Synchronous State Research Articles

A New Axial Compressor Test Facility for the Investigation of Aerodynamic Damping Featuring an Electromagnetic Excitation System

Abstract Aerodynamic damping simulations involving detailed computational fluid dynamics (CFD) models are nowadays an integral part of turbomachinery design processes, thanks to advancements made in simulation tools and in computer hardware over the last decades. However, the test cases available in the open literature suitable for the validation and continued development of simulation tools is rather limited. On this background, a new test facility is introduced that allows acquiring relevant and high-quality aerodynamic damping test data on axial compressor rotor test objects in a controlled manner. The test facility is built as a closed loop and thus enables operation at variable pressure levels ranging from near-vacuum to overpressure and with different operating media. Overall damping test data are acquired at controlled synchronous and nonsynchronous vibration conditions. While vibrations of the blades at controlled nonsynchronous vibration conditions can be introduced in the test facility by means of a proprietary electromagnetic excitation system that is acting below the hub line, vibrations at controlled synchronous conditions can be introduced from a set of magnets integrated into the casing. Great care has been spent to create a clean test case without any intruding parts or openings in the test section. In addition, an axial compressor blisk is presented, which acts as test object to show the capabilities of the test facility as well as to allow for detailed investigations of aerodynamic damping. While the first part of the article discusses the setup and the instrumentation of the test facility to excite and measure blade vibration in a nonintrusive way, flow-field measurements and the validation accompanying steady-state Reynolds-averaged Navier-Stokes (RANS) simulations are shown in the second part. The third part of the article focuses on aerodynamic damping measurements for seven different modes at three different pressure levels in total. It can be shown that aerodynamic damping is by far the largest contributor to the overall damping.

Order Effects on the Rubber Hand Illusion Expectancy: A Replication and Extension of Lush (2020)

The rubber hand illusion (RHI) is an illusion where the synchronized stroking of a rubber hand, placed in front of a participant, along with their concealed real hand results in a sense of ownership over the rubber hand. The questionnaire used to subjectively measure the RHI incorporated control and illusion statements to address demand characteristics. However, Lush (2020) demonstrated participants expected the illusion upon viewing a video of the RHI procedure despite the absence of tactile stimuli, indicating a lack of control over demand characteristics. In our study, Experiment 1, conducted with a Japanese sample (n = 27), was a preliminary direct replication of Lush (2020). Experiment 2, involving a larger sample (n = 185), also directly replicated Lush’s study and further investigated the influence of condition order on expectancies for the RHI. In both experiments, participants showed stronger agreement with the illusion statement expectancies in the synchronous condition compared to the asynchronous condition. However, this difference was not observed in the control statement expectancies, thereby replicating the outcomes of the original study. In Experiment 2, the group exposed to the asynchronous condition first showed stronger agreement with the illusion statement expectancies in that condition than those exposed to the synchronous condition first. As the RHI has been defined as the difference in direct and indirect measures between synchronous and asynchronous conditions, our results suggest that the methods in the RHI paradigm insufficiently control demand characteristics and order effects; thus, findings based on these methods are confounded by these effects.

Damage assessment in L-shaped bends by nonlinear feature guided waves

L-shaped bends are crucial lightweight structural components in aerospace manufacturing, renowned for their high specific strength and stiffness. However, the geometric configuration of these bends makes them susceptible to stress concentration in the bending zone. Effective technique for damage assessment of the bending region of L-shaped bends is highly demanded. This paper proposes a nondestructive testing method using nonlinear ultrasonic feature guided waves (FGWs) for early damage assessment of L-shaped bends. Semi-analytical finite element (SAFE) method and perfectly matched layer (PML) technique are employed to analyze FGW propagation in L-shaped bends. Three-dimensional finite element models are utilized to investigate the FGWs propagation in the bending region of L-shaped bends in a numerical perspective. Cumulative second harmonic generation (SHG) of FGWs is successfully observed by selecting suitable mode pairs based on synchronism and non-zero power flux conditions, which provides a promising alternative for early damage assessment in L-shaped bends. Experimental findings are consistent with the numerical ones. Subsequently, thermal damage and plastic deformation with varying gradients were introduced into the bending region of L-shaped bends. The use of SHG of selected FGW mode has effectively assessed the damage with an improved sensitivity, compared to conventional linear acoustic one. This investigation substantiates the potential of nonlinear FGWs in detecting early damage in L-shaped bends, offering promise for future industrial testing applications.

