System Dynamic Behavior Research Articles

Voltage profile enhancement of an AVR system using Ant Lion Optimization algorithm

Abstract This paper addresses voltage regulation, which is a critical issue in power systems. The automatic voltage regulator (AVR) plays a crucial role in maintaining voltage regulation within permissible limits The AVR system's dynamic behaviour is improved by applying the Proportional (KP) Integral (KI) Derivative (KD) (PID) controller. The primary challenges of using a PID controller in AVR include difficulties in tuning parameters to handle nonlinearities, load disturbances, and system dynamics, leading to potential instability, overshoot, or slow response. The controller parameters are tuned with Ant Lion Optimisation (ALO) algorithm and Integral Time Absolute Error (ITAE) as an objective function. Also, the proposed system has the ability to improve the transient response of AVR by decreasing the maximum overshoot, settling time, rise time, and peak time magnitudes of the generator terminal voltage with eliminating the steady state error. Once the suggested method had produced the best values for the three gains i.e., KP, KI, and KD of the PID controller, a transient response analysis is examined and compared with some of the existing techniques such as Bat Algorithm (BA), Grey Wolf Optimisation (GWO) and Biogeography Based Optimisation (BBO) algorithm to highlight the novelty of the proposed ALO optimized PID controller. Transient response analysis, root locus analysis, and bode analysis are used to assess the AVR system's stability. Comparisons to three well-known optimization techniques corroborate the findings. The results show that this new suggested method performs exceptionally well even when there are significant variations in power system parameters and load uncertainties.

Gradient descent based dynamic optimization for VSG dominated microgrid

AbstractAs the integration of distributed generation units into the power grid continues to grow, the system becomes increasingly vulnerable to power fluctuations and potential failures, which significantly challenge grid stability. Virtual synchronous generators (VSGs) have emerged as a key technology to enhance grid stability by emulating the behavior of traditional synchronous generators. However, with multiple VSGs operating within independent microgrids, coordinating their control parameters becomes critical to ensuring stable and reliable operation. This article proposes an optimization method based on the gradient descent algorithm to fine‐tune the control parameters of multiple VSGs in independent microgrids. A small‐signal model for microgrids with multiple VSGs is developed to analyze the system's dynamic behavior under small disturbances. The proposed method optimizes VSG control parameters to improve the frequency and voltage dynamic response and enhance the overall system stability. The simulation results demonstrate that the proposed multi‐VSG control parameter optimization method significantly enhances the frequency and voltage dynamic response, as well as the stability of the independent microgrid under small disturbances, offering a practical solution for improving microgrid performance in real‐world applications.

A review of hydro-turbine unit rotor system dynamic behavior: Multi-field coupling of a three-dimensional model

The dynamic behavior of hydro-turbine rotor system is a complex multi-field and nonlinear problem, which has been studied by many researchers. The analysis of the rotor system dynamic characteristics is usually carried out based on the behavior analysis of bearings, hydraulics, electromagnetics, etc., while the thermo-elasto-hydrodynamic characteristics of bearings are extremely important to numerical accuracy. Therefore, this paper first summarizes the research progress in bearing lubrication performance, and further summarizes the research on hydro-turbine rotor system dynamic characteristics, including the modal characteristics and dynamic response characteristics. Finally, this paper summarizes the main research progress of the hydro-turbine rotor system and proposes possible directions in future research. Literature review shows that the hydro-turbine runners and bearings have achieved multi-field coupling analysis of three-dimensional (3D) models, and some work on multi-field coupling of rotor systems has been carried out. The transition of 3D multi-field coupling from single component to rotor system is significant to accurately predict the rotor system dynamic characteristics and the solution of engineering problems, which requires further in-depth research on the multi-field coupling theory, numerical methods, 3D model integrity, simulation software, etc., and the spatiotemporal synergy between multi-fields should be fully considered.

