Dynamic Behavior Research Articles

Household residential energy choices in green transition: insights from a household survey in rural China.

It has been widely recognized that accelerating green residential energy transition from traditional solid fuels (biomass and coal) to clean and high-efficient energy sources is critical for rural sustainable development. However, little attention has been paid to estimate panel data discrete choice models to analyze the dynamic behavior information of individual households in the process of energy transition. Hence, this paper investigates green residential energy transition using a panel dataset from 3308 rural households in eight provinces of China in 2015 and 2018. The results show that although traditional solid biomass still plays a dominating role in rural residential energy choice, fuel switching from solid fuels to modern clean energy alternatives is taking place. Off-farm employment plays an important role in the transition towards more sustainable energy sources, as households with off-farm employed heads and higher off-farm income level are highly likely to choose superior energy alternatives other than traditional biomass. Besides, the educational level of the household head and household location are also important influencing factors of household residential energy choices. Based on these findings, this paper suggests that job creation in non-farm sectors should be given a priority in future policy design to promote rural green energy transition in residential sector. North-south differences should be taken into account, whereas more policy options for optimizing biomass energy use should be explored.

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.

Longitudinal vibrations and their suppression of a tethered satellite system using friction self-excitation mechanism

This paper proposes a friction self-excitation mechanism to regulate the longitudinal vibrations of the tether during the state-keeping phase of a two-body tethered satellite system. A simplified dynamic model is established, consisting of two mass blocks, a constant-length tether, and a conveyor belt. The Stribeck friction model is utilized to describe the effects of friction on the stability of longitudinal vibration. Numerical simulations reveal the instability phenomenon induced by Hopf bifurcation and indicate the potential self-excited vibration under different parameters. Additionally, the paper explores the design approach for the tether by introducing the concept of segmented tethering and its dynamic behavior under different design scenarios. Varying the stiffness of the tether can change the vibration modes of the system. Finally, a ground experiment is carried out to evaluate the dynamic behaviors of the system and to demonstrate the effectiveness of the proposed vibration suppression mechanism.

Optical and Chiroptical Stimuli-Responsive Chiral AgNPs@H-Leu-Poly(phenylacetylene) Nanocomposites in Water.

Dynamic macroscopically chiral nanocomposites are prepared by combining silver nanoparticles (AgNPs) and dynamic helical poly(phenylacetylene)s (PPAs) bearing pendants functionalized with amino groups. These amino groups provide the nanocomposite with the ability to disperse in water along with high stability due to the interaction between the ammonium group and the AgNP. Moreover, the equilibrium between NH3+/NH2 produces a "blinking" contact between the PPA and the AgNPs, which allows total control of the dynamic helical behavior of the polymer. The use of acidic or neutral pH allows controlling the morphology of the nanocomposite, which consists of a nanosphere that has trapped inside it a single AgNP (pH = 2) or several AgNPs (pH = 7) with ca. 30 nm of diameter. These nanocomposites combine the optical and chiroptical stimuli-responsive properties of both components, AgNPs and PPAs. Thus, the controlled aggregation of the nanocomposite produced variations in the LSPR band of the AgNPs in a reversible manner. In turn, given that the chiral coating is selective to Ba2+, the presence of this metal ion caused a helical inversion of the chiral coating of the nanocomposite detected by electronic circular dichroism. Moreover, it is possible to distinguish between three metal ions in different oxidation states, such as Ce4+, Fe3+, and Hg2+, which produce different responses of the nanocomposite when oxidizing the AgNP to Ag+.

Hydrogel Embedding Enables Enhanced Leaf Deposition and Bioavailability of Fe-Based Engineered Nanoparticles.

Nanofertilizers comprising engineered nanoparticles (ENPs) have great potential in sustainable agriculture due to their strong capabilities of improving crop yields. As an effective fertilization strategy, foliar spraying could lead to broken and splashed ENP droplets, resulting in inaccurate leaf targeting and potential environmental contamination. Herein, we propose embedding Fe-based ENPs into a supramolecular hydrogel to effectively enhance the deposition amount on leaves and thus the bioavailability. The proper rheological properties of the hydrogel droplets and their robust interaction with soybean leaf simultaneously reduce the droplet rebound and fragmentation, especially under elevated impact speeds, resulting in up to 168.9% more droplet deposition compared to the ENP suspension. Computational fluid dynamics simulation analysis suggests that the contact angle is a key sensitive factor influencing the dynamic deposition behavior of the hydrogel droplet. A 15% reduction in the contact angle results in a 14% reduction of the highest bouncing height. The incorporation of ENPs enhances the viscous dissipation rate by 7.4% in comparison with pure hydrogel droplets. The hydrogel embedding also causes a 1.5-fold increase in ENP uptake compared to that of the ENP suspension. The hydrogel embedding delivers a reduction of 80% in the ENP application amount, compared to ENP suspensions, while achieving a 28% increase in the fresh weight of soybean seedlings. This work provides an effective method to enhance the deposition of ENPs during foliar application.

