Dynamic Response Research Articles

Mathematical modeling for detecting variable gear tooth cracks in a windturbine gearbox operating under a ramp-up wind speed

Abstract Wind is natural and possess renewable energy. This energy is an opportunity when fossil fuel is shrinking very fast and creating heavy ecological imbalance. Wind energy (WE) is an input to wind turbine (WT) system that produces electricity. Therefore, knowledge of wind is a prerequisite for the dynamic modelling of wind turbine drivetrain system (WTDS) in case of defective gear drives. Wind speed is not certain and its ramp-up component can be very detrimental in poor occasions. Input torque to drivetrain system is modelled based on ramp-up wind speed for the diagnosis under healthy and defective WTDS. Lumped parameter model simplifies the mathematical model of gearbox and the Lagrange’s formulation gives insights to derive the equation of motion of the system. Time varying mesh stiffness (TVMS) computed at mean speed is interpolated in MatLab to get the same at ramp-up condition. The interpolated stiffness is incorporated in the mathematical formulation for both healthy and defective system. Numerical discretization of differential equation is done in MatLab using Houbolt method to produce the dynamic responses of WTDS with gear defects under ramp-up speed. Here, time frequency (TF) domain analysis is explored to perform dynamic analysis. Hence, short time Fourier transforms (STFT) is applied to low pass filtered signal; wavelet packet transform is helpful in filtering process. The signal processing technique is proposed to investigate the defective WTDS under variable wind speed. This can be helpful in future investigations in WT under more real situations.

Examining Ecology–Agriculture–Economy Nexus Shifts to Propose Win–Win–Win Pathways for Sustainable Development in Mountainous Areas: Insights From the Greenest City in China

ABSTRACTEcosystem restoration projects (ERPs) are effective methods for reversing land degradation. However, the dynamic responses of the ecology–agriculture–economy nexus to ERPs and socioeconomic development have yet to be systematically analyzed. To address this issue, we adopted an ERP hotspot as a basis for exploring the evolution regimes of sectoral variables and their determinants. ERPs facilitated vegetation restoration and strengthened carbon sequestration and soil retention, whereas grain productivity declined sharply since 2000 because of industrial and planting structure adjustments. Economic growth was accompanied by marked industrial transformation; the dominant role of the primary industry was gradually replaced by that of other industries, with the population and employment structures changing accordingly. Rural population loss weakened the agricultural production capacity, with the promotion of economic crops further degrading the dominant role of grain crops. Such nexus shifts indicated that positive progress in the ecological and economic sectors should be further optimized to reserve sufficient space for agricultural production and industrial development, instead of relying solely on expanding afforestation areas. Notably, the temporary synergic co‐evolution of nexus sectors from 2008 to 2022 is not stable because of the low grain self‐sufficiency ratio, which might threaten the sustainability of social‐ecological systems. Therefore, agricultural production efficiency must be improved through large‐scale production and improvements in agricultural production conditions to sustain the grain supply. The nexus perspective enriches our understanding of interlinkages among sectors in different phases, facilitating the formulation of coordinated strategies for regional sustainable development.

Replicating Dynamic Immune Responses at Single-Cell Resolution within a Microfluidic Human Skin Equivalent.

To enable in vitro investigation of human skin immunology, this study develops a microfluidic human skin equivalent (HSE) that supports the delivery of circulating immune cells via a vascular microchannel embedded within the dermis of a full-thickness construct. Within this platform, activation of keratinocyte inflammation promotes monocyte migration out of the vascular channel and into the dermal and epidermal compartments. Single-cell transcriptomic analysis reveals dynamic and cell-specific patterns of gene expression that are characteristic of acute activation and resolution of an inflammatory immune response, and the gene signatures of the monocyte-derived cells closely matches the differentiation trajectory of the monocytes into mature dermal macrophages. The microfluidic HSE is also applied to modeling age-associated immune dysfunctional and accurately replicates elevated monocyte recruitment in aged skin. Thus, the microfluidic HSE presented here replicates key aspects of dynamic inflammatory immune responses and represents a tractable experimental tool for interrogating mechanisms of human skin immunology.

