Behavior Of System Research Articles

Rope–sheave contact transient analysis in hoisting operations with a bristle model and an arbitrary Lagrangian–Eulerian approach

AbstractThis paper describes the development of a computational model for the rope–sheave contact interaction in reeving systems when the ropes are modeled with an arbitrary Lagrangian–Eulerian approach. This discretization approach has been developed in previous publications as a general and systematic method for the modeling and simulation of reeving systems. However, the rope–sheave contact model was avoided assuming the no-slip contact condition. The contact model developed in this paper introduces specialized ALE-ANCF-cubic rope contact elements that are used to discretize the rope segment winded at the sheave. The contact is modeled using a set of virtual discrete bristles attached to material points in the mid-line of the rope in one end and in contact with the sheave in the other end. Therefore, a second Lagrangian mesh, apart of the ALE mesh used to discretize the rope, is used to define the fixed ends of the bristles. The kinematics and dynamics used to calculate the normal and tangential contact forces are described in detail. The contact model is 3D and can be used to analyze the contact with a sheave groove with arbitrary shape. The tangential contact force model can be used to describe stick and slip contact conditions and, to improve the simulation performance of the model, an LuGre regularization tangential contact force model is used. The rope-sheave contact model is used to analyze the behavior of a simple elevator system. The numerical results show that the static rope-sheave contact interaction agrees well with an analytical solution of the problem. Finally, the same elevator system is analyzed dynamically for a cabin ride of 8 meters with a steady velocity of 1 m/s. Results show that the normal and tangential contact forces during the steady velocity period are not so different from the static solution, but very different from the classical Creep Theory and Firbank’s Theory.

DYNAMICS OF OPTICAL WAVE PROFILES TO THE FRACTIONAL THREE-COMPONENT COUPLED NONLINEAR SCHRÖDINGER EQUATION

This paper explores the truncated [Formula: see text]-fractional three-component coupled nonlinear Schrödinger (tc-CNLS) equation, that regulates the behavior of optical pulses in optical fibers. These equations are utilized in various scientific and engineering fields, including nonlinear fiber optics, electromagnetic field waves, and signal processing through optical fibers. The study of multi-component NLS equations has gained significant attention due to their ability to elucidate various complex physical phenomena and exhibit more dynamic structures of localized wave solutions. The freshly invented integration tools, known as the fractional modified Sardar subequation method (MSSEM) and fractional enhanced modified extended tanh-expansion method (eMETEM), are employed to ensure the solutions. The study focuses on extracting various types of optical solitons, including bright, dark, singular, bright-dark, complex, and combined solitons. Optical soliton propagation in optical fibers is currently a subject of great interest due to the multiple prospects for ultrafast signal routing systems and short light pulses in communications. In nonlinear dispersive media, optical solitons are stretched electromagnetic waves that maintain their intensity due to a balance between the effects of dispersion and nonlinearity. Furthermore, hyperbolic, periodic and exponential solutions are generated. The utilized methodology is effective in explaining fractional nonlinear partial differential equations (FNLPDEs) as it offers pre-existing solutions and additionally derives novel exact solutions by mixing outcomes from various procedures. Furthermore, we plot the visualizations of solutions by plotting 3D, 2D, and contour graphs with the corresponding parameter values. The findings of this paper can improve the understanding of the nonlinear dynamical behavior of a specific system and demonstrate the efficacy of the methodology used. We anticipate that our study will provide substantial benefits to a considerable group of engineering model experts. The findings demonstrate the efficacy, efficiency, and applicability of the computational method employed, particularly in dealing with intricate systems.

A physics-based dimension reduction and modeling method for monitoring data and its application to tunnel engineering

The promotion of intelligent control of engineering equipment requires extracting vital information from numerous original features in the monitoring data. Data-driven methods solely rely on data distribution to extract dimensional information, facing difficulties in comprehensively understanding and scrutinizing system behavior. To satisfy the control and optimization requirements of intelligent systems, this paper introduces a physics-based dimension reduction and modeling method. According to scientific principles, original features are transformed into causal variables with physical meaning. Then important causal variables are selected through regularization in machine learning. Finally, a concise prediction model with physical representation is established. An application to tunnel engineering demonstrates that the generated low-dimensional space reveals the essential laws between the control parameter and its main influencing factors (i.e., the geological conditions, operating status, and structural characteristics of the tunneling equipment), which can further provide a better prediction model balancing accuracy and dimensionality.