Sufficient synchronization conditions for resistively and memristively coupled oscillators of FitzHugh-Nagumo-type

We study the synchronization behavior of a class of identical FitzHugh-Nagumo-type oscillators under adaptive coupling. We describe the oscillators by a circuit model and we provide a sufficient synchronization condition that relies on the shape of the nonlinear conductance’s (i, u)-curve and the connectivity of the adaptive coupling network. The coupling network is allowed to be time-variant, state-dependent and locally adaptive, where we treat memristive coupling elements as a special case. We provide a physical interpretation of synchronization in terms of power dissipation and investigate the sharpness of our condition.

Fluid face but not gender: Enfacement illusion through digital face filters does not affect gender identity.

It has been shown that observing a face being touched or moving in synchrony with our own face increases self-identification with the former which might alter both cognitive and affective processes. The induction of this phenomenon, termed enfacement illusion, has often relied on laboratory tools that are unavailable to a large audience. However, digital face filters applications are nowadays regularly used and might provide an interesting tool to study similar mechanisms in a wider population. Digital filters are able to render our faces in real time while changing important facial features, for example, rendering them more masculine or feminine according to normative standards. Recent literature using full-body illusions has shown that participants' own gender identity shifts when embodying a different gendered avatar. Here we studied whether participants' filtered faces, observed while moving in synchrony with their own face, may induce an enfacement illusion and if so, modulate their gender identity. We collected data from 35 female and 33 male participants who observed a stereotypically gender mismatched version of themselves either moving synchronously or asynchronously with their own face on a screen. Our findings showed a successful induction of the enfacement illusion in the synchronous condition according to a questionnaire addressing the feelings of ownership, agency and perceived similarity. However, we found no evidence of gender identity being modulated, neither in explicit nor in implicit measures of gender identification. We discuss the distinction between full-body and facial processing and the relevance of studying widely accessible devices that may impact the sense of a bodily self and our cognition, emotion and behaviour.

Stochastic synchronization of Kuramoto-oscillator network with pinning control

The issue of time cost for the Kuramoto-oscillator network synchronization has received widespread attention. However, there is no relevant research on the stochastic synchronization of the Kuramoto-oscillator network with partial uncontrollable oscillators. This article investigates the synchronization problem of the Kuramoto-oscillator network in noisy environments using the pinning control strategy and multilayer distributed control. Previous research requires control of all oscillators, which implies a great control cost. To reduce the control cost, the finite/fixed time pinning controllers are designed to provide the conditions for synchronization in noisy environments, and then the upper bounds on the convergence time of the network are estimated. Finally, numerical simulations are performed to justify the theoretical conclusions.

Disorder-Induced Dynamics in Complex Networks

Disorder in parameters appears to influence the collective behavior of complex adaptive networks in ways that might seem unconventional. For instance, heterogeneities may, unexpectedly, lead to enhanced regions of existence of stable synchronization states. This behavior is unexpected because synchronization appears, generically, in symmetric networks with homogeneous components. Related works have, however, misidentified cases where disorder seems to play a critical role in enhancing synchronization, where it is actually not the case. Thus, in order to clarify the role of disorder in adaptive networks, we use normal forms to study, mathematically, when and how the presence of disorder can facilitate the emergence of collective patterns. We employ parameter symmetry breaking to study the interplay between disorder and the underlying bifurcations that determine the conditions for the existence and stability of collective behavior. This work provides a rigorous justification for a certain barycentric condition to be imposed on the heterogeneity of the parameters while studying the synchronization state. Theoretical results are accompanied by numerical simulations, which help clarify incorrect claims of disorder purportedly enhancing synchronization states.

