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This study explores the inherent nonlinearity of quarter car models by employing an experimental and numerical approach. The dynamics of vehicular suspension systems are pivotal for ensuring passenger comfort, vehicle stability, and overall ride quality. In this paper we assessed the impact of various parameters and components on suspension performance, enabled the optimization of ride comfort, stability, and handling characteristics. Firstly, experimental analysis allowed for the investigation of factors that are challenging to model theoretically, such as stiffness nonlinearity and damping characteristics, which may vary under different operating conditions. Time domain and frequency response diagram of the model has been obtained. Secondly, a quarter-car with single degree-of-freedom presented and investigated in fractional order form. Fractional order dynamics emphasize nonlinearities in quarter car models, capturing real-world dynamics effectively. The proposed fractional-order nonlinear quarter car model employed Caputo derivative. For numerical analysis of fractional order system, the Adam-Bashforth-Moulton method is used and the disturbance of road assumed to be stochastic. Results show that the dynamic response of the vehicle can be chaotic. Influence of road roughness amplitude and frequency on vehicle vibration is investigated.
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Vibrational energy harvesters are capable of converting low-frequency broad-band mechanical energy into electrical power and can be used in implantable medical devices and wireless sensors. With the use of such energy harvesters, it is feasible to generate continuous power that is more reliable and cost-effective. According to previous findings, the energy harvester can offer rich complex dynamics, one of which is obtaining the synchronization behavior, which is intriguing to achieve desirable power from energy harvesters. Therefore, we consider bistable energy harvesters with periodic and quasiperiodic excitations to investigate synchronization. Specifically, we introduce blinking into the coupling function to check whether it improves the synchronization. Interestingly, we discover that raising the normalized proportion of blinking can initiate synchronization behaviors even with lower optimal coupling strength than the absence of blinking in the coupling (i.e., continuous coupling). The existence of synchronization behaviors is confirmed by finding the largest Lyapunov exponents. In addition, the results show that the optimal coupling strength needed to achieve synchronization for quasiperiodic excitations is smaller than that for periodic excitations.
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Various dynamical properties of four-dimensional mammalian cold receptor model have been discussed widely in the literature considering noise and temperature as important parameters of discussion. Though various spiking and bursting behaviors of the neuron under various noise and temperature conditions studied for a single neuron, no much discussions have been done on the collective behavior. We investigate the collective behavior of these temperature dependent stochastic neurons and unlike the neuron models when forced by periodic external force there is no wave reentry or spiral waves in the network. Hence, we introduce obstacle in the network and depending on the orientation and size of the introduced obstacle, we could show their effects on the wave reentry in the network. Various significant discussions are produced in this paper to confirm that obstacles placed parallel to the wave entry affects the excitability of the tissues significantly compared to those obstacles place perpendicular. We could also show that those obstacles which are lesser in dimensions doesn't affect the excitabilities and hence doesn't contribute for wave reentry. We introduce a new technique to identify wave reentry and spiral waves using the period of individual nodes is proposed. This technique could help us identify even the lowest of excitability change which cannot be seen when using spatiotemporal snapshots.
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Though there are many neuron models based on differential equations, the complexity in realizing them into digital circuits is still a challenge. Hence, many new discrete neuron models have been recently proposed, which can be easily implemented in digital circuits. We consider the well-known FitzHugh-Nagumo model and derive the discrete version of the model considering the sigmoid type of recovery variable and electromagnetic flux coupling. We show the various time series plots confirming the existence of periodic and chaotic bursting as in differential equation type neuron models. Also, we have used the bifurcation plots, Lyapunov exponents, and frequency bifurcations to investigate the dynamics of the proposed discrete neuron model. Different topologies of networks like single, two, and three layers are considered to analyze the wave propagation phenomenon in the network. We introduce the concept of using energy levels of nodes to study the spiral wave existence and compare them with the spatiotemporal snapshots. Interestingly, the energy plots clearly show that when the energy level of nodes is different and distributed, the occurrence of the spiral waves is identified in the network.
RESUMO
Network performance of neurons plays a vital role in determining the behavior of many physiological systems. In this paper, we discuss the wave propagation phenomenon in a network of neurons considering obstacles in the network. Numerous studies have shown the disastrous effects caused by the heterogeneity induced by the obstacles, but these studies have been mainly discussing the orientation effects. Hence, we are interested in investigating the effects of both the size and orientation of the obstacles in the wave re-entry and spiral wave formation in the network. For this analysis, we have considered two types of neuron models and a pancreatic beta cell model. In the first neuron model, we use the well-known differential equation-based neuron models, and in the second type, we used the hybrid neuron models with the resetting phenomenon. We have shown that the size of the obstacle decides the spiral wave formation in the network and horizontally placed obstacles will have a lesser impact on the wave re-entry than the vertically placed obstacles.
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NeurôniosRESUMO
Vibrational energy harvesters can exhibit complex nonlinear behavior when exposed to external excitations. Depending on the number of stable equilibriums, the energy harvesters are defined and analyzed. In this work, we focus on the bistable energy harvester with two energy wells. Though there have been earlier discussions on such harvesters, all these works focus on periodic excitations. Hence, we are focusing our analysis on both periodic and quasiperiodic forced bistable energy harvesters. Various dynamical properties are explored, and the bifurcation plots of the periodically excited harvester show coexisting hidden attractors. To investigate the collective behavior of the harvesters, we mathematically constructed a two-dimensional lattice array of the harvesters. A non-local coupling is considered, and we could show the emergence of chimeras in the network. As discussed in the literature, energy harvesters are efficient if the chaotic regimes can be suppressed and hence we focus our discussion toward synchronizing the nodes in the network when they are not in their chaotic regimes. We could successfully define the conditions to achieve complete synchronization in both periodic and quasiperiodically excited harvesters.