A model to study the effect of micropitting on the dynamic behaviour of a geared system

Geared transmission systems operate under increasingly severe conditions that tend to promote micropitting on the gear flanks. Micropitting leads to an increased local coefficient of friction, in particular due to an increase in the average roughness of the damaged surfaces. Micropitting is hard to predict, but also hard to detect, since it mainly affects the contact conditions, which affect friction forces. On global signals such as input/output torque, these friction forces are one to two orders of magnitude lower than the normal forces and tend not to be detectable due to the noise in the experimental setup. This paper aims at studying the effect of micropitting on the vibrational levels of a geared system, by coupling the gear dynamics model developed by Osman and Velex with the roughness-dependent coefficient of friction formulation from Xu et al., and by representing micropitting by a local increase of the composite roughness. The simulations show that micropitting has a clear impact on the system dynamic behaviour, especially in the direction perpendicular to the plane of contact. Finally, it was shown that the bearing forces and the pinion acceleration are suitable candidates for micropitting detection, even with noisy signals.

Exploring chaos and sensitivity in the Ivancevic option pricing model through perturbation analysis.

This study explores the Ivancevic Option Pricing Model, a nonlinear wave-based alternative to the Black-Scholes model, using adaptive nonlinear Schrödingerr equations to describe the option-pricing wave function influenced by stock price and time. Our focus is on a comprehensive analysis of this equation from multiple perspectives, including the study of soliton dynamics, chaotic patterns, wave structures, Poincaré maps, bifurcation diagrams, multistability, Lyapunov exponents, and an in-depth evaluation of the model's sensitivity. To begin, a wave transformation is applied to convert the partial differential equation into an ordinary differential equation, from which soliton solutions are derived using the [Formula: see text] method. We explore various forms of the option price function at different time points, including singular-kink, periodic, hyperbolic, trigonometric, exponential, and complex solutions. Furthermore, we simulate 3D surface plots and 2D graphs for the real, imaginary, and modulus components of some of the obtained solutions, assigning specific parameter values to enhance visualization. These graphical representations offer valuable insights into the dynamics and patterns of the solutions, providing a clearer understanding of the model's behavior and potential applications. Additionally, we analyze the system's dynamic behavior when a perturbing force is introduced, identifying chaotic patterns using the Lyapunov exponent, Sensitivity, multistability analysis, RK4 method, wave structures, bifurcation diagrams, and Poincaré maps.

A Novel Event-triggered Practical Prescribed-Time Control for four Complex coupled Duffing-type MEMS resonators with Prescribed Performance

This paper explores the dynamic characteristics and a novel event-triggered practical prescribed-time controller for four complex coupled Duffing-type MEMS resonators. Initially, the effects of mechanical coupling stiffness, electrostatic coupling stiffness, and internal system parameters on the system's dynamic behavior are examined. The analysis results provide guidance for selecting system parameters. Furthermore, the dynamic analysis reveals that chaotic oscillations may occur in the system without input, significantly affecting system performance. To mitigate these chaotic oscillations, a novel event-triggered practical prescribed-time controller with prescribed performance is proposed, utilizing interval type-3 fuzzy system (IT3FS) to estimate unknown nonlinear functions. By employing a more relaxed practical prescribed-time stability (PPTS) criterion and a novel time-varying scale transformation function (STF), the designed controller ensures that all signals converge within a prescribed time. Additionally, a prescribed performance function (PPF) is employed, allowing for more flexible convergence boundary settings. A dynamic event-triggered mechanism is implemented to reduce the frequency of control signal updates. The stability analysis demonstrates that all signals converge within a prescribed time, and the tracking errors remain within their prescribed boundary. Finally, simulations validate the effectiveness of the proposed scheme.

Pattern‐moving‐based dynamic description and optimal control for non‐Newtonian mechanical systems with generalized cell mapping

AbstractThis article focuses on the problem of modelling and control for a non‐Newtonian mechanical system that may be subject to the statistical law instead of the traditional Newtonian principles mechanics. A discrete method, synthesizing the generalized cell mapping (GCM) together with dynamic programming (DP), with inverse search strategy, using pattern moving theory (PMT) framework, was proposed. The basic idea is to describe and control the system's dynamic peculiarity utilizing pattern class variable in ‘pattern moving space’. First, a few prior concepts were reviewed, including PMT, cell mapping, and optimal control for this kind system. Then, we articulated a cross‐mapping method to analyze the system dynamic behaviour, which takes into account the computational and statistical properties of pattern class variable simultaneously. For system optimal control, the improved GCM and cost function were performed to determine the discrete optimal control table (DOCT) in accordance with dynamic programming and inverse search. Finally, simulation results of two cases demonstrate the efficiency and practicality of the proposed approach. The study has resulted in a solution of describing and controlling based on pattern class variable for non‐Newtonian mechanical systems, and its main objective was to give a different perspective in term of research into non mechanistic principles modelling and application of nonlinear systems.