Periodic and Quasi-Periodic Orbits in the Collinear Four-Body Problem: A Variational Analysis

This paper investigated the periodic and quasi-periodic orbits in the symmetric collinear four-body problem through a variational approach. We analyze the conditions under which homographic solutions minimize the action functional. We compute the minimal value of the action functional for these solutions and show that, for four equal masses organized in a linear configuration, these solutions are the minimizers of the action functional. Additionally, we employ numerical experiments using Poincaré sections to explore the existence and stability of periodic and quasi-periodic solutions within this dynamical system. Our results provide a deeper understanding of the variational principles in celestial mechanics and reveal complex dynamical behaviors, crucial for further studies in multi-body problems.

Two-state free-volume model for anomalous dynamics in supercooled water macromolecules undergoing liquid–liquid phase transition

Modelling anomalous dynamics of complex liquid water is a huge challenge due to its various condensed molecular structures and the associated liquid–liquid phase transitions (LLPTs). In this study, we considered, for the first time, the influences of free volume and entropy on LLPTs for both low-density liquid (LDL) and high-density liquid (HDL) waters. Firstly, we proposed a two-state free-volume model and combined with the Adam-Gibbs models to investigate the dynamic equilibria with free diffusion coefficient, viscosity, glass transition temperature and the anomalous dynamics in the two-state water. Free-energy equation was then developed to explore the constitutive relationships of free volume, entropy and anomalously dynamic behaviors. Finally, analytical results of the proposed two-state free-volume model were compared with the experimental data of two-state water reported in literature, and good agreements between them were demonstrated. This study offers a new physical insight into free volume for the anomalous dynamics and LLPTs in the two-state water.

Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform

ABSTRACTPhotovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital to prevent further damage, improve maintenance strategies, and ensure uninterrupted energy production. In this study, we propose a novel fault detection method based on Time Frequency Analysis (TFA) using the Scaling Basis Chirplet Transform (SBCT). In this proposed fault detection method, PV array signal is decomposed into a set of chirplets using the SBCT. The chirplets represent localized time‐frequency components that can capture the dynamic behavior of the PV array signal. To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. The proposed method achieves high accuracy and robustness in detecting various types of faults in PV arrays, even in the presence of noise and uncertainties. The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses. The proposed approach can contribute to enhancing the overall performance, reliability, and lifespan of PV arrays, thereby advancing the adoption of renewable energy sources and promoting sustainable development.

Extreme multi-stability and circuit implementation for a two-ReLU-memristor-based jerk oscillator

Memristor-based oscillation circuits are prone to produce coexisting infinite attractors depending on the initial conditions of memristors, leading to the appearance of extreme multi-stability. In this paper, we propose a novel memristive jerk oscillator by bringing two ReLU-type memristors in a simple jerk oscillator and investigate its dynamical behaviors associated with the coupling parameters using bifurcation plots and Lyapunov exponent plots. Further, we discuss the planar equilibrium state and its stability, and then numerically explore the coexisting infinite attractors driven by the initial conditions of two ReLU-type memristors. Because of the intervention of the two ReLU-type memristors, the memristive jerk oscillator has a planar equilibrium state whose stability closely relies on the initial conditions of two ReLU-type memristors, and different initial conditions cause different attractors to coexist, resulting in bidirectional extreme multi-stability. Finally, the memristive jerk oscillator is implemented by analog circuit and digital hardware platform, and the numerical results are confirmed by circuit simulations and hardware experiments.

Structural damage detection for spatial frame structures with semi-rigid joints using multiple set wireless measurements

Damage detection of a complex spatial frame structure with semi-rigid joints is a challenging task. Most existing damage detection methods consider the joint as rigid and the nonuniform cross section of the member is not considered. This assumption results to a large error in the structural damage identification. A novel generic element is proposed for the nonuniform cross-section member with semi-rigid joints at both ends in this study. The finite element model of spatial structures with semi-rigid joints is established using the proposed element. The modal strain energy-based damage index is defined and its sensitivity to the member and joint damage has been investigated using numerical and experimental study. An 8-m long bridge model with the bolted connection at joints is built in the laboratory. Dynamic responses of the bridge under random excitations are monitored using 13 wireless tri-axis accelerometers. The spatial mode shapes of the bridge are extracted using the reference-based stochastic subspace identification from multiple set wireless measurements. The numerical model is validated using experimental results. Dynamic responses of the bridge with different damage scenarios have been simulated using the validated model and the corresponding damage indices are obtained. The results show that the proposed method is reliable and accurate to analyze dynamic behavior of the spatial structure with semi-rigid joints, and structural damage can be identified accurately using the modal strain energy-based damage index.

