Dynamic Behavior Research Articles

Effects of Memory on Inventory Control and Pricing Policy for Imperfect Production with Rework Process

Fractional calculus is a pertinent way to study the memory effect in an inventory model for investigating its dynamical behavior. Since inventory management is a memory-dependent process, fractional calculus approach may be employed to discover some fruitful insights and can help to gain more profit. In realistic scenarios, the manufacturing process cannot be perfect, and it delivers some faulty units due to many inevitable reasons. In literature, an imperfect production inventory problem under the memory effect has not been studied. Our study aims to investigate the memory effect on a production inventory system. In this article, a fractional order inventory control problem is formulated by considering an imperfect manufacturing process and price-sensitive demand. The faulty units are repaired through a rework process. Caputo fractional derivatives and integrals are used to consider the memory effect. Due to the nonlinear cost elements in the formulated problem, optimal pricing and production policies are investigated by using a quasi-Newton optimization algorithm and particle swarm optimization approach. The managerial implications of the proposed study are discussed with the help of numerical illustrations. The numerical outcomes suggest that consideration of memory in the inventory system boosts the profitability of the firm.

Mitigating Low-Speed Crawling and Jitter in Telescopic Hydraulic Cylinders through Stick-Slip Dynamics Analysis and Friction Reduction Strategies

Abstract This study investigates the mechanisms of jittering in telescopic cylinders and proposes effective mitigation strategies. The focus is on the dynamic behavior of hydraulic cylinders under low-speed conditions, particularly the stick-slip phenomenon. Through finite element analysis using Abaqus and tribological tests, the impact of various factors on the friction and wear properties of cylinder components is examined. Findings reveal that optimizing the average friction coefficient, stick-slip amplitude, and stick-slip time can significantly reduce jitter and creep in hydraulic cylinders. The results provide valuable insights for improving the performance and longevity of hydraulic systems in engineering machinery.

Quantum Flutter from Nonlinear Luttinger Liquid perspective

Abstract Quantum flutter is a ubiquitous phenomenon which can be observed from the fast moving impurity
injected into a fermionic or bosonic medium of quantum liquid. In this scenario, one usually consider
a medium of a fully polarized state and inject a spin-flipped impurity as the initial state. When the
initial velocity of the impurity is beyond the intrinsic sound velocity of the medium, the impurity
momentum dramatically exhibits a long-lived periodic oscillation with the periodicity remaining
invariant with respect to the initial velocity. In this letter, we show that such a novel phenomenon
can be explained by a linear Luttinger liquid coupled to a deep hole in the Fermi sea. Once the
deep hole excitations are involved and the impurity momentum surpass the Fermi momentum,
the propagator thus displays a periodic oscillation after a quick relaxation decay. The oscillation
periodicity is solely determined by the energy of the deepest hole excitation. Our result provides
deep insights into the dynamical behavior of quantum impurities immersed into one-dimensional
quantum liquids.

Hidden complex multistable dynamical analysis and FPGA implementation of integer-fractional order memristive-memcapacitive chaotic system

Abstract A chaotic circuit based on a magnetic-controlled memristor and charge-controlled memcapacitor is proposed in this paper. The study reveals that it is a hyperchaotic system with hidden characteristics in integer-order. Furthermore, as the parameters change, the attractors exhibit rich evolutionary phenomena. Even after adjusting some parameters to very large values, the system still maintains hyperchaotic behavior. Interestingly, the basin of attraction shows the multistability of the system. Under initial value control, coexisting attractors are categorized into two types: those with initial offset-boosting behavior and nested attractors. When under parameter control, coexisting attractors are divided into two types: symmetric coexisting attractors and nested coexisting attractors. By analyzing the spectral entropy (SE) complexity of the system and using a complexity distribution diagram with two parameters and two initial values, the existence of multiple complex dynamic behaviors in the system has been verified. The fractional-order memristive-memcapacitive system based on the Grunwald-Letnikov algorithm and the five fractional-order values of q i (i = 1, 2, 3, 4, 5) are taken as different in the numerical simulation, it also displays multiple coexisting phenomena similar to those of the integer-order. Finally, Matlab/Simulink and DSP Builder software platform are used to design the fractional-order five-dimensional chaotic memristive-memcapacitive system, and then combined with VHDL and Verilog HDL hardware language, the proposed circuit system is verified on the EP4CE115F29C7 FPGA main chip of Cyclone IV E series. The consistency of hardware implementation and software simulation shows the correctness and feasibility of the design.