An Improved Positive Displacement Pump Model Accounting for Suction Cavitation

Abstract Positive displacement (PD) pumps are widely employed in industrial settings due to their inherent simplicity and reliability, serving a variety of applications from slurry transport to jet washing. Although their operational principles are straightforward, the fluid dynamics of the pumped medium exhibit nontrivial characteristics, including intricate transient phenomena. Consequently, a comprehensive fluid dynamic description, such as a three-dimensional fluid analysis, presents challenges due to its demanding computational requirements. While simpler analytical PD pump models are available, they often fail to adequately represent the primary system behaviors, particularly when dealing with cavitation. Motivated by these challenges, this study aims to develop a novel one-dimensional model for PD pumps, offering a representation of essential fluid phenomena without imposing significant computational burdens. After assessing the relative importance of the fluid dynamic behaviors that the model must capture, we construct a pump model based on a one-dimensional fluid description and solve it using a second-order in time and space monotone upwind scheme for conservative law and total variation diminishing (MUSCL-TVD) scheme. The model's validity is confirmed by its application to both single-chamber and three-chamber diaphragm PD pumps, which are instrumented for experimental validation. The results of the one-dimensional model exhibit strong agreement with physical experiments, both in controlled laboratory environments and field conditions. This success suggests a promising approach for industrial applications.

Optimizing Sensor-Controlled Systems with Minimal Intervention: A Fuzzy Relational Calculus Approach

This article describes an approach for optimizing sensor-controlled systems through minimal intervention, utilizing fuzzy linear systems of equations (FLSEs). Starting with a generalized model of the system behavior, we incorporate an array of control units, environmental sensors, and an expert knowledge base. The described problems of detecting the level of intervention needed to change the system state to another is handled with the help of developed methods for solving the inverse problem faced by FLSEs. By achieving minimal intervention, we ensure that the system adjustments are effective, economically optimal, and non-intrusive. A MATLAB-based implementation is presented.

Anisotropic remixing of a phase separated binary colloidal system with particles of different sizes in an external modulation.

We explore the phase behavior of a binary colloidal system under external spatially periodic modulation. We perform Monte Carlo simulations on a binary mixture of big and small repulsive Lennard-Jones particles with a diameter ratio of 2:1. We characterize structure by isotropic and anisotropic pair correlation functions, cluster size distribution, bond angle distribution, order parameter, and specific heat. We observe the demixing of the species in the absence of external modulation. However, the mixing of the species gets enhanced with increasing potential strength along with the alignment of the particles transverse to the modulation. The de-mixing order parameter shows discontinuity with increasing modulation strength, characterizing a first order phase transition. The peak in specific heat increases linearly with the size of the system. We also look into the dynamical behavior of the system via computing Mean Square Displacement (MSD) along both parallel and perpendicular directions to the modulation. We observe a decrease in the diffusion coefficient for both types of particles as we increase the strength of the modulation.

Symmetry and complexity: a Lie symmetry method to bifurcation, chaos, multistability and soliton solutions of the nonlinear generalized advection-diffusion-reaction equation

