Dynamic Analysis Method Research Articles

Accurate Dynamic Analysis Method of Cable-Damper System Based on Dynamic Stiffness Method

To suppress large vibrations of the cable in cable-stayed bridges, it is common to install transverse dampers near the end of the cable. This paper focuses on the cable-damper system; based on the dynamic stiffness method, an accurate dynamic analysis method considering cable parameters, damper parameters, and cable forces is proposed. First, a mechanical analysis model is established which is closer to the cable with a transverse damper installed in the bridge. The model considers the cable bending stiffness, sag, inclination angle, cable force, damper stiffness, damping coefficient, and damper installation height. Then, the characteristic frequency equation of the cable-damper system is established, and a solution method that combines the initial value method and Newton–Raphson method is proposed. This method is confirmed to provide more accurate frequency analysis for the cable-damper system. Finally, using this method, the effect of the damper parameters on the dynamic characteristics of the system is investigated.

Research and Simulation Application of Automatic Generation of Judging Curves for IoT Mobile Devices in Train Driving Operations Incorporating Hybrid Genetic Algorithms

A control system for automatically generating a train operation evaluation curve based on a hybrid genetic algorithm has been proposed in order to improve the safety of automatic train operation. The system is based on Internet of Things (IoT) mobile devices and utilizes various sensors such as accelerometers, gyroscopes, barometers, and GPS to collect real-time data on the driver’s acceleration, angular velocity, air pressure, and position, among other parameters. 5G wireless communication technology is used to achieve high-speed data transmission and real-time communication with the cloud. Based on cloud data, a spatial grid area planning model is constructed to automatically generate the train operation evaluation curve. Using a spatial dynamic programming method, the entire network of Electric Multiple Unit (EMU) trains is treated as a whole, and a dynamic model of the EMU train is constructed. The spatial area parameters of the EMU train’s automatic driving operation evaluation curve are combined with the dynamic model analysis method. By identifying and analyzing environmental parameters such as train speed and distance, the EMU train’s automatic driving operation evaluation curve is optimized. A hybrid genetic evolution learning optimization algorithm is used to fit the motion spatial parameters of the EMU train’s automatically generated driving operation evaluation curve, and a spatial behavior analysis simulation is created for the EMU train’s automatically generated control driving operation curve. Through the use of hybrid genetic evolution learning optimization technology, adaptive control and automatic driving operation curve simulation planning for the automatically generated driving operation evaluation curve of the maglev train are achieved, as well as simulation and algorithm optimization design for the automatically generated driving operation evaluation curve of the maglev train. The simulation results show that the method has good adaptability and strong automatic control capability. The experimental results demonstrate the control effect of the proposed method on energy consumption and train stopping error, indicating that the proposed method can effectively improve the parameter adjustment and offset correction ability of the evaluation curve generated by the high-speed train driving operation.

ANALYSIS AND COMPARISON OF MACHINE LEARNING METHODS FOR MALWARE DETECTION

Our study aims to analyze and evaluate modern machine learning methods for detecting malware, a critical challenge given the increasing complexity and volume of cyber threats. We explore various algorithms, including Support Vector Machines, Random Forest, Logistic Regression, Decision Trees, and K-Means Clustering, comparing their effectiveness in identifying and classifying malware. Our methodology combines static, dynamic, and memory-based analysis techniques, offering a comprehensive approach to understanding malware behavior. Key findings reveal that Decision Trees and Random Forests demonstrate impressive accuracy in both binary and multi-class classification tasks. We also highlight novel methods such as the Self-Organizing Incremental Neural Network (SOINN), which effectively handles evolving malware threats. The integration of static and dynamic analysis methods deepens insights into malware behavior. This research underscores the importance of advancing machine learning techniques to enhance cybersecurity measures against evolving global malware threats, offering valuable insights for future research directions.