Advanced piezoelectric fluid energy harvesters by monolithic fluid–structure–piezoelectric coupling: A full-scale finite element model

A full-scale finite element model is presented for monolithic fluid–structure interaction (FSI) simulations of thin-walled piezoelectric fluid energy harvesters (PFEHs). Unlike widely used beam/plate-based models, our model employs a solid finite element discretization to precisely represent the complex PFEH designs involving microstructured transducers and non-uniform cantilevers. These features, plus the local FSI effects, are often ignored by simplified models. We applied the Galerkin method to formulate the weak form of the mixed equation system, integrating the flow dynamics, the geometrically nonlinear cantilever, the piezoelectric components, the electrode, and the output circuit within a closed-circuit electro-mechanical coupled system. The coupling of the multiple domains is achieved through boundary-fitted discretization within a monolithic scheme, using shifted-Crank–Nicolson temporal integration. This work explored implementing piezoelectric FSI systems within the FEniCS-based TurtleFSI library, and experimented techniques such as employing penalty functions for achieving electrode components with uniform electric potentials. We investigated various advanced PFEH features, including the baseplate design, the arrangement and microstructure of the piezoelectric components, and their influence on the system's dynamic and energy output behavior. The results confirmed the model's key advantages: full-scale modeling allows the integration of complex base structures and transducer microstructures in PFEH design. Combined with monolithic FSI coupling, it offers greater versatility, supporting a wider range of fluid environments and configurations in both wind and hydropower harvesting. Additionally, the modeling strategy can be intended not only to enhance power output, but also to minimize material usage, reduce mechanical fatigue, and extend the operational lifespan of PFEH systems.

Research on the Efficacy of Markov Chains in Analyzing and Predicting Chemical Reaction Rates

Abstract. The Markov chain is a powerful mathematical model used to describe stochastic processes in which the probability of each event depends only on the state attained in the previous event. The chemical reaction rate is a measure of how quickly or slowly a chemical reaction proceeds in multiple stages. This research paper uses the case study of enzymes to investigate the efficacy of the Markov chain in analyzing and predicting chemical reaction rates. By modeling chemical reaction pathways using Markov chains, this study tries to explore how Markov chains help capture and predict a chemical systems dynamic behavior. Scientists have been using multiple methods to predict chemical reaction rates, which involve methods like the Arrhenius equation, transition state theory, molecular dynamics, quantum mechanics, and machine learning. In conclusion, after comparing the advantages and disadvantages of the Markov chain predicting method with those methods above, Markov chains did stand out and provide valuable insights into the understanding of reaction dynamics.

Leaching Efficiency During Autumn Irrigation in China’s Arid Hetao Plain as Influenced by the Depth of Shallow Saline Groundwater and Irrigation Depth, Using Data from Static Water-Table Lysimeters and the Hydrus-1D and SIMDualKc Models

The need for controlling salinity in arid zones is essential for sustainable agricultural production and irrigation water use. A case study performed for two years in Hetao, Inner Mongolia, China, is used herein to rethink the contradictory issues of arid lands represented by water saving and controlling soil and water salinity. Two sets of static lysimeters, where water table depths (WTDs) were fixed at 1.25, 150, 2.00, and 2.25 m, were continuously monitored, and soil water and solute data were used to calibrate and validate two models: the soil water balance model SIMDualKc and the deterministic soil water and salt dynamics model HYDRUS-1D. Once accurately calibrated, the models were used to simulate maize water use, percolation, and capillary rise, along with the observed variables for the actual WTD and the autumn irrigation applied. Simulation scenarios also considered agricultural system degradation and dynamic water table behavior. Results have shown that large leaching efficiencies (Lefs) were obtained for large irrigation depths in cases of shallow water tables, but higher Lefs corresponded to high application depths when the water table was deeper. Agricultural system degradation, particularly increased groundwater salinity, lowered Lef, regardless of WTD. Conversely, water savings were minimal and only achievable when considering the dynamic nature of groundwater. These results indicate that there is a need to define different WTDs based on soil characteristics that influence fluxes and root zone storage, as well as the impacts of newly installed drainage systems aimed at salt extraction.