This paper deals with the complexities of nonlinear dynamics within the nonlinear generalized advection-diffusion-reaction equation, which describes intricate transport phenomena involving advection, diffusion, and reaction processes occurring simultaneously. Through the utilization of the Lie symmetry approach, we thoroughly examine this proposed model, transforming the partial differential equation into an ordinary differential equation using similarity reduction techniques to facilitate a more comprehensive analysis. Exact solutions for this transformed equation are derived employing the extended simplest equation method and the new extended direct algebraic method. To enhance understanding, contour plots along with 2D and 3D visualizations of solutions are employed. Additionally, we explore bifurcation and chaotic behaviors through a qualitative analysis of the model. Phase portraits are meticulously scrutinized across various parameter values, offering insights into system behavior. The introduction of an external periodic strength allows us to utilize various tools including time series, 3D, and 2D phase patterns to discern chaotic and quasi-periodic behaviors. Furthermore, a multistability analysis is conducted to examine the impacts of diverse initial conditions. These findings underscore the efficacy and practicality of the proposed methodologies in evaluating soliton solutions and elucidating phase dynamics across a spectrum of nonlinear models, offering novel perspectives on intricate physical phenomena

Origami of multi-layered spaced sheets

Two-dimensional (2D) origami tessellations such as the Miura-ori are often generalized to build three-dimensional (3D) architected materials with sandwich or cellular structures. However, such 3D blocks are densely packed with continuity of the internal material, while for many engineering structures with multi-physical functionality, it is necessary to have thin sheets that are separately spaced and sparsely connected. This work presents a framework for the design and analysis of multi-layered spaced origami, which provides an origami solution for 3D structures where multiple flat sheets are intentionally spaced apart. We connect Miura-ori sheets with sparsely installed thin-sheet parallelogram-like linkages. To explore how this connectivity approach affects the behavior of the origami system, we model the rigid-folding kinematics using analytic trigonometry and rigid-body transformations, and we characterize the elastic-folding mechanics by generalizing a reduced order bar and hinge model for these 3D assemblies. The orientation of the linkages in the multi-layered spaced origami determines which of three folding paths the system will follow including a flat foldable type, a self-locking type, and a double-branch type. When the origami is flat foldable, a maximized packing ratio and a uniform in-plane shear stiffness can be achieved by strategically choosing the link orientation. We show possible applications by demonstrating how the multi-layered spaced origami can be used to build deployable acoustic cloaks and heat shields.

Verification of Railway Control Systems Using Model Checking and CTL, Explained Through a Case Study

Systematic faults can often occur during the development of a system. The later such faults are discovered, the more expensive it can be to correct them. In systems engineering practice, there are many methods and tools to reduce the likelihood of systematic faults. In this paper, we present the application of a formal model–based verification technique – called model checking – to assist railway engineers in designing and verifying the safety-related functionality of railway control systems. The proposed process is part of a specification-verification environment that facilitates the construction of correct, complete, consistent, and verifiable functional specifications during development. The results and experience in model checking are illustrated by a case study of a vehicle detection point, a common component in this domain. The model checking of the case study has been performed in the widely used UPPAAL modeling and simulation framework, which can also verify formal properties and generate a counterexample in case of a property violation. By analyzing the counterexample, the designer can gain insights into the system's behavior and identify potential design flaws or failures. Model checking can be used to achieve a higher quality functional specification that is typically more complete and/or contains fewer faults compared to the traditional development approach.

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

Machine learning algorithms have proven to be practical in a wide range of applications. Many studies have been conducted on the operational energy consumption and thermal comfort of radiant floor systems. This paper conducts a case study in a self-designed experimental setup that combines radiant floor and fan coil cooling (RFCFC) and develops a data monitoring system as a source of historical operational data. Seven machine learning algorithms (extreme learning machine (ELM), convolutional neural network (CNN), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM)) were employed to predict the behavior of the RFCFC system. Corresponding prediction models were then developed to evaluate operative temperature (Top) and energy consumption (Eh). The performance of the model was evaluated using five error metrics. The obtained results showed that the RF model had very high performance in predicting Top and Eh, with high correlation coefficients (>0.9915) and low error metrics. Compared with other models, it also demonstrated high accuracy in Eh prediction, yielding maximum reductions of 68.1, 82.4, and 43.2% in the mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), respectively. A sensitivity ranking algorithm analysis was also conducted. The obtained results demonstrated the importance of adjusting parameters, such as the radiant floor supply water temperature, to enhance the indoor comfort. This study provides a novel and effective method for evaluating the energy efficiency and thermal comfort of radiant cooling systems. It also provides insights for optimizing the efficiency and thermal comfort of RFCFC systems, and lays a theoretical foundation for future studies integrating machine learning algorithms in this field.