ДИНАМИЧЕСКИЙ МЕХАНИЧЕСКИЙ АНАЛИЗ МОДИФИЦИРОВАННЫХ ПОЛИВИНИЛБУТИРАЛЕМ СОПОЛИМЕРОВ НА ОСНОВЕ ДИ(1-МЕТАКРИЛОКСИ-3-ХЛОРПРОПОКСИ-2-)МЕТИЛФОСФОНАТА И 2-ГИДРОКСИПРОПИЛМЕТАКРИЛАТА

The change of elastic modulus and tangent of angle of mechanical loss of materials based on polyvinyl butyral solutions in a mixture of monomers of di(1-methacryloyloxy-3-chloropropoxy-2-)methylphosphonate and 2-hydroxypropyl methacrylate has been investigated by the method of dynamic mechanical analysis. The presence of several peaks on the temperature dependences of tgd was observed, which is probably due to the presence of regions differing in crosslinking density. At 10 wt. % of polyvinylbutyral content in the process of (co)polymerization its partial migration to the surface of the formed polymeric material occurs.

Study on the stability of waste rock filling in goaf based on dynamic comprehensive analysis method

Study on the stability of waste rock filling in goaf based on dynamic comprehensive analysis method

Quality-aware fuzzy min-max neural networks for dynamic brain network analysis and its application to schizophrenia identification

Dynamic functional connections (dFCs) refer to the observed phenomenon that functional connectivity changes over a short time, which has become a pioneering method in the diagnosis of brain diseases. However, current dynamic brain network analysis methods are insufficient to address the fuzzy information of brain networks caused by the quality issues of rs-fMRI data, and the uncertainty arising from inconsistent data quality across different windows due to missing values and noise in individual windows. These results in unreliable integration of multiple windows. To this end, this paper proposes a dynamic brain network analysis method based on quality-aware fuzzy min–max neural networks (QFMMNet). The individual window of dFCs is treated as a view, and we define three convolution filters to extract features from the brain network under the multi-view learning framework, thereby obtaining multi-view evidence for dFCs. We design the multi-view fuzzy min–max neural networks (MVFMM) based on fuzzy sets to deal with the fuzzy information of the brain network, which takes evidence as input patterns to generate hyperboxes and serves as the classification layer of each view. A quality-aware ensemble module is introduced to deal with uncertainty, which employs Dempster-Shafer Theory (D-S theory) to directly model the uncertainty and evaluate the dynamic quality-aware weighting of each view. At the decision level of model, a weighted fusion strategy is employed for multi-view fusion based on the dynamic quality-aware weighting, thereby the quality of each view is fully considered to improve the trustworthiness of the decision. We verified the effectiveness of QFMMNet through comparison with advanced algorithms on three real schizophrenia datasets, and the results show that QFMMNet significantly improved the classification performance of brain disease diagnosis tasks. The accuracy on the Huaxi, COBRE, and Xiangya datasets reached 87.14%, 86.67%, and 85.27%, respectively.

Understanding the temporal variability and predictability of streamflow signatures in the Colorado River Basin

It is well established that streamflow regimes evolve over decadal time scales (i.e., low frequency) leading to long term shifts in distributions. Similar low frequency variations have also been documented in streamflow predictability. Here we explore connections between streamflow distribution attributes and predictability regimes in the Upper Colorado River Basin. We employ nonlinear dynamical time series analysis methods on streamflow timeseries covering the period 762 – 2019 for six locations in the basin. First, a wavelet spectral analysis is performed to obtain the quasi-periodic ‘signal’ of the streamflow. The wavelet analysis also provides the temporal variability of the variance of the signal time series. The signal time series is embedded in a D-dimensional space with appropriate lag to reconstruct the phase space of the dynamics – i.e. the attractor. Overall predictability is assessed by quantifying the average divergence trajectories in the phase space using Global Lyapunov Exponents and the temporal variability of predictability via the Local Lyapunov Exponents. Results show clear oscillations in streamflow predictability with periods of both high and low predictability occurring throughout the study period at all gauges. Comparing predictability timeseries across the stream gauges we find that general consistency in high and low predictability periods, although they do not perfectly align temporally. In general, higher (lower) predictability periods are characterized by lower (higher) streamflow variance. While there is not a clear relationship between streamflow magnitude and predictability in general, modern high predictability epochs are characterized by a slightly greater likelihood of dry years and lower likelihood of wet years than other epochs. These findings indicate the potential for statistically significant differences in streamflow signatures between high and low predictability periods. Exploring these fundings further with potential connections to large-scale climate can be helpful in exploiting them for skillful short and medium term flow projections.