Performance evaluation of a hybrid photovoltaic-vapor compression system serving a refrigerated van

The refrigerated transportation industry's growth necessitates addressing energy consumption and greenhouse gas emissions. This study estimates a photovoltaic system's energy and environmental benefits to power a vapour compression refrigeration (VCR) system serving a light-duty commercial refrigerated van. A comprehensive energy model encompassing thermal, electrical, and battery sub-models simulating the system's dynamic behaviour is calibrated with real-world data referring to an urban single-delivery mission. The potential benefits are estimated for a long-distance single-delivery mission starting from the University of Salerno (Italy) and ending at the Jaume I University in Castellon de la Plana (Spain). The results have shown that the system can reduce fuel consumption for refrigeration by more than 88 % during summer months and allows neutral refrigeration during winter months, leading to on-wheel emission savings between 4 and 8 gCO2,e/km. When considering total emissions, including electrical energy from the power grid and the increased weight due to the PV system, a 33–47 % reduction is obtained, corresponding to 1–5 gCO2,e/km. In detail, PV panels can cover up to 19 % of the total energy requirements. In economic terms, the system allows a cost-saving between 0.1 and 0.3 c€/km. The low complexity and promising results suggest the hybrid PV solution is a viable path towards decarbonising refrigerated transport.

Dynamic Analysis of Geared Rotor System with Hybrid Uncertainties

Current research on the dynamics and vibrations of geared rotor systems primarily focuses on deterministic models. However, uncertainties inevitably exist in the gear system, which cause uncertainties in system parameters and subsequently influence the accurate evaluation of system dynamic behavior. In this study, a dynamic model of a geared rotor system with mixed parameters and model uncertainties is proposed. Initially, the dynamic model of the geared rotor-bearing system with deterministic parameters is established using a finite element method. Subsequently, a nonparametric method is introduced to model the hybrid uncertainties in the dynamic model. Deviation coefficients and dispersion parameters are used to reflect the levels of parameter and model uncertainty. For example, the study evaluates the effects of uncertain bearing and mesh stiffness on the vibration responses of a geared rotor system. The results demonstrate that the influence of uncertainty varies among different model types. Model uncertainties have a more significant than parametric uncertainties, whereas hybrid uncertainties increase the nonlinearities and complexities of the system’s dynamic responses. These findings provide valuable insights into understanding the dynamic behavior of geared system with hybrid uncertainties.

Complex network-based multistep forecasting model for hyperchaotic time series.

We present a method for predicting hyperchaotic time series using a complex network-based forecasting model. We first construct a network from a given time series, which serves as a coarse-grained representation of the underlying attractor. This network facilitates multistep forecasting by capturing the local nonlinearity of the dynamics and offers superior accuracy over more extended periods than traditional methods. The network is formed by converting the patterns of local oscillations into sequences of numerical symbols, which are then used to create nodes and edges in a network, capturing the system's dynamical behavior at a reduced resolution. The network allows predictions up to several steps ahead without the exponential error increase usually associated with linear first-order methods. The improved predictions result from the unique ability of the network to collect identical pattern transitions in the orbit dynamics into a system of neighborhoods in the network. The effectiveness of this approach is demonstrated through its application to several high-dimensional hyperchaotic systems, where it outperforms both the linear first-order and other network-based methods in terms of prediction accuracy and horizon. Besides enhancing the predictability of chaotic systems, this methodology also outlines a procedure to develop a discrete model flow within an attractor.

