Dynamic Behavior Research Articles

Longitudinal dynamic behavior study of a vibrating rod connected through an elastic nonlinear single degree freedom coupler

This research explores the efficacy of integrating nonlinear single-degree-of-freedom systems in the vibration control of rod coupling systems. By interlinking two rods with a nonlinear single-degree-of-freedom system as the intermediary, the study employs the Lagrange method (LM) to forecast nonlinear vibrational behaviors. The findings, substantiated by numerical analyses, affirm the precision of LM in gauging the amplitude responses when such a nonlinear system is utilized. The nonlinear dynamics, characterized by intricate vibrational patterns, peak jumping phenomena, and the migration of resonance zones, are induced by the nonlinear single-degree-of-freedom system. By fine-tuning the system’s parameters, significant alterations in the vibrational states of the rod coupling system are achievable. This suggests that the application of a nonlinear single-degree-of-freedom system is a viable strategy for modulating vibrations in rod systems. Furthermore, optimal parameterization of this system is proven to effectively dampen vibrations, showcasing its potential as a sophisticated mechanism for vibration suppression in coupled rod configurations.

Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning

ABSTRACT BACE1 has been regarded as an essential drug design target for treating Alzheimer’s disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (MM-GBSA) method are integrated to elucidate the molecular mechanism underlying the effect of D93 and D289 protonation on binding of inhibitors OV6 and 4B2 to BACE1. The GaMD trajectory-based DL successfully identifies significant function domains. Dynamic analysis shows that the protonation of D93 and D289 strongly affects the structural flexibility and dynamic behaviour of BACE1. Free energy landscapes indicate that inhibitor-bound BACE1s have more conformational states in the protonated states than the wild-type (WT) BACE1, and show more binding poses of inhibitors. Binding affinities calculated using the MM-GBSA method indicate that the protonation of D93 and D289 highly disturbs the binding ability of inhibitors to BACE1. In addition, the protonation of two residues significantly affects the hydrogen bonding interactions (HBIs) of OV6 and 4B2 with BACE1, altering their binding activity to BACE1. The binding hot spots of BACE1 recognized by residue-based free energy estimations provide rational targeting sites for drug design towards BACE1. This study is anticipated to provide theoretical aids for drug development towards treatment of AD.

Constructing representative group networks from tractography: lessons from a dynamical approach

Human group connectome analysis relies on combining individual connectome data to construct a single representative network which can be used to describe brain organisation and identify differences between subject groups. Existing methods adopt different strategies to select the network structural features to be retained or optimised at group level. In the absence of ground truth, however, it is unclear which structural features are the most suitable and how to evaluate the consequences on the group network of applying any given strategy. In this investigation, we consider the impact of defining a connectome as representative if it can recapitulate not just the structure of the individual networks in the cohort tested but also their dynamical behaviour, which we measured using a model of coupled oscillators. We applied the widely used approach of consensus thresholding to a dataset of individual structural connectomes from a healthy adult cohort to construct group networks for a range of thresholds and then identified the most dynamically representative group connectome as that having the least deviation from the individual connectomes given a dynamical measure of the system. We found that our dynamically representative network recaptured aspects of structure for which it did not specifically optimise, with no significant difference to other group connectomes constructed via methods which did optimise for those metrics. Additionally, these other group connectomes were either as dynamically representative as our chosen network or less so. While we suggest that dynamics should be at least one of the criteria for representativeness, given that the brain has evolved under the pressure of carrying out specific functions, our results suggest that the question persists as to which of these criteria are valid and testable.

HOST‐GUEST INTERACTIONS FACILITATED BY CHALCOGEN BONDING WITHIN SELENADIAZOLE FUNCTIONALISED PORPHYRIN NANOTUBES

The structural rigidity of tetrakis(4‐pyridyl)porphyrin (TPyP) has been utilised to prepare a robust novel porous coordination polymer of composition Cd2(TPyP)(sez)2 (TPyP = 5,10,15,20‐tetra(4‐pyridyl)porphyrin, sez = 1,2,5‐benzoselenadiazole‐5‐carboxylate). The coordination polymer may be described as a hexagonal porphyrin nanotube (PNT) and has the potential to bind guest molecules through chalcogen bonding. Single crystal X‐ray diffraction (SCXRD) data indicate an internal pore diameter ~9 Å which represents ~35% of the crystal volume. Immersion of the PNTs in solvents such as DMSO and CS2 result in the incorporation of these molecules within the nanotubes with chalcogen bonding between host and guest. The crystallographic guest‐inclusion investigations are complemented by solid‐state 77Se, 13C, 113Cd and 2H NMR studies which provide insights into dynamic behaviour. The porosity of the crystals was further explored using gas adsorption experiments, indicating the reversible uptake of CO2, CH4, H2 and N2. Structure‐function relationships are clearly established from complementary crystallographic, NMR and adsorption investigations.