Capture behavior of self-propelled particles into a hexatic ordering obstacle

Abstract Computer simulations are utilized to investigate the dynamic behavior of self-propelled particles (SPPs) within a complex obstacle environment. The findings reveal that SPPs exhibit three distinct aggregation states within the obstacle, each contingent on specific conditions. A phase diagram outlining the aggregation states concerning self-propulsion conditions is presented. The results illustrate a transition of SPPs from a dispersion state to a transition state as persistence time increases within the obstacle. Conversely, as the driving strength increases, self-propelled particles shift towards a cluster state. A systematic exploration of the interplay between driving strength, persistence time, and matching degree on the dynamic behavior of self-propelled particles is conducted. Furthermore, an analysis is performed on the spatial distribution of SPPs along the y-axis, capture rate, maximum capture probability, and mean-square displacement. The insights gained from this research serve as valuable contributions to understanding the capture and collection of active particles.

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Abstract Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant 7D neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum and two-dimensional dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

Multifunctionality of two-dimensional sheets actuated by chemical pumps and motors

AbstractBased on computational modeling, the interaction between catalyst-coated surfaces and flexible micro-to-mesoscale two-dimensional (2D) sheets in solution promotes the self-organization of individual sheets into complex three-dimensional (3D) dynamic forms, including intertwined, self-spinning structures, coupled oscillators and motile, shape-shifting layers. These findings can hasten the development of active, reconfigurable interfaces needed for human–machine interactions. Furthermore, one sheet that autonomously reconfigures into multiple 3D shapes is beneficial for manufacturing, reducing the need to fabricate a new structure for each separate application. More generally, because the flexible 2D sheets display greater degrees of freedom than the previously studied hard zero-dimensional and one-dimensional particles, these elastic layers can reveal new, dynamic behavior in chemically active systems. Graphical abstract

Investigation on microstructure and dynamic fracture behavior of high-strength steel welded by LAHW under different heat inputs

Investigation on microstructure and dynamic fracture behavior of high-strength steel welded by LAHW under different heat inputs

A molecular dynamics simulation study of EthylChlorophyllide A molecules confined in a SiO2 nanoslit.

This paper investigates the dynamic behavior of EthylChlorophyllide A (EChlideA) molecules in a methanol solution confined within a 4nm silica nanoslit, using molecular dynamics simulations over a duration of 1ms. Three systems, containing 1, 2, and 4 solutes, were studied at 298K. The results demonstrate that EChlideA molecules predominantly adsorb onto the silica surfaces, driven by specific interactions between chlorin ring's methyl group and the hydroxyl groups of the silica. This adsorption leads to stable binding, particularly in less crowded environments, as indicated by the potential of mean force analysis. Higher molecular concentrations, such as those with four EChlideA molecules, introduce variation in binding strength due to molecular aggregation and complex interactions. The orientation analysis reveals that the chlorin ring tends to align parallel to the surface, requiring rotational adjustments during surface diffusion. In addition, solvent coordination around the Mg ion remains consistent under bulk conditions, although with some variation in higher concentrations. This study also highlights a decrease in linear diffusion and an increase in rotational relaxation times for EChlideA molecules within the confined nanoslit, reflecting the influence of molecular concentration and arrangement on their dynamics. These findings provide valuable insights into the role of surface interactions, molecular orientation, and solvent coordination in confined environments, offering implications for the design of nanoscale systems.

Spatiotemporal fluctuation induces Turing pattern formation in the chemical Brusselator

Chemical reactions are embedded in spatiotemporal fluctuations instead of a constant environment. Here, we aimed to assess reaction–diffusion (RD) with dichotomous noise‐controlling system parameters in the Brusselator and examine the effect of these fluctuations on the dynamic behavior of chemical reactions. By performing a multiscale perturbation analysis, we demonstrated that the correlated noise can broaden the Turing region even if molecular memory (autocorrelation time) exists. However, for small noise, short‐term memory promotes Turing instability. The instability of the Brusselator is determined by the noise strength, which belongs to the optimal region if the diffusion coefficient is fixed. Turing pattern selection and stability are also governed by the dynamic character of the amplitude equation, and the entire Turing instability region shifts to the right in the phase space with noise perturbation. Finally, numerical simulations validate the theoretical derivation that correlated noise can amplify Turing pattern formation to maintain distinct patterns.

