1,721,020 research outputs found
Robustness of attractors in tapping mode atomic force microscopy
Full text linkIn this work, we perform a comprehensive analysis of the robustness of attractors in tapping mode atomic force microscopy. The numerical model is based on cantilever dynamics driven in the Lennard–Jones potential. Pseudo-arc-length continuation and basins of attraction are utilized to obtain the frequency response and dynamical integrity of the attractors. The global bifurcation and response scenario maps for the system are developed by incorporating several local bifurcation loci in the excitation parameter space. Moreover, the map delineates various escape thresholds for different attractors present in the system. Our work unveils the properties of the cantilever oscillation in proximity to the sample surface, which is governed by the so-called in-contact attractor. The robustness of this attractor against operating parameters is quantified by means of integrity profiles. Our work provides a unique view into global dynamics in tapping mode atomic force microscopy and helps establishing an extended topological view of the system.Dynamics of Micro and Nano SystemsMicro and Nano Engineerin
Linear and non-linear vibrations of fluid-filled hollow microcantilevers interacting with small particles
No full textLinear and non-linear vibrations of a U-shaped hollow microcantilever beam filled with fluid and interacting with a small particle are investigated. The microfluidic device is assumed to be subjected to internal flowing fluid carrying a buoyant mass. The equations of motion are derived via extended Hamilton's principle and by using Euler-Bernoulli beam theory retaining geometric and inertial non-linearities. A reduced-order model is obtained applying Galerkin's method and solved by using a pseudo arc-length continuation and collocation scheme to perform bifurcation analysis and obtain frequency response curves. Direct time integration of the equations of motion has also been performed by using Adams-Moulton method to obtain time histories and analyze transient cantilever-particle interactions in depth. It is shown that exploiting near resonant non-linear behavior of the microcantilever could potentially yield enhanced sensor metrics. This is found to be due to the transitions that occur as a matter of particle movement near the saddle-node bifurcation points of the coupled system that lead to jumps between coexisting stable attractors.Accepted Author ManuscriptMicro and Nano Engineerin
Imperfection-induced internal resonance in nanotube resonators
Full text linkThrough molecular dynamics simulations, we demonstrate the possibility of internal resonances in single-walled carbon nanotubes. The resonant condition is engineered with a lack of symmetry in the boundary condition and activated by increasing the energy exchange with a coupled thermal bath. The critical temperature threshold for initiating modal interaction is found to be chirality-dependent. By applying the proper orthogonal decomposition algorithm to molecular dynamics time responses, we show how the thermal fluctuations influence the vibrational behaviour of the nanotube leading to both flexural–flexural and flexural–longitudinal resonances. Understanding the interaction between nanotube resonators and the thermal bath is crucial for designing and optimizing their performance for various nanoscale sensing, actuation, and signal processing applications.Dynamics of Micro and Nano System
Non-Smooth Dynamics of Tapping Mode Atomic Force Microscopy
No full textThis study examines the nonlinear dynamics in tapping-mode atomic force microscopy (AFM) with tip-surface interactions that include van der Waals and Derjaguin-Muller-Toporov contact forces. We investigate the periodic solutions of the hybrid system by performing numerical pseudo-arclength continuation. Through the use of bifurcation locus maps in the set of parameters of the discontinuous model, the overall dynamical response scenario is assessed. We demonstrate the influence of various dissipation mechanisms that are related with the AFM touching or lacking contact with the sample. Local and global analyses are used to investigate the stability of the stable solution in the repulsive regime. The impacting nonsmooth dynamics framed within a higher-mode Galerkin discretization is able to capture windows of irregular and complex motion
Revealing compactness of basins of attraction of multi-DoF dynamical systems
