Fragmentation Experiment and Model for Falling Mercury Drops

The experiment consists of counting and measuring the size of the many fragments observed after the fall of a mercury drop on the floor. The size distribution follows a power-law for large enough fragments. We address the question of a possible crossover to a second, different power-law for small enough fragments. Two series of experiments were performed. The first uses a traditional film photographic camera, and the picture is later treated on a computer in order to count the fragments and classify them according to their sizes. The second uses a modern digital camera. The first approach has the advantage of a better resolution for small fragment sizes. The second, although with a poorer size resolution, is more reliable concerning the counting of all fragments up to its resolution limit. Both together clearly indicate the real existence of the quoted crossover. The model treats the system microscopically during the tiny time interval when the initial drop collides with the floor. The drop is modelled by a connected cluster of Ising spins pointing up (mercury) surrounded by Ising spins pointing down (air). The Ising coupling which tends to keep the spins segregated represents the surface tension. Initially the cluster carries an extra energy equally shared among all its spins, corresponding to the coherent kinetic energy due to the fall. Each spin which touches the floor loses its extra energy transformed into a thermal, incoherent energy represented by a temperature used then to follow the dynamics through Monte Carlo simulations. Whenever a small piece becomes disconnected from the big cluster, it is considered a fragment, and counted. The results also indicate the existence of the quoted crossover in the fragment-size distribution.Comment: 6 pages, 3 figure

Markov Properties of Electrical Discharge Current Fluctuations in Plasma

Using the Markovian method, we study the stochastic nature of electrical discharge current fluctuations in the Helium plasma. Sinusoidal trends are extracted from the data set by the Fourier-Detrended Fluctuation analysis and consequently cleaned data is retrieved. We determine the Markov time scale of the detrended data set by using likelihood analysis. We also estimate the Kramers-Moyal's coefficients of the discharge current fluctuations and derive the corresponding Fokker-Planck equation. In addition, the obtained Langevin equation enables us to reconstruct discharge time series with similar statistical properties compared with the observed in the experiment. We also provide an exact decomposition of temporal correlation function by using Kramers-Moyal's coefficients. We show that for the stationary time series, the two point temporal correlation function has an exponential decaying behavior with a characteristic correlation time scale. Our results confirm that, there is no definite relation between correlation and Markov time scales. However both of them behave as monotonic increasing function of discharge current intensity. Finally to complete our analysis, the multifractal behavior of reconstructed time series using its Keramers-Moyal's coefficients and original data set are investigated. Extended self similarity analysis demonstrates that fluctuations in our experimental setup deviates from Kolmogorov (K41) theory for fully developed turbulence regime.Comment: 25 pages, 9 figures and 4 tables. V3: Added comments, references, figures and major correction