Comment on “Dynamical behavior and modulation instability of optical solitons in nonlinear directional couplers”

Comment on “Dynamical behavior and modulation instability of optical solitons in nonlinear directional couplers”

Adaptive Remaining Capacity Estimator of Lithium-Ion Battery Using Genetic Algorithm-Tuned Random Forest Regressor Under Dynamic Thermal and Operational Environments

The increasing interests and recent advancements in artificial intelligence and machine learning have significantly accelerated the development of novel techniques for the state estimation of batteries in electrified vehicles’ battery management systems (BMSs). Determining the remaining capacity among the several BMS states is crucial for ensuring the safe and stable functioning of an electric vehicle. This paper proposes an adaptive estimator for the remaining capacity of lithium-ion batteries, leveraging a Genetic Algorithm (GA)-tuned random forest (RF) regressor. The estimator is designed to function effectively under varying thermal conditions. The optimization of critical parameters, namely, the number of estimators (n-estimators) and the minimum number of samples per leaf (min-samples-leaf), is a focal point of this study to enhance model accuracy and robustness. The model effectively captures the battery’s dynamic behavior and inherent non-linearity. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) achieved during testing demonstrate promising accuracy and superior prediction. The results demonstrated significant improvements in state of charge (SOC) estimation accuracy. The proposed GA-optimized RF model achieved an MAE of 0.0026 at 25 °C and 0.0102 at −20 °C, showing a 41.37% to 50% reduction in the MAE compared to traditional random forest models without GA optimization. The RMSE was also reduced by 18.57% to 31.01% across the tested temperature range. These improvements highlight the model’s ability to accurately estimate the SOC in varying thermal conditions.

Dynamic Behavior of FG-TPMS Plates on a Viscoelastic Foundation Subjected to Moving Loads Using Moving Element Method

Dynamic Behavior of FG-TPMS Plates on a Viscoelastic Foundation Subjected to Moving Loads Using Moving Element Method

A Novel Capacitance Estimation Method of Modular Multilevel Converters for Motor Drives Using Recurrent Neural Networks with Long Short-Term Memory

Accurate estimation of submodule capacitance in modular multilevel converters (MMCs) is essential for optimal performance and reliability, particularly in motor drive applications such as permanent magnet synchronous motor (PMSM) drives. This paper presents a novel approach utilizing recurrent neural networks with long short-term memory (RNN–LSTM) to precisely estimate capacitance in MMC-based PMSM drives. By leveraging simulation data from MATLAB, the LSTM neural network is trained to predict capacitance based on voltage, current, and their temporal variations. The proposed LSTM architecture effectively captures the dynamic behavior of MMCs in PMSM drives, providing high-precision capacitance estimates. The results demonstrate significant improvements in estimation accuracy, validated through mean squared error (MSE) metrics and comparative analysis of actual versus estimated capacitance. The method’s robustness is further confirmed under varying operating conditions, highlighting its practical utility for real-time applications in power electronic systems.

Vehicle-To-Grid (V2G) Charging and Discharging Strategies of an Integrated Supply–Demand Mechanism and User Behavior: A Recurrent Proximal Policy Optimization Approach

With the increasing global demand for renewable energy and heightened environmental awareness, electric vehicles (EVs) are rapidly becoming a popular clean and efficient mode of transportation. However, the widespread adoption of EVs has presented several challenges, such as the lagging development of charging infrastructure, the impact on the power grid, and the dynamic changes in user charging behavior. To address these issues, this paper first proposes a vehicle-to-grid (V2G) optimization framework that responds to regional dynamic pricing. It also considers power balancing in charging and discharging stations when a large number of EVs are involved in scheduling, with the aim of maximizing the benefits for EV owners. Next, by leveraging the interaction between environmental states and the dynamic behavior of EVs, we design an optimization algorithm that combines the recurrent proximal policy optimization (RPPO) algorithm and long short-term memory (LSTM) networks. This approach enhances system convergence and improves grid stability while maximizing benefits for EV owners. Finally, a simulation platform is used to validate the practical application of the RPPO algorithm in optimizing V2G and grid-to-vehicle (G2V) charging strategies, providing significant theoretical foundations and technical support for the development of smart grids and sustainable transportation systems.

