex nonlinear systems. Neural networks provide a new approach to solving control problems in complex systems due to their powerful learning and adaptive capabilities. Neural networks can improve the performance of control systems by learning from large amounts of data, capturing the dynamic characteristics of the system, and adjusting control strategies online. Combining neural networks with PID control is expected to fully leverage the advantages of both. Neural networks can adjust the parameters of PID controllers in real-time, enabling them to better adapt to the complex dynamic changes and external disturbances of quadcopter aircraft. This fusion control method brings new possibilities for improving the control performance of quadcopter aircraft. This study explores the application of neural network PID in quadcopter aircraft to achieve stable and precise control, laying the foundation for its widespread promotion in practical applications.

Experimental study of multiscale dynamic characterization of the gas-solid phase in a disturbing rotary centrifugal air classifier using pressure signal testing and analysis methods

This study investigates the complex dynamic characteristics of the flow field inside a disturbing rotary centrifugal air classifier through experimental analysis. To this end, micro-pressure sensors are employed to measure pressure signals in different spatial regions under various operating conditions. The analysis includes time-frequency characterization, complexity assessment, and multi-scale energy analysis. The time-domain fluctuation characteristics of the air classifier's pressure signal are closely tied to operating parameters, with the most intense pressure fluctuations occurring in the middle and back region of the air classifier. The loaded condition intensifies the fluctuations, with particle presence exacerbating low-frequency pressure variations and affecting the cyclone's vortex stability. Additionally, the internal installation of the screen cage structure exhibits maximum complexity and randomness in the internal flow field when Rf = 45 Hz, f = 0.4 kg/s, and β = 0°, showing higher randomness and reflecting fine-scale interactions between gas and particles. Conversely, larger scale characteristics are revealed by DET values in higher detail signal levels. Changes in operational parameters, such as perturbation frequency and feeding time, significantly affect the axial distribution of multi-scale energy in the air classifier, with a greater influence on pressure fluctuation at the macroscopic scale, indicating intensified turbulent behavior between gas and particles. This study reveals the influencing mechanisms of various operating parameters on the multiscale dynamic characteristics and complexity of the flow field inside the rotary centrifugal air classifier.

A New Hyperchaotic Map Based on Discrete Memristor and Meminductor: Dynamics Analysis, Encryption Application, and DSP Implementation

At present, discrete memristor is a focus of chaotic mapping. However, the study of discrete memcapacitor and discrete meminductor has not begun. In order to study the dynamics of discrete meminductor and the dynamics of chaotic map containing discrete meminductor. In this paper, a five dimensional Hyperchaotic mapping is constructed by combining discrete memristor and discrete meminductor. In addition, the dynamical behavior of chaotic mapping is analyzed by means of the phase diagram of chaotic attractor, LEs, Bifurcation diagram and spectral entropy complexity. Theoretical analysis and numerical simulation show that the five -dimensional chaotic map not only has hyperchaotic phenomenon, but also can generate different types of chaotic attractors with the change of parameters. Especially, the special phenomenon of coexistence of attractors is discovered. This makes this five-dimensional hyperchaotic map more suitable for many chaotic based engineering applications. So, an application of discrete chaotic system in chaotic image encryption is proposed. The security and reliability of the encryption scheme are analyzed by NIST test, entropy, correlation and histogram. Finally, the results of the numerical simulation are verified by digital circuit. Due to its complex dynamical characteristics, the 5-D hyperchaotic map has great value in engineering applications.

Applications of Fractional Time Delayed Grey Model in Primary Energy Consumption Prediction

The prediction of total energy consumption is crucial across various domains including the economy, environment, market, and geopolitics. Accurate forecasts can guide policy-making, investment decisions, and international strategies, contributing to sustainable development and energy security. Fractional models have been proven to better capture the long-term memory effects and complex dynamic characteristics of systems, with time delay playing a crucial role in capturing dynamic behaviors. Such models enhance the accuracy and reliability of predicting future trends and behaviors. For the prediction of primary energy consumption in South and Central America, the Middle East, and Africa, this study opts for the existing fractional time delayed grey model, optimizing the fractional order using the particle swarm optimization algorithm. Experimental results demonstrate that in most cases, the predictive capability of the fractional time delayed grey model surpasses that of other grey models. This indicates the effectiveness and reliability of the model in forecasting energy consumption, providing valuable references and foundations for decision-making in relevant fields.