Dynamic coefficient of flexural motion of beam experiencing simple support under successive moving loads

AbstractAnalytic expressions of the dynamic coefficient (DC) factor and vibrational behavior of a uniformly elastic isotropic beam with a simple boundary condition caused by accelerating masses with varying velocities are analyzed. The motion of this problem is described by a fourth‐order partial differential equation, which governs its behavior. The weighted residual method converts the governing equation into a sequence of linked second‐order differential equations to facilitate the analysis. A rewritten version of Struble's asymptotic method further simplifies the transformed governing equation. This modification aids reduction in the complexity of the equation. The closed‐form response is contrasted across three force motions: acceleration, deceleration, and uniform motion. The study thoroughly examines how different velocities and frequencies of the moving force affect the dynamic behavior of the beam. The study also examines the influence of load velocity on the DC of the beam subjected to pinned–pinned boundary conditions.

Electromechanical characteristics analysis of L-shaped pipelines with enhanced active constrained damping treatment

Abstract Fluid-conveying pipelines are widely employed in various engineering fields, such as aerospace, nuclear, and marine fields. These pipelines work in serious vibration environments, which can quickly damage the pipeline system. The vibration control of pipelines is a prominent challenge in the engineering field. This paper is aimed to investigate the electromechanical analysis characteristics of L-shaped pipelines with the Enhanced Active Constrained Layer Damping (EACLD) structure. A finite element model of the L-shaped pipeline with EACLD is established. The dynamic behaviour of an L-shaped pipeline with an EACLD structure was analysed in both the time and frequency domains. The influence of the voltage and the position, the length and the elastic modulus, the thickness and the edge element parameters are all considered. Additionally, the influence of the EACLD patch orientation on static displacement and stress are considered. Simulation results indicate that reasonable selection of the parameters for the EACLD patch and edge element can enhance vibration damping effectiveness, which can provide effective design guidance for active vibration control of the pipeline system. 

A Computational Pipeline for Identifying Gene Regulatory Networks: A Case Study of Response to Exercise.

Gene regulatory networks are foundational in the control of virtually all biological processes. These networks orchestrate a myriad of cell functions ranging from metabolic rate to the response to a drug or other intervention. The data required to accurately identify these control networks remains very cost and labor intensive typically leading to relatively sparse time course data that is largely incompatible with conventional data-driven model identification techniques. In this work, we combine empirical identification of gene-gene interactions with constraints describing the expected dynamic behavior of the network to infer regulatory dynamics from under-sampled data. We apply this to the identification of gene regulatory subnetworks recruited in groups of subjects participating in several different exercise interventions. Intervention-specific response networks are compared to one another and control actions driving differences are identified. We propose that this approach can extract statistically robust and biologically meaningful insights into gene regulatory dynamics from a dataset consisting of a small number of participants with very limited longitudinal sampling, for example pre- and post- intervention only.

Rotor angle stability of a microgrid generator through polynomial approximation based on RFID data collection and deep learning.

The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable.

New dynamical behaviors and soliton solutions of the coupled nonlinear Schrodinger equation

New dynamical behaviors and soliton solutions of the coupled nonlinear Schrodinger equation

A dynamic strain prediction method for malfunction of sensors in buildings subjected to seismic loads using CWT and CNN.

Structural health monitoring (SHM) is a technique used to evaluate the safety of a structure based on the structural response. In the SHM, sensors may fail to measure the structural response, or data loss can occur during the data transmission and reception. In this regard, this paper proposes a method of predicting the strain response when a sensor installed in a structural member suffers a malfunction. To this end, a convolutional neural network (CNN) is introduced to predict the strain response. Time-history strain data for seismic loads measured in adjacent structural members are set as the input layer of the CNN, and continuous wavelet transform (CWT) results are additionally set in the input layer of the CNN to reflect the dynamic structural behavior during earthquakes in the CNN predictions. Time-history strain data for a member, the target of prediction, are set as the output layer of the CNN. Then, the trained CNN uses the time-history strain data of adjacent members with no sensor failure and the CWT result of the data to predict the strain response of a structural member with sensor failure. A numerical study on the seismic load of a single-span three-story steel moment frame and an experimental study on a single-span three-story reinforced concrete frame were carried out to verify the validity of the proposed method. This study presents a method of applying CWT data to the input layer of the CNN and discusses the effectiveness of CWT data for predicting the nonlinear response of a structural member with damaged sensors.