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

Factors influencing central nervous system medication deprescribing and behavior change in hospitalized older adults.

Central nervous system (CNS) medications are linked to higher morbidity and mortality in older adults. Hospitalization allows for deprescribing opportunities. This qualitative study investigates clinician and patient perspectives on CNS medication deprescribing during hospitalization using a behavioral change framework, aiming to inform interventions and identify recommendations to enhance hospital deprescribing processes. This qualitative study focused on hospitalists, primary care providers, pharmacists, and patients aged ≥60 years hospitalized on a general medicine service and prescribed ≥1 CNS medications. Using semi-structured interviews and focus groups, we aimed to evaluate patient medication knowledge, prior deprescribing experiences, and decision-making preferences, as well as provider processes and tools for medication evaluation and deprescribing. Rapid qualitative analysis applying the Capability, Opportunity, Motivation, and Behavior (COM-B) framework revealed themes influencing deprescribing behavior in patients and providers. A total of 52 participants (20 patients and 32 providers) identified facilitators and barriers across deprescribing steps and generated recommended strategies to address them. Clinicians and patients highlighted the opportunity for CNS medication deprescribing during hospitalizations, facilitated by multidisciplinary teams enhancing clinicians' capability to make medication changes. Both groups also stressed the importance of intensive patient engagement, education, and monitoring during hospitalizations, acknowledging challenges in timing and extent of deprescribing, with some patients preferring decisions deferred to outpatient clinicians. Hospitalist and pharmacist recommendations centered on early pharmacist involvement for medication reconciliation, expanding pharmacy consultation and clinician education on deprescribing, whereas patients recommended enhancing shared decision-making through patient education on medication adverse effects, tapering plans, and alternatives. Hospitalists and PCPs also emphasized standardized discharge instructions and transitional care calls to improve medication review and feedback during care transitions. Clinicians and patients highlighted the potential advantages of hospital interventions for CNS medication deprescribing, emphasizing the necessity of addressing communication, education, and coordination challenges between inpatient and outpatient settings.

Numerical investigation of the effect of operating conditions on performance of PV-PCM system

The photovoltaic module only manages to convert around 15% of the incoming light into usable electricity and the rest is lost as heat, reducing the module's overall efficiency. Hybrid photovoltaic thermal systems are therefore the best way to collect thermal and electrical energy. The cooling and heat storage benefits of PV cells are amplified using phase change materials (PCMs). In this article, a simple two-dimensional CFD model to forecast the temperature of the PV where it contacts the PCM. The findings of the numerical simulations show that PV systems equipped with PCM have been studied under a variety of situations. A contemporary thermodynamic study is performed to examine system behaviour under different operating settings by investigating the effect of some rather crucial aspects that have not been taken into account before. The research takes into account three instances, each with its own unique combination of wind speed, ambient temperature, and sun irradiation. It has been shown that the solar radiation is a key component for growth in the PV temperature. The findings show that an increase in PV temperature of 5–7°C is caused by an increase in solar irradiance of 1000 W/m2. The rise in wind velocity, heat dissipation rate increases resulting to the fall in the temperature of the PCM and glass, the decrease in PCM temperature leads to a further drop in glass temperature. More so than wind speed or solar irradiation, ambient temperature determines how quickly PV glass heats up. Furthermore, at an ambient temperature of 35°C, PCM is found to melt entirely in 435 min, whereas at an ambient temperature of 30°C, PCM melts up to a liquid fraction of 0.77 min. Therefore, the efficiency of the PV system improves dramatically as the outside temperature is lowered.