Numerical and Experimental Investigation of an Extradosed Cable-Stayed Bridge in Urban Rail Transit Under Dynamic Train Load

With the urban rail transit construction boom, more and more extradosed cable-stayed bridges have been built and put into service in the urban rail transit lines, due to the advantages of aesthetic appearance and high stiffness. There are distinct dynamic characteristics of the metro train and extradosed cable-stayed bridge system, which have rarely been explored. This paper presents the numerical and experimental investigation of an extradosed cable-stayed bridge under dynamic train load in urban rail transit with the maximum span among its type of bridge in China. Three vehicle modes with different degrees of freedom and two bridge finite element models are adopted to perform the dynamic analysis of the metro train and extradosed cable-stayed bridge system. Moreover, the bridge is instrumented in a comprehensive field measurement campaign including ambient vibration testing, static load testing, and dynamic load testing. The collected modal parameters and static and dynamic responses of the bridge are employed to validate the established numerical models. Dynamic characteristics of the analyzed bridge are subsequently explored. In particular, dynamic effects of the main components including the main girder, pylons, and inclined cables are examined considering a variety of operational conditions. This study provides an effective method for analyzing the dynamic responses and valuable insights into the dynamic performance of large-span extradosed cable-stayed bridges under metro train load in urban rail transit.

The Simulation of Pose Control for Stewart Platform Based on Gain-Enhanced PID with Generalized Boolean Algebra

Due to the nonlinear and time-varying uncertainties commonly present in practical industrial systems, traditional PID controllers often fail to achieve optimal control performance under significant input variations. This paper proposes a gain-enhanced PID control method optimized by generalized Boolean algebra, applying Boolean algebraic logic control strategies to traditional PID control to improve parameter adaptability and real-time control performance. Using MATLAB/Simulink simulation tools, a dual-loop control simulation model is established for the Stewart platform, incorporating both conventional PID control and the gain-enhanced PID control optimized by generalized Boolean algebra. Both models are configured with identical PID parameters for simulation, and comparative experimental results are presented. The experimental results demonstrate that, in the Simulink simulation model of the Stewart platform's pose control system, the proposed gain-enhanced PID control system optimized by generalized Boolean algebra improves the dynamic performance and response speed compared to the traditional PID control system, enhancing the real-time performance and stability of the Stewart platform control.

Non-linear strain based FE model for free vibration analysis of stiffened functionally graded folded plates in hygrothermal environment

Stiffened functionally graded (FG) folded plates are being widely used these days in aeronautical, automotive, and naval applications which are often exposed to extreme environmental conditions characterized by high temperature and moisture concentrations. However, a numerical model to estimate the dynamic response of such structures in hygrothermal environment is almost non-existent. It is from this perspective that, a MATLAB-based finite element (FE) model is developed to assess the free vibration characteristics of stiffened FG folded panels in hygrothermal environment and is presented for the first time in this article. The governing equation is derived following the first order shear deformation theory including hygro-elastic and thermo-elastic relations. Temperature and moisture concentrations are assumed to vary both linearly and uniformly through the plate’s thickness to numerically simulate hygrothermal environment. Non-linear strains are incorporated in the present FE model to consider the hygrothermal effect. Another FE model is also developed in COMSOL Multiphysics software for these structures and is compared with the MATLAB-based model. Numerical simulations reveal that the free vibration characteristics of stiffened FG folded plates are significantly affected by hygrothermal environment. The present FE models can be effectively utilized by engineers and researchers to analyze and optimize the dynamic behavior of stiffened FG folded plates. The results are also expected to serve as a benchmark for future research works.