Sensitivity Analysis in Mathematical Models of the Hypothalamus-Pituitary-Thyroid Axis

Mathematical models are promising and important for advancing the current medical practice in the field of endocrinology. To assess the reliability of the range of mathematical models describing the hypothalamuspituitary- thyroid axis and to establish their applicability in clinical decision support, we conducted a local and global sensitivity analysis of the model. Thyroid regulation in euthyroid and diseased individuals may be quantified and dynamic behavior predicted through mathematical models, thereby revolutionizing the current clinical practice. We investigated the influence of model parameters of a selected mathematical model utilizing ordinary differential equations describing the HPT-axis. Motivated by a graphical depiction of the varying influence of the model parameters, feasible methods such as a local sensitivity analysis are conducted. Furthermore, to account for the influence of parameters on the output variance of the considered model, the theory of Sobol’ indices is utilized. Although the system of differential equations describing the hormone concentrations of thyroid-simulating hormones and unbound Thyroxine has similar equation structures, the results of the sensitivity analyses varied according to the equation.

Comprehensive analysis of thermo-mechanical responses in collapsible soil under varied water content conditions

AbstractIn this research, the effect of both temperature gradients and varied water content on heat transfer in collapsible soil is investigated. The study based on one-dimensional laboratory setup, soil temperature distribution in proximity to a heat source, was examined across four distinct temperatures (ranging from 50 to 200 °C) under varying water content (0%, 10%, 15%, and 20%). Through steady-state conditions and extended measurements over days, data were collected to compare soil thermal conductivity at 10% water content using two different methods. The first method required some of soil characteristics, such as dry density and optimum water content, while the second method relied on heating parameters and supplied heating content. A robust agreement between thermal conductivity values obtained through these two methods was observed. Correlations from experimental data were analyzed to enrich understanding, and multivariate linear regression was employed to predict the thermal conductivity and resistivity of collapsible soils. Results indicated that the higher soil density, the increasing the thermal conductivity, whereas greater soil porosity exhibited the opposite trend. Elevated temperatures were found to enhance soil density, influencing the spatiotemporal distribution of heat within the soil. This research contributes valuable insights into the dynamic behavior of heat transfer in collapsible soil, emphasizing the complex interaction of temperature gradients and water content variations. The findings of this study can advance the development of efficient and sustainable geothermal systems in regions with collapsible soils, potentially enhancing the design and management of structures built on such soils, especially in arid and semi-arid areas.

Nonlinear behavior and energy harvesting performance of a new tunable quasi-zero stiffness system.

Energy harvesting from vibrations is a popular research topic. However, it is difficult to change the values of its dynamic stiffness characteristics during the operation of device. This paper analyzes the nonlinear dynamical behavior and energy harvesting performance of an energy harvesting device with a gear unit and an inertial amplifier. The Melnikov method is used to discriminate the chaos behavior of the system and is illustrated by numerical simulations. Chaotic dynamics analysis provides a simplified analytical idea that offers more insight into the performance of this energy harvesting device. In addition, the basins of attraction of the coexistence solution of the system are plotted, and the trajectory and energy harvesting efficiency of the system are analyzed for changes in the initial value. The results show that the device has the convenience to change the form and efficiency of system energy harvesting. The theory of Melnikov functions recognizes the presence of chaos and can provide solutions for the parameterization of the system. The choice of initial value greatly affects the energy harvesting efficiency.

Initial monitoring of a six-story lightweight timber frame building under different environmental conditions

AbstractThis study presents a comprehensive investigation of the dynamic behavior of a six-story lightweight timber frame building through periodic and continuous ambient vibration tests. Seasonal changes in temperature and relative humidity can influence the dynamic response of timber buildings, by affecting stiffness, strength, and connection properties due to changes in moisture content. Systematic tests were performed from October 2022 until May 2024 to identify natural frequencies, damping ratios, and mode shapes of the building using five battery-driven data acquisition units equipped with 15 uni-axial accelerometers. Two different only-output frequency and time domain Operational Modal Analysis (OMA) methods were used to evaluate the dynamic properties of the building. Additionally, the building has been continuously monitored with temperature and humidity sensors since its construction. Results obtained from the periodic measurements highlighted the need for a permanent system to capture the transient changes in the modal parameters. A complementary permanent system to record the accelerations at the roof level was installed in May 2024. The modal parameters from in situ measurements were compared with those obtained from the Finite Element (FE) model of the structure. The FE model, calibrated through a multi-stage approach, incorporating non-structural elements, and varying imposed loads, showed good agreement with experimental data. Comparative analysis of the results obtained under different temperature and humidity conditions showed the effect of the environmental conditions on the building dynamics properties, for both periodic and permanent measurements. This study highlights the importance of continuous monitoring and detailed FE modeling in understanding the dynamic properties and long-term structural behavior under varying conditions. This information can be used to ensure that the building meets safety and performance requirements and to identify potential issues that may need to be addressed during the design and maintenance phases.