Design & Analysis of Non-Linear Property of Material for Semi-Rigit, Fixed & Flexible Structural Frames

During an earthquake, failure of structure starts off-evolved at factors of weak spot. This weak point arises due to discontinuity in mass, stiffness and geometry of structure. The systems having this discontinuity are termed as irregular systems. Irregular structures contribute a massive portion of city infrastructure. The effect of vertically irregularities within the seismic overall performance of systems will become definitely vital. The main objective of the analysis is to study the behavior of flat slab system in vertical irregular multi-storied building against different forces acting on it during earthquake. Also, the objective of analysis is to study the structural behavior of shear wall – flat slab interaction with opening. The analysis is made between in the three types of G+9 storey building with 0 % vertical irregularity, 200 % vertical irregularity and 300% vertical irregularity. In all these building’s shear wall is provided with & without 20% opening. Total 15 modeled are studied and their results were compared. Key Words: Flat slab, Vertical Geometric Irregularity, Shear wall with opening, Response Spectrum analysis, STAAD Pro2007

Electrochemical Monitoring of Real‐Time Vesicle Dynamics Induced by Tau in a Confined Nanopipette

The microtubule‐associated protein tau participates in neurotransmission regulation via its interaction with synaptic vesicles (SVs). The precise nature and mechanics of tau's engagement with SVs, especially regarding alterations in vesicle dynamics, remain a matter of discussion. We report an electrochemical method using a synapse‐mimicking nanopipette to monitor vesicle dynamics induced by tau. A model vesicle of ~30 nm is confined within a lipid‐modified nanopipette orifice with a comparable diameter to mimic the synaptic lipid environment. Both tau and phosphorylated tau (p‐tau) present two‐state dynamic behavior in this biomimetic system, showing typical ionic current oscillation, induced by lipid‐tau interaction. The results indicate that p‐tau has a stronger affinity to the lipid vesicles in the confined environment, blocking the vesicle movement to a higher degree. Taken together, this method bridges a gap for sensing synaptic vesicle dynamics in a confined lipid environment, mimicking vesicle movement near the synaptic membrane. These findings contribute to understanding how different types of tau protein regulate synaptic vesicle motility and to underlying its functional and pathological behaviours in disease.

Electrochemical Monitoring of Real-Time Vesicle Dynamics Induced by Tau in a Confined Nanopipette.

The microtubule-associated protein tau participates in neurotransmission regulation via its interaction with synaptic vesicles (SVs). The precise nature and mechanics of tau's engagement with SVs, especially regarding alterations in vesicle dynamics, remain a matter of discussion. We report an electrochemical method using a synapse-mimicking nanopipette to monitor vesicle dynamics induced by tau. A model vesicle of ~30 nm is confined within a lipid-modified nanopipette orifice with a comparable diameter to mimic the synaptic lipid environment. Both tau and phosphorylated tau (p-tau) present two-state dynamic behavior in this biomimetic system, showing typical ionic current oscillation, induced by lipid-tau interaction. The results indicate that p-tau has a stronger affinity to the lipid vesicles in the confined environment, blocking the vesicle movement to a higher degree. Taken together, this method bridges a gap for sensing synaptic vesicle dynamics in a confined lipid environment, mimicking vesicle movement near the synaptic membrane. These findings contribute to understanding how different types of tau protein regulate synaptic vesicle motility and to underlying its functional and pathological behaviours in disease.

Data-driven exploration of swarmalators with second-order harmonics.

We explore the dynamics of a swarmalator population comprising second-order harmonics in phase interaction. A key observation in our study is the emergence of the active asynchronous state in swarmalators with second-order harmonics, mirroring findings in the one-dimensional analog of the model, accompanied by the formation of clustered states. Particularly, we observe a transition from the static asynchronous state to the active phase wave state via the active asynchronous state. We have successfully delineated and quantified the stability boundary of the active asynchronous state through a completely data-driven method. This was achieved by utilizing the enhanced image processing capabilities of convolutional neural networks, specifically, the U-Net architecture. Complementing this data-driven analysis, our study also incorporates an analytical stability of the clustered states, providing a multifaceted perspective on the system's behavior. Our investigation not only sheds light on the nuanced behavior of swarmalators under second-order harmonics, but also demonstrates the efficacy of convolutional neural networks in analyzing complex dynamical systems.