Effects of series sequence on the nonlinear dynamic responses of integrated quasi-zero stiffness isolators

Effects of series sequence on the nonlinear dynamic responses of integrated quasi-zero stiffness isolators

Memristor-based feature learning for pattern classification

Inspired by biological processes, feature learning techniques, such as deep learning, have achieved great success in various fields. However, since biological organs may operate differently from semiconductor devices, deep models usually require dedicated hardware and are computation-complex. High energy consumption has made deep model growth unsustainable. We present an approach that directly implements feature learning using semiconductor physics to minimize disparity between model and hardware. Following this approach, a feature learning technique based on memristor drift-diffusion kinetics is proposed by leveraging the dynamic response of a single memristor to learn features. The model parameters and computational operations of the kinetics-based network are reduced by up to 2 and 4 orders of magnitude, respectively, compared with deep models. We experimentally implement the proposed network on 180 nm memristor chips for various dimensional pattern classification tasks. Compared with memristor-based deep learning hardware, the memristor kinetics-based hardware can further reduce energy and area consumption significantly. We propose that innovations in hardware physics could create an intriguing solution for intelligent models by balancing model complexity and performance.

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.

Thermo-viscoelastic analysis of a composite disk under parabolic temperature distribution using the Rayleigh-Ritz Method with Trigonometric Ritz Functions

The study focuses on the thermo-viscoelastic behavior of a composite disk subjected to a parabolic temperature distribution. The analysis employs the Rayleigh-Ritz method, combined with trigonometric Ritz functions, to model the dynamic response of the disk. The key objective is to investigate how temperature variations and viscoelastic material properties influence the radial and tangential stresses and strains in the disk. The thermo-viscoelastic problem is formulated by considering the time-dependent evolution of stresses and strains, incorporating both thermal expansion and viscoelastic relaxation effects. The radial stress and tangential stress are expressed as functions of time, governed by the relaxation functions, respectively. These functions describe the material's viscoelastic behavior, while the thermal expansion coefficients account for temperature-induced strain variations. The parabolic temperature distribution introduces a spatially varying thermal load, which is integrated into the thermo-viscoelastic equations. The Rayleigh-Ritz method is used to discretize the problem, with trigonometric Ritz functions chosen to represent the spatial variation of stresses and strains. This approach allows for efficient computation of the disk's response while capturing the anisotropic and viscoelastic nature of the composite material. The results highlight the interplay between thermal effects and viscoelastic behavior in determining the stress and strain distributions. The parabolic temperature distribution leads to non-uniform thermal expansion, which interacts with the viscoelastic relaxation processes to produce complex stress patterns. The analysis provides insights into the time-dependent evolution of stresses and strains, offering a comprehensive understanding of the thermo-viscoelastic response of the composite disk under thermal loading.

Augmented carbon utilization and ammonia assimilation in heterotrophic microorganism under magnetic field stimulation.

Ammonia assimilation is crucial in microbial nitrogen metabolism, and researching the impact of magnetic field (MF) on heterotrophic ammonia assimilation (HAA) contributes to improving nitrogen utilization and environmental remediation. This study systematically investigated the profound effects of MF stimulation on carbon and ammonia assimilation mechanisms in heterotrophic microorganisms. The dynamic responses of microbial carbon source metabolic efficiency and nitrogen source assimilation rates were quantitatively analyzed by designing a multidimensional stimulation environment of different nutrient levels (C/N 20, 25, 30) and MF intensities (0, 1, 20 mT). The findings demonstrated that weak MF stimulation markedly enhanced carbon utilization efficiency and ammonia assimilation capacity, evidenced by accelerated metabolic flux, significant biomass augmentation and metabolic network reconfiguration. MF induced pronounced activation of key metabolic enzymes, uncovering coordinated regulatory mechanisms at the molecular level. Additionally, the MF stimulation enriched genera the Halomonas and Marinobacter, enhancing the stability of microbial community. These discoveries not only advance the theoretical understanding of MF-enhanced metabolic modulation but also offer cutting-edge insights and potential solutions for developing MF-assisted bioconversion and environmental remediation technologies.