Numerical Simulations Using iRIC Nays2DH for Sediment Transport Behaviors in Dam Breach Tests

After the breach of a landslide dam, the sediment in the breach opening will be carried downstream by the breach flood. The river channel will also be eroded by the flood, resulting in bed load transport. Three large-scale dam breach tests were conducted to investigate the sediment transport behavior after a dam breach. The topography data of the creek channel were measured before and after the dam breach tests to understand the sediment transport behavior. The sediment transport simulations of the dam breach tests were conducted using the iRIC Nays2DH software. The simulations focused on three types of test setups: the single dam, single dam with a spur dike, and double dam models. The terrain (DEM) for the numerical model input was designated based on the LiDAR results, and a flow hydrograph during the dam breach tests was applied. The accuracy of the simulations was assessed using the “coverage index” and “mean absolute percentage error”. A numerical parametrical study was performed to find the major parameters that influenced the simulations. The results showed that the dynamic behavior of water flow and sediment during the dam breach processes were effectively captured by the iRIC Nays2DH simulation, but with limitations. The average flow velocity of the flood in the single dam case was the fastest among the three types of dam breaches. Due to the contraction of the creek channel caused by the spur dike, severe erosion occurred locally, and the flow rate increased in the narrowed section. Water impoundment between the two dams after the first dam breach and the consequent breach of the second dam were also well-simulated for the double dam breach. The findings and simulations in this study help explain dam breaches better and can guide researchers working on sediment transport during dam-breach floods.

Effective spin dynamics of spin-orbit coupled matter-wave solitons in optical lattices

We consider matter–wave solitons in spin–orbit coupled Bose–Einstein condensates embedded in an optical lattice and study the dynamics of the soliton within the framework of Gross–Pitaevskii equations. We express spin components of the soliton pair in terms of nonlinear Bloch equations and investigate the effective spin dynamics. It is seen that the effective magnetic field that appears in the Bloch equation is affected by optical lattices, and thus the optical lattice influences the precessional frequency of the spin components. We make use of numerical approaches to investigate the dynamical behavior of density profiles and center-of-mass of the soliton pair in the presence of the optical lattice. It is shown that the spin density is periodically varying due to flipping of spinors between the two states. The amplitude of spin-flipping oscillation increases with lattice strength. We find that the system can also exhibit interesting nonlinear behavior for chosen values of parameters. We present a fixed point analysis to study the effects of optical lattices on the nonlinear dynamics of the spin components. It is seen that the optical lattice can act as a control parameter to change the dynamical behavior of the spin components from periodic to chaotic.

MotY modulates proton-driven flagellar motor output in Pseudomonas aeruginosa

MotY homologs are present in a variety of monotrichous bacterial strains and are thought to form an additional structural T ring in flagellar motors. While MotY potentially plays an important role in motor torque generation, its impact on motor output dynamics remains poorly understood. In this study, we investigate the role of MotY in P. aeruginosa, elucidating its interactions with the two sets of stator units (MotAB and MotCD) using Förster resonance energy transfer (FRET) assays. Employing a newly developed bead assay, we characterize the dynamic behavior of flagellar motors in motY mutants, identifying MotY as the key functional protein to affect the clockwise bias of naturally unbiased motors in P. aeruginosa. Our findings reveal that MotY enhances stator assembly efficiency without affecting the overall assembly of the flagellar structure. Additionally, we demonstrate that MotY is essential for maintaining motor torque and regulating switching rates. Our study highlights the physiological significance of MotY in fine-tuning flagellar motor function in complex environments.