A study of the dynamic transmission efficiency of the cycloid pin gear pair under dynamic characteristic analysis

Transmission efficiency is one of the main technical indicators for evaluating the transmission performance of a cycloid pin gear planetary reducer. The cycloid pin gear pair is a crucial component of such a reducer. When the design parameters of the cycloid pin gear pair are determined, its transmission efficiency is often considered a constant, which significantly hinders accurate calculations of the transmission efficiency of the cycloid pin gear planetary reducer. To address this issue, this paper introduces a dynamic transmission efficiency analysis method specifically tailored for the cycloid pin gear pair, grounded firmly in dynamic characteristic analysis. Firstly, a comprehensive and meticulous dynamic performance analysis method is proposed for the cycloid pin gear pair, enabling precise calculations of dynamic transmission efficiency. Secondly, an optimization framework is introduced, with a primary focus on enhancing the transmission efficiency of the cycloid pin gear pair. A transmission efficiency assessment model is established as the objective function, which takes into account the pressure angles of both the driving gear and the driven gear at the meshing position. To demonstrate the practical utility and effectiveness of these methodologies, an illustrative example is presented. The results obtained from this example underscore a remarkable improvement in the overall transmission performance of the system. This enhancement is characterized by substantial gains in transmission efficiency and effective vibration control, highlighting the significance and practicality of the proposed approaches.

Influence of dynamic complexity on serial robots with joint clearance based on a nonlinearity measure

Conditional monitoring and diagnosis of serial robots depend not only on the method used but also on the dynamic complexity of the robotic system itself. Previously, researchers have focused mainly on the analysis of dynamic behaviors with different parameters of a mechanical system with clearance. Although the indicators such as Root Mean Square (RMS), spectrum kurtosis (SK), Entropy have the potentiality to express the specific trend by different parameters, it can be noticed that they mainly depend on the values of the dynamic responses and neglect the information of the nonlinear characteristics in the responses of mechanical systems. In this paper, a nonlinearity measure-based method is proposed to analyze the impact of dynamic complexity on serial robots with joint clearance. The kinetic equations of a serial robot with joint clearance are established by considering the contact forces in the joint. The dynamic model of the robotic system is validated by experiments. A quantization method for dynamic complexity analysis is established based on the nonlinearity measure, and the influences of parameters on the dynamic complexity are analyzed via numerical computations for the validated dynamical models of the robotic system. A relationship is developed among the clearance radius, friction coefficient, and dynamic complexity. This shows that a larger clearance radius will lead to a more complex system and that friction coefficients within a certain range can reduce the dynamic complexity of the system. These results can guide methodological choices for condition monitoring and diagnosis of serial robotic systems.

Parallel–Serial Robotic Manipulators: A Review of Architectures, Applications, and Methods of Design and Analysis

Parallel–serial (hybrid) manipulators represent robotic systems composed of kinematic chains with parallel and serial structures. These manipulators combine the benefits of both parallel and serial mechanisms, such as increased stiffness, high positioning accuracy, and a large workspace. This study discusses the existing architectures and applications of parallel–serial robots and the methods of their design and analysis. The paper reviews around 500 articles and presents over 150 architectures of manipulators used in machining, medicine, and pick-and-place tasks, humanoids and legged systems, haptic devices, simulators, and other applications, covering both lower mobility and kinematically redundant robots. After that, the paper considers how researchers have developed and analyzed these manipulators. In particular, it examines methods of type synthesis, mobility, kinematic, and dynamic analysis, workspace and singularity determination, performance evaluation, optimal design, control, and calibration. The review concludes with a discussion of current trends in the field of parallel–serial manipulators and potential directions for future studies.

Generalized pole-residue method for dynamic analysis of nonlinear systems based on Volterra series

Generalized pole-residue method for dynamic analysis of nonlinear systems based on Volterra series

How do Ritz Vectors React to the Imposed Load?

All real structures behave dynamically when loaded or displaced. Additional inertial forces are equal to the force in acceleration using Newton's second law. If the forces or the change of places are applied very slowly, the inertial forces can be ignored and a static analysis can be done. Therefore, it can be said that dynamic analysis is a simple extension of static analysis. In addition, all potential real structures have unlimited degrees of freedom. Therefore, the most critical part of structural analysis is creating a model with a limited number of degrees of freedom that has several almost massless members and several nodes, which can estimate the behavior of the structure appropriately. The mass of the structure can be concentrated in the nodes. Also, for a linear elastic system, the stiffness characteristics of the members can be estimated very accurately - according to the experimental data - although the estimation of dynamic loading, energy loss, and boundary conditions can be very difficult. Therefore, the nonlinear dynamic analysis method is usually used in theoretical studies and is not used in routine designs. So, it is a worthwhile task to propose a simple and computer method for seismic estimation of structures. For this purpose, POA has been introduced. In many cases, we can get more valuable information from POA than nonlinear dynamic analysis, this method is simple and economical and has attracted more attention nowadays.