Full-bridge DC-DC Converter based on Linear Active Disturbance Rejection Control

Dual Active Bridge (DAB) converter has been a research hotspot in recent years. It is widely used in distributed power generation system, DC microgrid system and power management system. Traditional PI controllers do not perform well in the face of nonlinear loads and external disturbances, so a more advanced control method is needed to improve the stability and performance of the system. In this paper, a control strategy based on active disturbance rejection control (ADRC) is proposed to improve the control performance of full-bridge DC-DC converter. First of all, the small signal mathematical model of full-bridge DC-DC converter is established, and the function relationship between input, output, control quantity and disturbance quantity in the system is obtained. Then, the extended state observer of ADRC core module is designed, and its parameters are adjusted and simulated, compared with the traditional PI controller. The simulation results show that the full-bridge DC-DC converter based on LADRC has faster dynamic response and better disturbance immunity, which can effectively improve the stability and robustness of the system.

Intelligent Control of the Air Compressor (AC) and Back Pressure Valve (BPV) to Improve PEMFC System Dynamic Response and Efficiency in High Altitude Regions

Proton exchange membrane fuel cells (PEMFCs), as a clean energy technology, show remarkable potential for a wide range of applications. However, high altitude regions pose significant challenges for PEMFC system operation due to thin air and low oxygen partial pressure. Existing logic judgement-based controls exhibit defects such as poor robustness and poor adaptability, which seriously restrict PEMFC system operation. In order to address this issue, this paper puts forth an intelligent control of a PEMFC system air compressor (AC) and back pressure valve (BPV) using an asynchronous advantage actor-critic (A3C) algorithm and systematically compares it with the logic judgement-based control. The application of an A3C-based control under three distinct high altitude test conditions demonstrated a notable enhancement in dynamic responsiveness, with an improvement of up to 40% compared to the results for the logic judgement-based control. Additionally, an improvement of 5.8% in electrical efficiency was observed. The results demonstrate that the A3C-based control displays significant robustness and control precision in response to altitude alterations.

Development of a Sustainable Flexible Humidity Sensor Based on Tenebrio molitor Larvae Biomass-Derived Chitosan.

This study presents the fabrication of a sustainable flexible humidity sensor utilizing chitosan derived from mealworm biomass as the primary sensing material. The chitosan-based humidity sensor was fabricated by casting chitosan and polyvinyl alcohol (PVA) films with interdigitated copper electrodes, forming a laminate composite suitable for real-time, resistive-type humidity detection. Comprehensive characterization of the chitosan film was performed using Fourier-transform infrared (FTIR) spectroscopy, contact angle measurements, and tensile testing, which confirmed its chemical structure, wettability, and mechanical stability. The developed sensor exhibited a broad range of measurements from 6% to 97% relative humidity (RH), a high sensitivity of 2.43 kΩ/%RH, and a rapid response time of 18.22 s with a corresponding recovery time of 22.39 s. Moreover, the chitosan-based humidity sensor also demonstrated high selectivity for water vapor when tested against various volatile organic compounds (VOCs). The superior performance of the sensor is attributed to the structural properties of chitosan, particularly its ability to form reversible hydrogen bonds with water molecules. This mechanism was further elucidated through molecular dynamics simulations, revealing that the conductivity in the sensor is modulated by proton mobility, which operates via the Grotthuss mechanism under high-humidity and the packed-acid mechanism under low-humidity conditions. Additionally, the chitosan-based humidity sensor was further seamlessly integrated into an Internet of Things (IoT) framework, enabling wireless humidity monitoring and real-time data visualization on a mobile device. Comparative analysis with existing polymer-based resistive-type sensors further highlighted the superior sensing range, rapid dynamic response, and environmental sustainability of the developed sensor. This eco-friendly, biomass-derived, eco-friendly sensor shows potential for applications in environmental monitoring, smart agriculture, and industrial process control.