An integrative analysis of functional consequences of PKD2 missense variants on RNA and protein structures: a computational approach

BackgroundThe PKD2, encoding polycystin-2 (PC2) protein, is second major genetic determinant of autosomal dominant polycystic kidney disease (ADPKD) after PKD1. However, the structural and functional consequences of genetic variants in PKD2 remain poorly understood. Given the complexity and heterogeneous nature of ADPKD, understanding its pathogenesis at cellular and molecular levels is vital for deciphering genotype–phenotype correlations and disease severity, thus informing patient-centered treatments. We analyzed missense variants of PKD2 to assess their impact on RNA structure using computational tools and explored associated protein structure dynamics through MD simulation.ResultsOur findings reveal distinct structural alterations and dynamic behaviors associated with specific missense variants. The variants such as c.1789C > A (p.L597M), c.1109G > A (p.S370N), c.1849C > A (p.L617I), and c.646 T > C (p.Y216H) induced major changes not only in RNA structure and accessibility profile but also in protein structure dynamics. In contrast, variants such as c.915C > A (p.N305K), c.1354A > G (p.I452V), and c.568G > A (p.A190T) resulted in minor alterations in RNA structure but exhibited noticeable effects on certain parameters of protein structure dynamics.ConclusionThis study suggests the multifaceted impact of these missense variants on both RNA and protein levels. It lays the groundwork in identifying high-impact variants in terms of pathogenicity and prioritizing these for further implications in understanding disease heterogeneity and eventually contributing to the development of targeted therapeutic interventions for ADPKD.Graphical Study HighlightsPKD2 missense variants analyzed for impact on RNA and protein StructuresAlterations in RNA Structures and protein dynamics observedComputational integration of analyses aids in prioritizing variants for further studyProvide insights into disease heterogeneity and potential therapeutic targets

Seismic Loading and Reinforcement Effects on the Dynamic Behavior of Soil Slopes

Seismic Loading and Reinforcement Effects on the Dynamic Behavior of Soil Slopes

Novel chaotic image cryptosystem based on dynamic RNA and DNA computing

In view of the security problems of image encryption algorithms encoded by single DNA or RNA, to increase the randomness of the diffusion process and the uncertainty of the coding rules, we propose a combining dynamic RNA and DNA computing based chaotic image encryption algorithm, which has a more complicated encryption process for improving the security of the encryption algorithm and increases the difficulty of decoding. First, a new three-dimensional hyperchaotic map is proposed, which exhibits a rich set of dynamic behaviors. Second, the sequences generated by the proposed map are passed to NIST test with good randomness and implemented by digital signal processing hardware, which shows the feasibility of the proposed chaotic map for industrial applications. Second, the K-means algorithm is used to split the plaintext into two parts. Third, the chaotic sequence is used to displace and diffuse the two parts of the plaintext, respectively. Then, chaotic sequences were used to encode using dynamic DNA and RNA of these two parts, respectively. Then, the chaotic sequences were used to compute the dynamic DNA and RNA computing of these two parts, respectively. Finally, the cipher text is decoded accordingly. The experimental results show that compared with some related encryption algorithms, our method has higher security.

Spindle vibration induced optical performance deterioration and its trans-scale characterization for diamond turned large-aperture optics

In ultra-precision diamond turning of large-aperture optics, the spindle vibration (SV) would cause a deterioration of optical performance. To discuss this issue, the spindle vibration was first excited by a series of designed mass center errors for diamond turning of large-aperture optics. Then, a trans-scale characterization was developed to reveal its dynamic behaviors by combining a white light interferometer and laser interferometer. Next, the amplitude variations were quantitatively evaluated by surface texture parameters and the scale bandwidth was revealed through power spectrum density (PSD). Finally, the point spread function (PSF), encircled energy ratio (EER), and modulation transfer function (MTF) were analyzed and compared. The results indicated that, with the increased SV, Sq and Sz increased and Ssk and Sku showed a transition from skew to normal height distribution. PSD showed that spindle vibration mainly affected the large-scale components rather than the small-scale components. The shape of PSF changed from a standard ring to a dispersed half ring. At the 1 µm radius of the Airy disk, the EER decreased from 82.0% (no SV) to 27.5% (high SV). At the scale of 200mm−1, the MTF decreased from 0.84 (no SV) to 0.44 (high SV). The work provides a fundamental understanding of the evolutionary mechanism of spindle vibration induced optical performance deterioration.