Parametric global mode method for dynamical modeling and response analysis of a rotating and length-varying flexible manipulator

Rotating and Length-Varying Flexible Manipulators (RLVFMs) benefit from the ability to transform their length to adapt to complex and demanding workspaces but suffer from increased complexity in nonlinear dynamical characteristics and thus difficulties in modeling. To provide an in-depth understanding of the RLVFMs, this paper proposes a novel dynamical modeling approach for the RLVFMs, called the Parametric Global Modal Method (PGMM), and presents a framework to study their nonlinear responses. It is capable of addressing time-varying boundary conditions and describing the elastic deformation of all flexible components with only one set of modal coordinates. A low-dimensional dynamical model of a RLVFM is developed. The natural characteristic results obtained from the models developed by the PGMM and the finite element method (FEM) are compared for verifications of the PGMM. Via a convergence analysis of responses, the high precision of the model developed by the PGMM is verified to be achieved by using only the first two modes. On this basis, the dynamic responses and computational efficiency of the low-dimensional model are validated through experiments and finite element method (FEM) simulations. Moreover, the responses of the RLVFM under operations of rapid maneuvering are studied and a potential vibration control strategy for the RLVFM is preliminarily demonstrated. This work provides a new way of developing advanced dynamical modeling methods of reconfigurable and deformable multi-component mechanisms for their dynamical design, response analysis, and system control.

A novel probabilistic analysis method for long-term dynamical response analysis

A novel probabilistic analysis method for long-term dynamical response analysis

Scalar- and vector-valued seismic fragility assessment of segmental shield tunnel lining in liquefiable soil deposits

Seismic fragility analysis can quantitatively evaluate the seismic performance of structures from a probabilistic viewpoint and accurately characterize the relationship between the degree of structural damage and ground motion intensity. This study investigates the seismic fragility of shield tunnels in three different liquefiable and non-liquefiable soils. A plane-strain finite element model of the saturated soil and shield tunnel is established via the OpenSees computational platform employing the multi-yield surface elastoplastic PressureDependMultiYield and PressureIndependMultiYield models to simulate the constitutive behaviour of liquefiable and non-liquefiable soils. The developed model is utilized to conduct nonlinear dynamic effective stress time history analyses to generate the seismic fragility curves and surfaces based on the incremental dynamic analysis method. Meanwhile, appropriate scalar- and vector-valued intensity measures are identified based on their correlation, efficiency, practicality and proficiency. Compared with the fragility curves based on scalar-valued intensity measures, the fragility surfaces based on the vector-valued intensity measures can better describe the effect of ground motion characteristics on the structural seismic demand, and thus can more accurately assess the structural seismic performance. The seismic damage probabilities derived from the fragility curves and surfaces reveal that the seismic damage risk of the shield tunnel in sandwiched liquefiable soil deposit is higher than that of the tunnel structure located in entirely liquefiable or non-liquefiable soil profiles. This finding underscores the importance of carefully evaluating the seismic safety of shield tunnels situated in sandwiched liquefiable soil deposits.

Multiplet relaxation transitions in fluorurethane coating after climate aging

The relaxation transition from a glassy to a highly elastic state (α-transition) of a fluoropolyurethane coating deposited on the surface of VPS-48/778 glass fiber reinforced plastic was studied using the method of dynamic mechanical analysis. It is shown that the relaxation maximum of the dynamic loss modulus in the initial state is a superposition of α1-, α2-, α3-transitions, corresponding, respectively, to transitions from the glassy to highly elastic state of VE-69 enamel and EP-0215 epoxy primer. The transition temperature α1, which is the glass transition temperature of fluoropolyurethane VE-69, after 3 years of exposure decreases in proportion to the average annual air temperature of the region. The transition temperatures α2 and α3 after full-scale exposure due to post-curing increased by 13−15°C and acquired stable values α2 = 99 ± 1°C, α3 = 122.5 ± 0.5°C, regardless of the climatic conditions of the tests.