Experimental Investigation on the Dynamic Response of a Turbocharger Centrifugal Compressor in Surge Transitions

Abstract Centrifugal compressors play a critical role in various industrial applications, and understanding their operational behavior, especially during surge conditions, is essential for enhancing efficiency and reliability. This study investigates surge transitions in centrifugal compressors by employing a comprehensive approach that integrates dynamic mass flow rate and pressure measurements together with dynamic structural response data. To gain insight into the transition from the stable to the unstable region of the compressor, an extensive experimental analysis on a centrifugal compressor was conducted. The activity involves measurements used to characterize and identify the behavior of the compressor in correspondence of surge inception conditions. Frequency and time–frequency data analysis techniques have been employed to examine the inflow pressure and the anemometric signal. The purpose is to identify their characteristics and define the compressor operation as stable or unstable. Synchronous averages, applied in the time domain, have proven to be effective for detecting incipient surge conditions. Additionally, statistical techniques, such as variance and spectral kurtosis (SK), have been utilized for the detection in signals with strong additive noise and nondeterministic contents. A method for system identification in deep surge is introduced, particularly focusing on the correlation between the compressor circuit geometries and the operating conditions of the turbocharger. This approach enhances the diagnostic capability of the monitoring system by providing a better knowledge of surge occurrence. The paper is aimed to provide information for the development of control strategies and predictive maintenance protocols to mitigate surge-related issues.

NONLINEAR VIBRATION OF A BEAM SUBJECTED TO MECHANICAL IMPACT AND WINKLER-PASTERNAK FOUNDATION

The simultaneous effects of mechanical impact and Winkler-Pasternak foundation on the dynamic response of an Euler-Bernoulli beam are studied. By means of the Galerkin-Bubnov procedure, the governing equation with partial derivatives is reduced to an ordinary differential equation. This nonlinear equation is solved by means of the Optimal Homotopy Asymptotic Method (OHAM).

Extending a higher-order foldability constitutive model for dynamic response analysis of 3D-reinforced shell of deformable

Extending a higher-order foldability constitutive model for dynamic response analysis of 3D-reinforced shell of deformable

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.

Numerical investigation of the effect of geometric defects on dynamic frequency responses in body-centered cubic lattice structures

This work presents a numerical study that analyzes the effect of geometric defects on the dynamic frequency responses of a metal block of body-centered cubic (BCC) lattice structures. The study considers 2 types of geometric defects: one involving the variability of the cross-sectional area of the bars that make up the lattice structures, and the other involving the eccentricities of the truss. An algorithm was programed in Python to generate these defects, which were then implemented in the commercial finite-element software Abaqus/Standard. The imperfections were allocated according to a normal distribution, with parameters determined from computed tomography data presented by other authors. The natural frequency of different blocks has been calculated, considering various levels of defects. The results indicate that dynamic frequency response can be a valuable tool for analyzing practical issues related to non-destructive damage identification of imperfections in BCC structures.

Nonlinear Dynamic Response Analysis of Cable–Buoy Structure Under Marine Environment

The nonlinear dynamics of the cable–buoy structure in marine engineering present significant analytical challenges due to the complex motion of the buoy, which impacts the system’s dynamic response. The drag force acting on the structure can be categorized into the absolute velocity and relative velocity models, distinguished by their reference frames. The absolute velocity model incorporates flow velocity coupling terms, offering higher accuracy but at the expense of increased computational complexity. In contrast, the relative velocity model is computationally simpler and therefore more widely adopted. Nevertheless, the accuracy and applicability of these simplified models remain open to further in-depth investigation. To address these challenges, this study derives coupled differential equations for the cable–buoy structure based on the two drag force models. Galerkin discretization is then employed to construct coupled systems that account for nonlinear buoy motion, as well as decoupled systems assuming linear buoy motion. The modulation equations for the system’s primary resonance response are derived using the method of multiple scales. Numerical results indicate that changes in cable parameters lead to complex modal coupling behaviors in the system. The flow velocity coupling terms in the absolute velocity drag force model enhance the system’s damping effect, and the relative velocity drag force model, which omits these coupling terms, results in increased system response amplitudes. Although neglecting nonlinear buoy motion has little impact on the cable’s dynamic response, it significantly reduces the amplitude of the buoy’s dynamic motion. The relative velocity drag force model and the decoupled system can serve as effective simplifications for analyzing the dynamic responses of cable–buoy systems, providing a balance between computational efficiency and result accuracy. Variations in system parameters cause both qualitative and quantitative changes in the system’s nonlinear stiffness characteristics.