The impact of resource limitation on the pest-natural enemy ecosystem with anti-predator behavior and fear effect

Research on agricultural pest control data indicates that the actual effectiveness of insecticides may exhibit slight variations, influenced by factors such as the type and quantity of pests. Additionally, the development of resistance by pests, as a result of adaptation to the environment, can impact the precision of pest control strategies. Hence we suggest implementing a nonlinear pulse state-dependent feedback control system, considering resource limitations, to investigate the influence of varying pesticide fatality rates on pest outbreaks. This paper takes into account the fear effect and anti-predator behavior to accurately portray the farmland ecosystem. We assessed the phase set and Poincaré map under the conditions for the existence of order-k (where k=1,2,3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$k=1,2,3$\\end{document}) periodic solutions and examined their stability behavior. Remarkably, the system exhibited diverse bifurcation phenomena associated with pesticide-related parameters. Furthermore, the system demonstrated a multistability phenomenon involving the coexistence of the order-1 periodic solution and the limit cycle. This suggests that altering control strategies can disturb the initial coexistence status of pests and natural enemies. It also underscores that resource limitations can indeed impact the outbreak patterns and frequencies of pests. In addition to the variability in pesticide fatality rates, the inclusion of natural enemy releases in the nonlinear pulse strategy contributes to the model exhibiting complex dynamic characteristics. All these findings are substantiated by numerical simulations.

PKG-DTSFLN: Process Knowledge-guided Deep Temporal–spatial Feature Learning Network for anode effects identification

In the aluminum electrolysis process, the accurate identification of anode effect (AE) can improve production efficiency. However, the existing methods fail to effectively capture the features of the anode current signal (ACS) due to its complex dynamic characteristics and temporal–spatial dependence. To address this issue, we propose a Process Knowledge-guided Deep Temporal–spatial Feature Learning Network (PKG-DTSFLN). We believe that knowledge and production data are complementary. Knowledge has potential to deduce beyond observational conditions. Data can be used to detect unexpected patterns. The combination of data and knowledge is potential to improve the performance. Specifically, knowledge is utilized to construct the adjacency matrix to represent the spatial structure of ACS. Then, a deep learning model is constructed by integrating the 1D-CNN and GAT, which is used to capture the temporal–spatial features of ACS. The experimental results on ACS dataset show that the accuracy is more than 99% with low computational cost.

Vertical dynamics analysis and multi-objective optimization of electric vehicle considering the integrated powertrain system

Integrated powertrain system, mainly composed of driving motor and reducer, is one of the excitation sources to electric vehicle vibration but is limited in coupling with the vertical vehicle dynamic in the existing research, which restricts the reliability of dynamics investigation of vehicle and integrated powertrain system. This work proposes a novel vertical dynamic model of the electric vehicle considering the integrated powertrain system, enabling the way to access the coupled vibration among the chassis, unsprung mass, and integrated powertrain system. The road irregularity, unbalanced magnetic pull, and gear mesh excitation are developed and are further applied to the novel vehicle dynamic model with ten degrees of freedom (DOFs). Then, the dynamic response between the novel model and traditional one is compared. Further, the effects of the stiffness and damping of suspension and mounting on the vibration of chassis and integrated powertrain system are explored by numerical calculation. Finally, a multi-objective optimization model is proposed and is driven by the dragonfly algorithm to minimize the vibration of chassis and integrated powertrain system. The research results shown that there will be present the new complex dynamic characteristics with considering the excitation from the integrated powertrain system. The suspension and mounting parameters have the nonlinear effects on the vehicle vibration. And the system vibration can be globally optimized with the optimization algorithm.