Examining situational interest and its relationship with self-efficacy in asynchronous and synchronous video lectures

Recently, the number of online courses provided for university students around the world has increased substantially. Instruction is delivered most commonly through either asynchronous video lectures (pre-recorded videos with no real-time communication) or synchronous video lectures (live-streamed videos with real-time communication). From a learning perspective, it is important to capture the interest of learners based on specific situational aspects of both asynchronous and synchronous video lectures, and situational interest of learners may vary depending on the overall intrinsic qualities of each modality. Additionally, there may be variations in regards to the way in which self-efficacy interacts with situational interest depending on which modality is used. This study examines survey responses of university students (n = 93) in South Korea to determine if students perceive differences in situational interest between two different conditions: asynchronous video lectures and synchronous video lectures. Additionally, the difference between situational interest levels of learners with low self-efficacy and learners with high self-efficacy within each condition was examined. The main results showed no overall difference in situational interest between the two conditions and no difference in situational interest between learners with low self-efficacy and learners with high self-efficacy in the synchronous video lecture condition. However, there was a significant difference in situational interest found between learners with low self-efficacy and learners with high self-efficacy in the asynchronous video lecture condition. Results are explained through the differing effects of self-efficacy on situational interest based on the amount of autonomy provided in online learning environments.

Sampled-Data Control for Exponential Synchronization of Delayed Inertial Neural Networks With Aperiodic Sampling and State Quantization.

This article is devoted to dealing with exponential synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) under the framework of aperiodic sampling and state quantization. First, by taking the effect of aperiodic sampling and state quantization into consideration, a novel quantized sampled-data (QSD) controller with time-varying control gain is designed to tackle the exponential synchronization of INNs. Second, considering the available information of the lower and upper bounds of each HTVD, a refined Lyapunov-Krasovskii functional (LKF) is proposed. Meanwhile, an improved looped-functional method is utilized to fully capture the characteristic of practical sampling patterns and further relax the positive definiteness requirement for LKF. Consequently, less conservative exponential synchronization conditions with extra flexibility are derived. Finally, a numerical example is employed to demonstrate the effectiveness and advantages of the proposed synchronization method.

Prescribed time formation synchronous tracking of unmanned aerial vehicles with denial of service attacks

This paper addresses the issue of robust prescribed time synchronous tracking control of unmanned aerial vehicles (UAVs) formation with denial of service attacks. A novel prescribed time synchronous tracking control scheme is proposed within a denial of service attack conversion mechanism. Firstly, on account of the UAV formation in the presence of aerodynamic damping and denial of service attacks, a formation cooperative control model is established under a second-order system. Secondly, to perform formation synchronous tracking within a settling time, a time-varying auxiliary function on account of synchronous state errors is designed for the convergence of prescribed time tracking. The control parameters are designed using a key eigenvalue scaling technique under the effect of denial of service attacks on network topology. The proposed scheme is also extended to UAV formation with a detailed-balanced graph. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy.

An Adaptive State Consistency Architecture for Distributed Software-Defined Network Controllers: An Evaluation and Design Consideration

The Physically Distributed Logically Centralized (PDLC) software-defined network (SDN) control plane is physically dispersed across several controllers with a global network view for performance, scalability, and fault tolerance. This design, providing control applications with a global network view, necessitates network state synchronization among controllers. The amount of inter-controller synchronization can affect the performance and scalability of the system. The absence of standardized communication protocols for East-bound SDN interfaces underscores the need for high-performance communication among diverse SDN controllers to maintain consistent state exchange. An inconsistent controller’s network view can significantly impact network effectiveness and application performance, particularly in dynamic networks. This survey paper offers an overview of noteworthy AI and non-AI solutions for PDLC SDN architecture in industry and academia, specifically focusing on their approaches to consistency and synchronization challenges. The suggested PDLC framework achieves an adaptive controller-to-controller synchronization rate in a dynamic network environment.