Impact performance of the marine rotating machinery coupled with floating raft-airbag-limiter under heaving motion

A dynamic model of an asymmetric system is developed with limiter under heave conditions to explore the impact characteristics of a marine rotating machinery coupled airbag-floating raft-limiter systems. The steady-state response of the system is numerically solved using the Runge-Kutta theory. Nonlinear dynamic analysis methods, including energy trajectories and time-averaged energy diagrams, have been introduced to examine the influence of rotor speed, heaving amplitude, limiter clearance, and contact stiffness on the dynamic characteristics of the system. The results show that the amplitude of the heave increases, the system collides with the limiter, resulting in a significant reduction in amplitude. Meanwhile, the dynamic behavior of system transitions from quasi-periodic motion to chaotic state. After the impact, the energy of the system begins to concentrate in the region of impact, and the higher the time-averaged energy value, the more severe the impact of the system. The method proposed in this paper can provides an optimization scheme and theoretical reference for the impact problem of this system.

Fractional-Order Modeling and Stochastic Dynamics Analysis of a Nonlinear Rubbing Overhung Rotor System

To study the nonlinear dynamic behavior and system stability of a rubbing overhung rotor with viscoelastic and memory-effect damping and random uncertain parameters, this paper introduces a fractional-order modeling and stochastic dynamic analysis method for the nonlinear overhung rotor system with frictional impact faults. Firstly, the dynamic equations of the overhung rotor considering friction effect and fractional damping effect are established based on the transfer matrix method and fractional order derivative. Then, the time-domain response of the fractional-order dynamic equations is solved by combining the Runge–Kutta method and the continuous fractional expansion, and the steady-state response characteristics of different fractional damping are analyzed in the deterministic case. Finally, to analyze the response of the system under the effect of stochastic parameters, the sparse grid-based PCE metamodel of the system response is developed. Statistical moments, probability distributions, and sensitivity indices of the response of stochastic systems are revealed. The results of this paper provide a theoretical basis for efficient and accurate prediction of the stochastic response of nonlinear rubbing overhung rotor systems.

Engineering and Modeling of Water Flow via Computational Fluid Dynamics (CFD) and Modern Hydraulic Analysis Methods

The manuscripts presented in this Special Issue will both present engineering analyses of problems of contemporary importance in fluid mechanics and hydrodynamics and describe historical water systems and the technologies used to design and operate them by ancient Old and New World water engineers [...]

Winch Traction Dynamics for a Carrier-Based Aircraft Under Trajectory Control on a Small Deck in Complex Sea Conditions

When the winch traction system of a carrier-based aircraft works under complex sea conditions, the rope and the tire forces are greatly changed compared with under simple sea conditions, and it poses a potential threat to the safety and stability of the aircraft’s traction system. The accurate calculation of the rope and tire forces of a carrier-based aircraft’s winch traction under complex sea conditions is an arduous problem. A novel method of dynamic analysis of the aircraft-winch-ship whole system under complex sea conditions is proposed. A multiple-frequency excitation is adopted to describe the complex sea conditions and the influences of pitching amplitude, and the rolling frequency on the traction dynamics of a carrier-based aircraft along the setting trajectory under complex sea conditions are studied. The advantages and disadvantages of a winch traction system with trajectory control and without trajectory control in complex sea conditions are analyzed. For realizing the trajectory control of the aircraft, the vector difference between the center of mass for the carrier-based aircraft and the position on the predetermined Bessel curve is calculated, so as to obtain the azimuth vector in the aircraft coordinate system. This research is innovative in the modeling of the whole system and the trajectory control of a carrier-based aircraft’s winch traction system under the complicated sea condition of the multi-frequency excitation. ADAMS (Automatic Dynamic Analysis of Mechanical System) is used to verify the correctness of the theoretical calculation for the winch traction. The results show that the complex sea environment has a certain influence on the winch traction safety of the aircraft; in the range of 10–15 s for the traction, the rope force amplitude of complex sea conditions under the multi-frequency excitation is 29.5% larger than that of the single-frequency amplitude, while the vertical force amplitude of the tire is 201.1% larger than that of the single-frequency amplitude. This research has important guiding significance for the selection of rope and tire models for a carrier-borne aircraft’s winch traction in complex sea conditions.