A high-frequency reduced-order model for parameters optimization of AC/DC distribution systems considering random disturbances

Accompanying the rapid development of AC/DC distribution systems, along with the random disturbances introduced by a significant number of distributed generations and loads, the difficulty of system stability analysis has increased. The phenomenon of DC bus voltage instability has become severe, affecting the safe and stable operation of power systems. This paper focuses on the research of multi-terminal AC/DC distribution systems. A reduced-order method for multi-terminal distribution systems is proposed. Through parameter equivalent transformation, the system model is simplified while maintaining its dynamic characteristics. Parameter sensitivity analysis is utilized to identify key parameters influencing the system’s dynamic behavior, achieving an analytical expression of the system’s dynamic characteristics. And stability domain analysis is applied to delineate the variations in system dynamic behavior and responses to random disturbances under different parameter settings. Then a multi-parameter optimization method is designed to enhance system stability while achieving fast system response. Finally, theoretical analysis is validated through software simulation and hardware-in-the-loop experiments.

Cerium single atom anchored silver selenide: A high-performance catalyst for hydrogen evolution reaction with ultra-low activation energy and enhanced stability

Single-atom catalysts (SAC) have enabled high electrocatalytic activity production because of the enormous active sites bearing catalysts for hydrogen evolution reaction (HER). Herein, we report cerium single atom (Cesa) anchored silver selenide (Ag2Se) via metal-chalcogenide interaction for high-performance HER evolution. The designed electrocatalyst possesses extraordinary electrochemical and optical properties, thus the Cesa-Ag2Se could be a potential catalyst for the photo-electrochemical HER process. The sluggish diffusion rate of the Ce atom within the Ag2Se facilitates the high stability and the dispersed Cesa displayed ∼2 eV of energy differences with the Ce dimer proving the formation of dispersed Cesa over Ag2Se is potentially favorable. The minimum energy pathway (MEP) suggested the Cesa-Ag2Se system established only 0.003 eV as an ultra-low activation energy barrier in order to produce H2, which is by far the finest performance of Ag2Se-based thermodynamically favourable catalysis. The accumulated electron density due to the presence of Cesa enabled the presence of a large number of potential active sites (Ce and Ag) for hydrogen adsorption, in addition, the supportive zero band gap of the Ag2Se accelerated the kinetics for HER. Ultimately, we employed the Wentzcovitch cell dynamics-based molecular dynamics study for H* adsorbed Cesa-Ag2Se that suggests the stability of the catalyst for 500 fs. The in-depth HER modelling using the Lennard-Jones force field demonstrated the insightful dynamics behaviour of the catalytical system.

Dynamic characteristics analysis and vibration control of coupled bending-torsional system for axial flow hydraulic generating set

Aiming at the vibration problems of shaft system for axial flow hydraulic generating sets caused by complex excitations, the coupled bending-torsional dynamic equations of rotor-runner system are established considering multi-effect including hydraulic, mechanical, electrical and other external vibration sources. Whether taking the torsional degree of freedom into account, the stability and disparate destabilization laws of system periodic solutions are analyzed using the shooting method combined with Floquet theory. On this basis, the influence of coupling bending-torsional effect on system dynamic behaviors is discussed through bifurcation diagrams, Poincaré maps, trajectories, time domains and frequency spectrums. Furthermore, in order to alleviate the unsteady vibration of rotor as well as runner, the magnetorheological fluid damper is introduced into the shaft system, and numerical simulations are performed on models of coupled bending-torsional systems with or without the magnetorheological fluid damper. The analyses indicate that the interaction between bending and torsional vibration of the shaft system will expand the chaotic sustained range for rotor. Meanwhile, the amplitude of the rotor-runner system is magnified owing to the torsional vibration, which leads to local rubbing faults of the system under specific operating conditions, while the intervention of the magnetorheological fluid damper can significantly suppress the nonlinear motion of rotor and runner, reduce the amplitude of system vibration, so as to avoid the occurrence of friction defects effectively. The above research results can provide conducive references for fault diagnosis, optimized design, and vibration control of axial flow hydraulic generating sets.