A novel 4D memristor-based hyperchaotic system with hidden attractors: Dynamics, periodic orbits analysis, and DSP realization

As a controllable nonlinear component, memristors are easy to generate chaotic signals, and memristor-based hyperchaotic systems are currently a hot research topic. This paper constructs a novel 4D memristive hyperchaotic system, where the presence of trigonometric functions improved system complexity, enabling it to generate richer and more complex dynamic behaviors. The proposed memristive hyperchaotic system has no equilibrium point, thus the corresponding attractors belong to hidden attractors. We discussed the bifurcation behaviors as the system varies with parameters, transient phenomena, 0−1 test, and extreme homogeneous multistability of the system. In addition, complexity analyses based on the SE and C0 algorithms were investigated. Furthermore, the unstable periodic orbits of the system were analyzed, appropriate symbol encodings were established, and the pruning rules and periodic distribution patterns of the cycles were explored. Finally, the new system was experimentally validated using the DSP digital circuit, and the results were consistent with numerical simulations. Therefore, the new system has complex dynamic characteristics, which have a wide range of applications in the fields of secure communication and image encryption.

Exploring representative samples for modeling of wave buoy motion behavior

To collect sufficient data to describe buoy motion behavior in extreme environment conditions is intractable. The conventional data-driven models constructed with randomly selected and limited training samples are unable to perform adequately for exhibiting complex dynamic characteristics. In this work, an active sample exploring strategy is developed to build a reliable prediction model with limited samples. First, an evaluated index is designed to depict both the data distribution characteristics and estimation uncertainty of all test data using a probabilistic model. The representative data are then captured and integrated into the initial training set based on this index, and the updated uncertainty information is used as the criterion to judge the exploring termination. Finally, the initial training set is supplemented to enhance the model performance. Experimental and comparative results demonstrate the superiority of the proposed sample exploring strategy.

Nonlinear vibration control of magnetic bearing system considering positive and negative stiffness

The nonlinear dynamics are studied for an active magnetic bearing (AMB) system under the low-frequency sinusoidal excitation, which can affect the operational stability of a high-speed magnetic bearing. A single-degree-of-freedom nonlinear dynamics model of magnetic bearing was developed with a c-type active magnetic bearing as the research target. The characteristics of the magnetic bearing system show positive and negative stiffness for different feedback gains. The amplitude–frequency response curves of the vibration characteristics of the positive stiffness magnetic bearing system were obtained by using the method of multiple scales. The influence of each parameter on the resonance amplitude and nonlinearity of the magnetic bearing system is understood by analyzing the amplitude–frequency response curve. The bifurcation diagram for the rotor with rotor mass, and control parameters is derived from the numerical simulation of the negative stiffness magnetic bearing system. The parameter intervals that cause single-cycle vibration, double-cycle vibration, and chaotic state of the magnetic bearing rotor are discovered by analyzing the bifurcation diagrams of each parameter. The simulation results show that the magnetic bearing system exhibits complex nonlinear dynamic characteristics when it is a negative stiffness system.

Dynamics modeling and simulation of steel cord conveyor belt based on dynamic elastic modulus

Steel cord conveyor belts are widely used in long-distance transportation of various materials. The steel cord belt is composed of rubber and steel cord core, exhibiting excellent viscoelasticity and complex dynamic characteristics during operation. The existing static elastic modulus cannot accurately reflect the real dynamic properties of the conveyor belt. In this paper, based on the catenary theory, the formula for the dynamic elastic modulus of the conveyor belt is derived by introducing the sag and elastic deformation of the steel cord conveyor belt on the basis of the static elastic modulus. The dynamic modeling of the steel cord conveyor belt is carried out using rigid belt block element + flexible beam method. Then, the RecurDyn software is used to establish the dynamic simulation system of the belt conveyor and conduct simulations, which are validated through a test bench. The results show that dynamics simulation based on the dynamic elastic modulus is closer to the real dynamic characteristics of the belt conveyor. Finally, the dynamic characteristics of belt conveyors under different start-up times, different start-up speeds, and idler spacing are analyzed using this modeling and simulation method. The results demonstrate that the dynamic elastic modulus can accurately reflect the real dynamic properties of the conveyor belt, providing a more accurate dynamic modeling and simulation method for the design of belt conveyors.