Theoretical Model and Experimental Application for Solute Diffusion in Leaching Test With Time-Dependent Boundary Conditions

Abstract Measuring the diffusion coefficient of clay-based liner materials is important in estimating and predicting long-term barrier performance in waste containment facilities. Various theoretical models, including the finite cylindrical model, have been commonly used to determine the diffusion properties of clay-based liner materials in leaching tests. However, the assumption of zero-concentration boundary conditions of the traditional finite cylindrical model contradicts the measured variation of concentration in real leaching tests, likely resulting in (1) underestimated and unconservative diffusion coefficient, or (2) requirement of a relatively large liquid-to-soil ratio and frequent leachate replacement in the experiment to maintain the zero-concentration boundary condition. In this study, a theoretical model was developed to evaluate the solute diffusion process within a soil specimen under arbitrary, time-dependent concentration boundary conditions. The proposed model, incorporating the time-dependent boundary conditions, provides efficient calculations of the concentration distribution and the cumulative fraction leached of solute across the soil specimen. The example application of the proposed model to experimental data demonstrates the capability of the proposed model to determine apparent diffusion coefficients of clay-based liner materials without introducing errors associated with the assumption of a zero concentration boundary condition. The proposed model provides a comprehensive method to investigate the dynamic transport behaviors of solutes through clay-based liner materials in future studies.

The interplay between liquid–liquid and ferroelectric phase transitions in supercooled water

The distinctive characteristics of water, evident in its thermodynamic anomalies, have implications across disciplines from biology to geophysics. Considered a valid hypothesis to rationalize its unique properties, a liquid-liquid phase transition in water below the freezing point, in the so-called supercooled regime, has nowadays been observed in several molecular dynamics simulations and is being actively researched experimentally. The hypothesis of ferroelectric phase transition in supercooled water can be traced back to 1977, due to Stillinger. In this work, we highlight intriguing and far-reaching implications of water: The ferroelectric and liquid-liquid phase transitions can be designed as two facets of the same underlying phenomenon. Our results are based on the analysis of extensive molecular dynamics simulations and are explained in the context of a classical density functional theory in mean-field approximation valid for a polar liquid, where dipolar interaction is treated perturbatively. The theory underpins the potential role of ferroelectricity in promoting the liquid-liquid phase transition, being the density-polarization coupling inherent in the dipolar interaction potential. The existence of ferroelectric order in supercooled low-density liquid water is confirmed by the observation in molecular dynamics simulations of collective modes in space-time polarization correlation functions, traceable to spontaneous breaking of continuous rotational symmetry. Our work sheds light on water's supercooled behavior and opens the door to experimental investigations of the static and dynamic behavior of water's polarization.

Cooling peak load reduction potential of ventilated slabs: a numerical study

Thermally activated building systems provide an effective method to combine thermal inertia and HVAC systems, and can be used to promote comfortable indoor conditions in summer. Ventilated slabs (VS) use supply air flow directly for heating or cooling, without the need for separate auxiliary systems. This paper proposes a detailed model of an office building equipped with VS under three different climatic conditions during one month in the summer. A Model Predictive Controller and a Genetic Algorithm, developed in MATLAB, are subsequently implemented to reduce energy consumption of the system while maintaining the room temperature set point. The outcomes of both short and extended sequences are presented after conducting modelling considerations. The dynamic behaviour of the room is highlighted, while the cooling demand is further evaluated using statistical indexes. The reduction in peak cooling load was noted in all instances, including during outdoor conditions that are unfavourable to free cooling strategies.

Numerical Simulations and Bifurcation of Ca2+ Oscillatory Behaviour in the Connection of Neurons and Astrocytes.

Extensive research has demonstrated that astrocytes actively participate in the regulation of synaptic communication. To examine the dynamic behavior of the model, a neuron-astrocyte model has been solved, and a bifurcation analysis has been performed. This paper uses the equilibrium point, stability theory, and the center manifold theorem to theoretically investigate the dynamical analysis of Ca2+ oscillations in the cytosol. The connections at tripartite synapses between the cells have been modeled using IP3 and 2-AG. A mathematical model is used to depict the overall framework of bifurcation and induced Ca2+ dynamics. The differences in the presence and disappearance of Ca2+ oscillations are partially explained by two subcritical Hopf bifurcation points, according to the results. Communication between the cells occurs through the oscillations of Ca2+ concentration. Furthermore, numerical simulations are conducted to confirm the efficacy of the suggested approach. Thus, our findings imply that neuron-astrocyte crosstalk plays a fundamental role in generating a variety of neuronal activities, thereby improving the brain's capacity for information processing.