Adaptive control for synchronization of time‐delayed complex networks with multi‐weights based on semi‐linear hyperbolic PDEs

SummaryThis paper studies the adaptive synchronization of complex spatio‐temporal networks modeled by semi‐linear hyperbolic partial differential equations (CSTNSLHPDEs) as well as considering time‐invariant and time‐varying delays in a one‐dimensional space. Firstly, a distributed adaptive controller is proposed, where different nodes are with different adaptive gains. Secondly, four cases, CSTNSLHPDEs with time‐invariant delays and one single weight, with time‐invariant delays and multi‐weights, with time‐varying delays and one single weight, and with time‐varying delays and multi‐weights, are successively analyzed, and synchronization conditions of these four cases are obtained by using the proposed distributed adaptive controller. In the end, examples illustrate the effectiveness of the proposed distributed adaptive controller.

Global exponential synchronization of switching neural networks with leakage time-varying delays

In this paper, the synchronization problem of a class of switching neural networks with leakage time-varying delays is studied. A system solution-based direct analysis method is proposed to derive the sufficient conditions of global exponential synchronization for master–slave systems. Firstly, the state variable expression of the error system is derived by constructing a suitable regulation function, in which the leakage delays are explicitly transformed outside the state variable. Then, on this basis, the corresponding synchronization conditions is obtained by using the transition condition ingeniously. In addition, the obtained sufficient conditions contain only some simple linear scalar inequalities, which is different from most publications and greatly reduces the computational complexity. Finally, the reliability of the theoretical results is verified by numerical simulation. It is worth noting that the synchronization problem of switching neural networks with time-varying leakage delays is studied for the first time in this paper, and the method adopted does not need to construct any Lyapunov–Krasovskii functionals, which simplifies the proof process.

Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control

SummaryThe cluster synchronization problem of stochastic two‐layer networks with infinite distributed delays is concerned. Firstly, we study the cluster synchronization of the first layer (leader‐layer) network with the average state of each cluster of sets as synchronization target. Secondly, we design a delayed pinning impulsive controller to synchronize the second layer (follower‐layer) network to the first layer network in a mean square cluster sense. Based on stochastic impulsive analysis and Lyapunov stability theory, some sufficient conditions for cluster synchronization are obtained. Finally, the effectiveness of the theoretical results is verified through numerical simulations.

Collective dynamics of coupled Lorenz oscillators near the Hopf boundary: Intermittency and chimera states.

We study collective dynamics of networks of mutually coupled identical Lorenz oscillators near a subcritical Hopf bifurcation. Such systems exhibit induced multistable behavior with interesting spatiotemporal dynamics including synchronization, desynchronization, and chimera states. For analysis, we first consider a ring topology with nearest-neighbor coupling and find that the system may exhibit intermittent behavior due to the complex basin structures and dynamical frustration, where temporal dynamics of the oscillators in the ensemble switches between different attractors. Consequently, different oscillators may show a dynamics that is intermittently synchronized (or desynchronized), giving rise to intermittent chimera states. The behavior of the intermittent laminar phases is characterized by the characteristic time spent in the synchronization manifold, which decays as a power law. Such intermittent dynamics is quite general and is also observed in an ensemble of a large number of oscillators arranged in variety of network topologies including nonlocal, scale-free, random, and small-world networks.

Synchronization transitions in coupled q-deformed logistic maps

The concept of q-deformation has been expanded in a variety of situations including q-deformed nonlinear maps. Coupled q-deformed logistic maps with positive and negative q-diffusive coupling are investigated. The transition to state of synchronized fixed point in the thermodynamic limit for random initial conditions is studied as dynamic phase transitions. Though rare for one-dimensional maps, q-deformed maps show multistability. For weak coupling, the synchronized state may not be observed in the thermodynamic limit with random initial conditions even if it is linearly stable. Thus multistability persists. However, for large lattices with random initial conditions, five well-defined critical points are observed where the transition to a synchronized state occurs. Two of these show continuous phase transition. One of them is in the directed percolation universality class. In another case, the order parameter shows power-law decay superposed with oscillations on a logarithmic scale at the critical point and does not match with known universality classes.