Controlling windscreen wiper vibration through yaw angle adjustments: a study of dynamic contact behavior using fluorescence observation

Controlling windscreen wiper vibration through yaw angle adjustments: a study of dynamic contact behavior using fluorescence observation

Multi-timescale modeling and order reduction towards stability analysis of isolated microgrids

Microgrids incorporate a significant proportion of renewable energy sources and power electronic converters in the energy conversion process, creating a sustainable and clean energy infrastructure. However, the multi-timescale dynamics of microgrids are interactively coupled under a nonlinear structure, which makes it difficult to gain insight into the instability mechanisms without a high-fidelity reduced-order model that preserves the main dynamic behaviors of the system. For the isolated AC microgrid dominated by voltage source inverters (VSI), a detailed state-space model of the system, including the inverter, network, and load, is first developed. Based on this model, the eigenvalue analysis is carried out, and a participation factor analysis tool is also utilized to identify the relevant dynamics that have a strong impact on the system's dominant mode. Furthermore, to simplify the system modeling process without losing essential dynamic interactions, a novel multi-timescale coupled reduced-order model is proposed using a transfer function-based order reduction method, which retains the open-loop gain characteristics to preserve the critical couplings between fast inner loop dynamics and slow droop control dynamics. Finally, the accuracy of the reduced-order model is verified by comparing it with the detailed model and the conventional singular perturbation reduced-order model through eigenvalue distribution and time-domain simulation analysis.

A Method for Dynamic Kolsky Bar Compression at High Temperatures: Application to Ti-6Al-4V

Abstract An experimental apparatus for measuring the dynamic behavior of materials subjected to strain rates on the order of 10 $$^3$$ 3 s $$^{-1}$$ - 1 and temperatures up to 800°C with a unique triple actuation system is developed in this work. This system is based on the traditional Kolsky (or split-Hopkinson pressure) bar design, with the addition of an external furnace used to heat the specimen to the desired temperature. A synchronized triple pneumatic actuation system is used to control the motion and timing of the the sample, incident, and transmitted bars. The cold contact time (CCT), or the time during which the heated sample is in contact with the room temperature bars before compression, is measured experimentally and carefully controlled to minimize the development of a temperature gradient across the sample and avoid heating of the bars. Experiments are performed in conjunction with ultra high speed imaging and 2D digital image correlation (DIC), as well as high speed thermal imaging. To verify the viability of the proposed system, experiments were conducted on Ti-6Al-4V (wt.%) at temperatures from 25°C up to and 800°C at an average strain rate of approximately 1200 s $$^{-1}$$ - 1 .

Enlightenment the dynamic behavior of norbornene–modified ’click’ 4–arm polyethylene glycol hydrogel: Delving into framework properties and transport properties through molecular dynamics simulations

The thiol-norbornene cross-linked 4-arm Polyethylene Glycol (PEG) ’click’ hydrogel is a synthetic, sustainable, and bio-inspired polymer extensively used in biomedical applications. Experimental techniques are widely used to study this system, whereas all-atom molecular dynamics simulations, which offer insight into the dynamic behaviours of the system, are never used to study this system. Three models of thiol-norbornene cross-linked PEG ’click’ hydrogel and a base PEG hydrogel are crafted. It is immersed in water to study its structural and transport properties. Structural properties are analysed through root-mean-square deviation (RMSD), radial distribution function (RDF), hydrogen bonds (H-bond), and stress–strain behavior. The RMSD reveals that the norbornene component enhances hydrogel stability compared to the base model. The RDF illustrates interactions between oxygen in PEG chains and water. H-bond results underscore PEG’s strong H-bond acceptance. The norbornene-functionalized crosslinked PEG hydrogel displays maximum H-bonding. It demonstrates a superior swelling ratio attributed to freezing and non-freezing water effects, indicating high stability and potential suitability for applications. Mean square displacements (MSD) unveil the diffusion coefficients of the hydrogel. The base hydrogel model shows the diffusive behavior, and the diffusion coefficient is 1.97 × 10-10 m2/s. The base model has the highest MSD value compared to other systems. The thiol-norbornene crosslinked click hydrogel has the highest Young’s modulus, which signifies the stiffness of the material. Findings illuminate thiol-norbornene ’click’ PEG hydrogel behaviour, emphasising chemical cross-linking’s crucial role in advanced biomedical applications.