Chaos and bifurcations of a two-dimensional hepatitis C virus model with hepatocyte homeostasis.

In this paper, we delve into the intricate local dynamics at equilibria within a two-dimensional model of hepatitis C virus (HCV) alongside hepatocyte homeostasis. The study investigates the existence of bifurcation sets and conducts a comprehensive bifurcation analysis to elucidate the system's behavior under varying conditions. A significant focus lies on understanding how changes in parameters can lead to bifurcations, which are pivotal points where the qualitative behavior of the system undergoes fundamental transformations. Moreover, the paper introduces and employs hybrid control feedback and Ott-Grebogi-Yorke strategies as tools to manage and mitigate chaos inherent within the HCV model. This chaos arises due to the presence of flip and Neimark-Sacker bifurcations, which can induce erratic behavior in the system. Through the implementation of these control strategies, the study aims to stabilize the system and restore it to a more manageable and predictable state. Furthermore, to validate the theoretical findings and the efficacy of the proposed control strategies, extensive numerical simulations are conducted. These simulations serve as a means of confirming the theoretical predictions and provide insight into the practical implications of the proposed control methodologies. By combining theoretical analysis with computational simulations, the paper offers a comprehensive understanding of the dynamics of the HCV model and provides valuable insights into potential strategies for controlling and managing chaos in such complex biological systems.

Effect of partial substitution of K2O by MgO on the structure, and thermal, physical, optical properties and crystallization behaviour of K2O–MgO–B2O3–P2O5 glasses

In this work, a series of K2O–MgO–B2O3–P2O5 (KMBP) glasses were prepared by the melt-quenching method and the glass-ceramics were prepared by the one-step heat treatment. The thermal, structural, physical and optical properties, as well as the crystallization behaviour of the KMBP system, were investigated in detail using characterization techniques such as DSC, XRD, SEM, FTIR, transmittance spectroscopy and microhardness. As indicated by DSC, the thermostability and glass-forming ability were gradually enhanced through the partial substitution of K2O by MgO. XRD showed that the main crystalline phases of the glass-ceramics (GC) were gradually converted from the P–Mg–K–O based crystalline phases to the P–Mg–O based and the P–Al–O based crystalline phases. The SEM results indicated that tiny grain-like crystals were successfully precipitated from the parent glasses after heat treatment. The deconvoluted FTIR spectra results showed a gradual conversion of the [BO3] and [PO3] units towards the [BO4] and [PO4] units with the increase of the MgO contents. The increase in the degree of glass network polymerization was the essential reason for the increment in the microhardness of the parent glasses. The microhardness of GC featured a trend concordant with the crystallinity of the crystalline phases. The values of metallization criteria (MC) between 0.34 and 0.45, as well as the higher values of third-order nonlinear polarizability (χ(3)) and nonlinear refractive index (nnl) are indicative of the good suitability of the KMBP glasses for the nonlinear optical applications.

High-strain dynamic model of large-diameter pipe piles with soil plug for vertical vibration analysis

A rigorous analytical model is developed for simulating the vibration behaviors of large-diameter open-ended pipe piles (OEPPs) and surrounding soil undergoing high-strain impact loading. To describe the soil behavior, the soil along pile shaft is divided into slip and nonslip zones and the base soil is modeled as a fictitious-soil pile (FSP) to account for the wave propagation in the soil. True soil properties are adopted and slippage at the pile-soil interface is considered, allowing realistic representation of large-diameter OEPP mechanics. The developed model is validated by comparing with conventional models and finite element method (FEM). It is further used to successfully simulate and interpret the behaviors of a steel OEPP during the offshore field test. It is found that the variation in the vertical vibrations of shaft soil along radial direction is significant for large-diameter OEPPs, and the velocity amplitudes of the internal and external soil attenuate following different patterns. The shaft soil motion may not attenuate with depth due to the soil slippage, while the wave attenuation at base soil indicates an influence depth, with a faster attenuation rate than that in the pile. The findings from the current study should aid in simulating the vibration behaviors of large-diameter OEPP-soil system under high-strain dynamic loading.