How neuronal morphology impacts the synchronisation state of neuronal networks.

The biophysical properties of neurons not only affect how information is processed within cells, they can also impact the dynamical states of the network. Specifically, the cellular dynamics of action-potential generation have shown relevance for setting the (de)synchronisation state of the network. The dynamics of tonically spiking neurons typically fall into one of three qualitatively distinct types that arise from distinct mathematical bifurcations of voltage dynamics at the onset of spiking. Accordingly, changes in ion channel composition or even external factors, like temperature, have been demonstrated to switch network behaviour via changes in the spike onset bifurcation and hence its associated dynamical type. A thus far less addressed modulator of neuronal dynamics is cellular morphology. Based on simplified and anatomically realistic mathematical neuron models, we show here that the extent of dendritic arborisation has an influence on the neuronal dynamical spiking type and therefore on the (de)synchronisation state of the network. Specifically, larger dendritic trees prime neuronal dynamics for in-phase-synchronised or splayed-out activity in weakly coupled networks, in contrast to cells with otherwise identical properties yet smaller dendrites. Our biophysical insights hold for generic multicompartmental classes of spiking neuron models (from ball-and-stick-type to anatomically reconstructed models) and establish a connection between neuronal morphology and the susceptibility of neural tissue to synchronisation in health and disease.

Exponential Synchronization of Complex Dynamic Networks with Time Delay and Uncertainty via Adaptive Event-Triggered Control

In this paper, exponential synchronization problem of uncertain complex dynamic networks with time delay is studied via adaptive event-triggered control. Considering the influence of external environment, a new dynamic event-triggered mechanism is proposed, in order to reduce the transmission signal among nodes and reduce the consumption of communication resources. Moreover, in the proposed control mechanism, the controller is adaptive, that is, it only works when the triggering conditions are satisfied. Then, according to the designed adaptive event-triggered control strategy, the sufficient conditions for exponential synchronization are obtained by using Lyapunov functions and inequality technique. In addition, it is proved that the system can avoid Zeno behavior. At last, using two examples to verify the feasibility of the results.

Stability analysis of synchronization in long-range temporal networks using theory of dichotomy.

Most of the previous studies on the stability analysis of synchronization in static or time-varying networks are based on the master stability function approach, which is a semi-analytical concept. The necessary and sufficient conditions for synchronization in time-varying networks are challenging problems since the last few years. We focus on the stability analysis of synchronization in time-varying networks, particularly long-range networks. The use of dichotomy theory to derive sufficient conditions for synchronization in this context is an interesting approach. The incorporation of long-range interactions adds complexity and might lead to larger regions of synchronization, providing valuable insights into the dynamics of such networks. Analyzing the co-action of the time-varying nature in the network topology and long-range interactions is a relevant and challenging task, especially when the network is not synchronized. This work appears to explore the interplay between these factors and their impact on synchronization. Additionally, the numerical study considering long-range connections governed by a power-law within the framework of an Erdös-Rényi random network is a practical way to validate and test the analytical results. It is good to see that we are exploring the effects of varying parameters such as rewiring probability, coupling strength, and power-law exponent on the synchronization state.

Synchronization in scale-free neural networks under electromagnetic radiation.

The functional networks of the human brain exhibit the structural characteristics of a scale-free topology, and these neural networks are exposed to the electromagnetic environment. In this paper, we consider the effects of magnetic induction on synchronous activity in biological neural networks, and the magnetic effect is evaluated by the four-stable discrete memristor. Based on Rulkov neurons, a scale-free neural network model is established. Using the initial value and the strength of magnetic induction as control variables, numerical simulations are carried out. The research reveals that the scale-free neural network exhibits multiple coexisting behaviors, including resting state, period-1 bursting synchronization, asynchrony, and chimera states, which are dependent on the different initial values of the multi-stable discrete memristor. In addition, we observe that the strength of magnetic induction can either enhance or weaken the synchronization in the scale-free neural network when the parameters of Rulkov neurons in the network vary. This investigation is of significant importance in understanding the adaptability of organisms to their environment.