Full text linkGlobal properties of Multi-Degrees-of-Freedom (M-DoF) systems, in particular phase space organization, are largely unexplored due to the computational challenge requested to build basins of attraction. To overcome this problem, various techniques have been developed, some trying to improve algorithms and to exploit high speed computing, others giving up to possibility of having the exact phase space organization and trying to extract major information on a probability base. Following the last approach, this work exploits the method of “basin stability” (Menck et al., 2013) in order to drastically reduce the numerical effort. The probability of reaching the attractors is evaluated using a reasonable number of trials with random initial conditions. Then we investigate how this probability depends on particular generalized coordinate or a pair of coordinates. The method allows to obtain information about the basins compactness and reveals particular features of the phase space topology. We focus the study on a 2-DoF multistable paradigmatic system represented by a parametric pendulum on a moving support and model of a Church Bell. The trustworthiness of the proposed approach is enhanced through the comparison with the classical computation of basins of attraction performed in the full range of initial conditions. The proposed approach can be effectively utilized to investigate the phase space in multidimensional nonlinear dynamical systems by providing additional insights over traditional methods.Dynamics of Micro and Nano System
Piecewise integrable neural network: An interpretable chaos identification framework
Full text linkArtificial Neural Networks (ANNs) are an effective data-driven approach to model chaotic dynamics. Although ANNs are universal approximators which easily incorporate mathematical structure, physical information and constrains, they are scarcely interpretable. Here we develop a neural network framework in which the chaotic dynamics is reframed into piecewise models. The discontinuous formulation defines switching laws representative of the bifurcations mecha- nisms, providing to recover the system of differential equations and its primitive (or integral) which describe the chaotic regime
Modeling and Analysis of an Electrically Actuated Microbeam Based on Nonclassical Beam Theory
No full textLe dinamiche culturali della globalizzazione, tra inclusione ed esclusione
No full textDopo aver passato in rassegna le principali teorie sociologiche della globalizzazione, il saggio prende in esame il ruolo delle religioni nello scenario globale, con particolare riguardo ai temi dell'inclusione, dell'esclusione e della determinazione degli elementi universalistici necessari al dialogo interculturale
Size- and temperature-dependent bending rigidity of graphene using modal analysis
No full textThe bending rigidity of two-dimensional (2D) materials is a key parameter for understanding the mechanics of 2D NEMS devices. The apparent bending rigidity of graphene membranes at macroscopic scale differs from theoretical predictions at micro-scale. This difference is believed to originate from thermally induced dynamic ripples in these atomically thin membranes. In this paper, we perform modal analysis to estimate the effective bending rigidity of graphene membranes from the frequency spectrum of their Brownian motion. Our method is based on fitting the resonance frequencies obtained from the Brownian motion in molecular dynamics simulations, to those obtained from a continuum mechanics model, with bending rigidity and pretension as the fit parameters. In this way, the effective bending rigidity of the membrane and its temperature and size dependence, are extracted, while including the effects of dynamic ripples and thermal fluctuations. The proposed method provides a framework for estimating the macroscopic mechanical properties in other 2D nanostructures at finite temperatures.Dynamics of Micro and Nano SystemsApplied MechanicsQN/Steeneken La
Machine learning to probe modal interaction in dynamic atomic force microscopy
Full text linkModal interactions are pervasive effects that commonly emerge in nanomechanical systems. The coupling of vibrating modes can be leveraged in many ways, including to enhance sensing or to disclose complex phenomenologies. In this work we show how machine learning and data-driven approaches could be used to capture intermodal coupling. We employ a quasi-recurrent neural network (QRNN) for identifying mode coupling and verify its applicability on experimental data obtained from tapping mode atomic force microscopy (AFM). Hidden units of the QRNN are monitored to trace fingerprints of modes activation and to quantify their contributions over the global distortion of orbits in the phase space. To demonstrate the broad applicability of the method, the trained model is re-applied over different experiments and on diverse materials. Over a range of tip-sample configurations, dynamic AFM possesses features general enough to be seized by the QRNN and it is not required an ad-hoc re-training for the identification of interacting modes. Our study opens up a route for utilizing established machine learning techniques for rapid recognition of nonlinear complex effect such as internal resonances in nanotechnology. The QRNN analysis is meant to assist AFM sensing operations when exploiting modal interaction to enhance the signal-to-noise ratio of higher harmonics and provide high resolution compositional contrast in multi-frequency AFM applications
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