arXiv subjects

M. Rivera

Publications and source records attributed to M. Rivera.

4 recordsLinked to original sources

arXiv2021

An alternate approach to simulate the dynamics of perturbed liquid drops

Liquid drops when subjected to external periodic perturbations can execute polygonal oscillations. In this work, a simple model is presented that demonstrates these oscillations and their characteristic properties. The model consists of a spring-mass network such that the masses are analogous to liquid molecules and the springs are to intermolecular forces. Neo-Hookean springs are considered to represent these intermolecular forces. The restoring force of a neo-Hookean spring depends nonlinearly on its length such that the force of a compressed spring is much higher than the force of a spring elongated by the same amount. This is equivalent to the incompressibility of liquids, making these springs suitable to simulate the polygonal oscillations. It is shown that this spring-mass network can imitate most of the characteristic features of experimentally reported polygonal oscillations. Additionally, it is shown that the network can execute certain dynamics which so far have not been observed in a perturbed liquid drop. The features of dynamics which are observed in the perturbed network are: polygonal oscillations, rotation of network, numerical relations (rational and irrational) between the frequencies of polygonal oscillations and the forcing signal, and the dependency of the shape of the polygons on the parameters of perturbation.

physics.flu-dyn↗

arXiv2020

Explosive synchronization in temporal networks: A comparative study

We present a comparative study on Explosive Synchronization (ES) in temporal networks consisting of phase oscillators. The temporal nature of the networks is modeled with two configurations: (1) oscillators are allowed to move in a closed two dimensional box such that they couple with their neighbors, (2) oscillators are static and they randomly switch their coupling partners. Configuration (1) is further studied under two possible scenarios: in the first case oscillators couple to fixed numbers of neighbors while in other they couple to all oscillators lying in their circle of vision. Under these circumstances, we monitor the degrees of temporal networks, velocities, and radius of circle of vision of the oscillators, and the probability of forming connections in order to study and compare the critical values of the coupling required to induce ES in the population of phase oscillators.

nlin.AO↗

arXiv2020

Sounds of Leidenfrost drops

We show that when a drop of water is maintained in its Leidenfrost regime, a sound in the form of periodic beats emits from the drop. The process of beat emission involves two distinct frequencies. One component is the frequency of beats itself and second is the frequency of sound in every beat which is emitted when one oscillation in the drop occurs. Experiments have been performed by placing a drop of water over a concave metallic surface and the beats of the drop were recorded by fixing a microphone above the drop. A video camera was also fixed above the drop to record its oscillations. Simple analytical techniques like Fourier and wavelet transforms of the audio signals and image processing of the videos of the drop have been used to gain insight about mechanism of beat emission process. This analysis also helped us in studying the dependence of frequencies, if any, on the radius of the drop and the substrate temperature.

physics.flu-dyn↗

arXiv2015

Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network

In this study, an artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the short-term $x(t+6)$. The performance prediction was evaluated and compared with another studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called {\it stochastic} hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute uncertainties of predictions for noisy Mackey--Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level ($σ_{N}$) from 0.01 to 0.1.

physics.comp-ph↗