Emergent topological quasiparticle kinetics in constricted nanomagnets

The ubiquitous domain wall kinetics under magnetic field or current application describes the dynamic properties in nanostructured magnets. However, when the geometrical size of a nanomagnetic system is constricted to the limiting domain wall length scale, the competing energetics between anisotropy, exchange, and dipolar interactions can cause emergent kinetics due to quasiparticle relaxation, similar to bulk magnets of atomic origin. This paper presents a joint experimental and theoretical study to support this argument: constricted nanomagnets, made of antiferromagnetic and paramagnetic neodymium thin film with honeycomb motif, reveal fast kinetic events at picosecond timescales due to the relaxation of topological quasiparticles that persist to low temperature in the absence of any external stimuli. This discovery is especially important considering the fact that paramagnets or antiferromagnets have no net magnetization. Yet, the kinetics in neodymium nanostructures is quantitatively similar to that found in ferromagnetic counterparts and only varies with the thickness of the specimen. This suggests that a universal, topological quasiparticle-mediated dynamical behavior can be prevalent in nanoscopic magnets, irrespective of the nature of the underlying magnetic material. Published by the American Physical Society 2024

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

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

Revealing Dynamics and Competitive Mechanism of Gas-Induced Surface Segregation of PdFe0.08 Dilute Alloy by Multi-Dimensional Imaging.

The restructuring of dilute alloys under gas environments has shown a great impact on their catalytic performance due to intriguing structural sensitivity, but the structural dynamics and underlying mechanism remains elusive. Herein, we directly resolved the distinct dynamic behaviors of PdFe0.08 dilute alloys under CO or O2 environment by multidimensional imaging. The stronger binding of gaseous CO with Fe atoms stimulates Fe segregation out of the PdFe0.08, resulting in 3D growth of Fe islands, whereas the dissociative adsorption of O2 results in 2D layer-by-layer growth of segregated FeO as encapsulation overlayers that bind strongly with the Pd surface underneath. Such varied structures remarkably tune the catalytic activity for CO oxidation, showing a considerably high activity for a CO-treated sample. Our results reveal the competitive mechanism between adsorbate-metal and metal-metal interaction for gas-induced surface segregation, which should be highly considered for the rational design of dilute alloys with dynamically tuned structure and reactivity.

The Influence of Graft Rigidity on the Dynamical Behavior of PMMS-Based Polymer Brushes at Ambient and High Pressures

The Influence of Graft Rigidity on the Dynamical Behavior of PMMS-Based Polymer Brushes at Ambient and High Pressures

Osmotic and phoretic competition explains chemotaxic assembly and sorting

Microscale objects responding to chemical gradients by migrating toward or away from a preferred species is a simple yet constitutive mechanism by which transport occurs in biological organisms. Synthetic chemotaxis provides key physical descriptions of simplified systems that can be used in biological models, or in the creation of advanced responsive material systems. In this article, we provide a quantitative framework for understanding synthetic chemotaxis of microparticles which involves a competition between phoresis and osmosis. We present separate quantitative measurements of phoresis and osmosis acting on individual taxing particles, finding that phoresis follows the long-predicted [Formula: see text] scaling while the osmotic contribution depends on the geometry and details of the system, and must be solved on a case-by-case basis. Through this, we are able to develop a more accurate picture of particle transport at the single particle level. Equipped with this approach, we go on to describe how high concentrations of particles in a symmetric chemical gradient grow close-packed hives that reach a steady-state size tunable through light intensity or particle size. Last, we demonstrate that mixed particles experiencing the same chemical gradient will selectively migrate toward or away depending on the nature of the particle surface, thereby locally sorting out a particular species. We anticipate these results will be important in describing both biological and synthetic chemotaxis in phoretic systems and should bring a wealth of studies that take advantage of competing osmotic flows to illicit unexpected dynamic active behavior.

An Analysis of Dynamic Recrystallization During the Reduction Pretreatment Process Using a Multiscale Model

In this study, a multiscale model is developed through secondary development (UMAT and UEXTERNALDB) in Abaqus with the objective of simulating the thermal deformation process with dynamic recrystallization behavior. The model couples the finite element method (FEM) with the multiphase field model (MPFM), thereby establishing bidirectional coupling between macroscopic mechanical behavior and microstructural evolution. A comparison between the single-element hot compression simulation and experimental results demonstrates that the model accurately simulates both the macroscopic mechanical behavior and microstructural evolution during the thermal deformation process, thereby exhibiting high precision. Simulations of the reduction pretreatment (RP) process under different reduction amounts and billet surface temperatures demonstrate that increasing the reduction amount and billet surface temperature significantly enhances both plastic deformation and the volume fraction of dynamic recrystallization in the billet core. This results in the closure of core voids and the refinement of the core microstructure, thereby providing valuable guidance for the development of optimal reduction pretreatment (RP) processes.