Parameter identification of overhead conductor rail support and its influence on the contact forces

Overhead conductor rail (OCR) serves as a crucial device employed in tunnels to power electric locomotives. The OCR is hung by supports mounted on the tunnel ceiling. Previously, the support was usually considered as a linear spring when modelling the OCR. The linear stiffness and mass adopted to the support model were imprecise, and damping was absent. It may be insufficient to reproduce the pantograph-OCR system (POCR) dynamic behaviour. This paper is devoted to improving the accuracy of POCR dynamic model and makes it more realistic. Field tests are conducted in order to identify the support parameters, including stiffness, damping and mass, for the first time. Then, a novel nonlinear stiffness-damping-mass support model is developed utilising the identified parameters and verified by comparing simulation results with measurement data. Finally, in pursuit of improving current collection quality, the effects of support damping and train speeds on contact forces are investigated. The results show that the support damping can mitigate the OCR’s vibration. The increase of support damping within a specific range, 0–200 Ns/m, has a positive effect on the current collection quality.

Impact of environmental stochastic fluctuations on the evolutionary stability of imitation dynamics.

To show the impact of environmental noise on imitation dynamics, the stochastic stability and stochastic evolutionary stability of a discrete-time imitation dynamics with random payoffs are studied in this paper. Based on the stochastic local stability of fixation states and constant interior equilibria in a two-phenotype model, we extend the concept of stochastic evolutionary stability to the stochastic imitation dynamics, which is defined as a strategy such that, if all the members of the population adopt it, then the probability for any mutant strategy to invade the population successfully under the influence of natural selection is arbitrarily low. Our main results show clearly that the stochastic evolutionary stability of the system depends only on the properties of the mean matrix of the random payoff matrix and is independent of the randomness of the random payoff matrix. Moreover, as two examples, we show also that under the framework of stochastic imitation dynamics, the noise intensity affects the evolution of cooperative behavior in a stochastic prisoner's dilemma game and the system's nonlinear dynamic behavior.

The Light-Fueled Self-Rotation of a Liquid Crystal Elastomer Fiber-Propelled Slider on a Circular Track.

The self-excited oscillation system, owing to its capability of harvesting environmental energy, exhibits immense potential in diverse fields, such as micromachines, biomedicine, communications, and construction, with its adaptability, efficiency, and sustainability being highly regarded. Despite the current interest in track sliders in self-vibrating systems, LCE fiber-propelled track sliders face significant limitations in two-dime nsional movement, especially self-rotation, necessitating the development of more flexible and mobile designs. In this paper, we design a spatial slider system which ensures the self-rotation of the slider propelled by a light-fueled LCE fiber on a rigid circular track. A nonlinear dynamic model is introduced to analyze the system's dynamic behaviors. The numerical simulations reveal a smooth transition from the static to self-rotating states, supported by ambient illumination. Quantitative analysis shows that increased light intensity, the contraction coefficient, and the elastic coefficient enhance the self-rotating frequency, while more damping decreases it. The track radius exhibits a non-monotonic effect. The initial tangential velocity has no impact. The reliable self-rotating performance under steady light suggests potential applications in periodic motion-demanding fields, especially in the construction industry where energy dissipation and utilization are of utmost urgency. Furthermore, this spatial slider system possesses the ability to rotate and self-vibrate, and it is capable of being adapted to other non-circular curved tracks, thereby highlighting its flexibility and multi-use capabilities.

The dynamical analysis of simplicial SAIS epidemic model with awareness programs by media

In this paper, we propose an SAIS epidemic model with awareness programs by media on higher-order networks to study the impact of media and higher-order networks on disease transmission, in which the simplicial complex is utilized to construct a social network that describes connections between nodes, where a single link can connect more than two individuals. We use the cumulative density of awareness programs to quantify the impact of media and use the Microscopic Markov Chains method to get the probability evolution equation of each node. In order to solve the computational difficulties caused by the excessively high dimension of the equation, we use the mean field method to reduce the dimension of the equation to get the equilibrium of the system. Through dynamical analysis, we obtain the threshold of disease outbreak and conditions for the existence and stability of the disease-free equilibrium and the endemic equilibrium. The numerical simulations of SAIS model prove that the mean field method has good predictive performance. We also obtain the influence of parameters on threshold and system dynamical behavior through sensitivity analysis. The results show that: on one hand, the introduce of 2-simplex increases the probability of disease transmission in healthy populations, and the system appears discontinuous transition and bistable coexistence of health state and disease epidemic state, on the other hand, awareness programs by media can increase the threshold of disease outbreak, decrease the final density of infected individuals and inhibit the explosive growth of disease, thus effectively control the spread of diseases.