Călin Vamoş

On the self-averaging of dispersion for transport in quasi-periodic random media

In this study we present a numerical analysis for the self-averaging of the longitudinal dispersion coefficient for transport in heterogeneous media. This is done by investigating the mean-square sample-to-sample fluctuations of the dispersion for finite times and finite numbers of modes for a random field using analytical arguments as well as numerical simulations. We consider transport of point-like injections in a quasi-periodic random field with a Gaussian correlation function. In particular, we focus on the asymptotic and pre-asymptotic behaviour of the fluctuations with the aid of a probability density function for the dispersion, and we verify the logarithmic growth of the sample-to-sample fluctuations as earlier reported in Eberhard (2004 J. Phys. A: Math. Gen. 37 2549–71). We also comment on the choice of the relevant parameters to generate quasi-periodic realizations with respect to the self-averaging of transport in statistically homogeneous Gaussian velocity fields.

DETERMINISTIC COMPONENTS IN THE LIGHT CURVE AMPLITUDE OF Y OPH

About two decades after the discovery of the amplitude decline of the light curve of the classical Cepheid Y Oph, its study is resumed using an increased amount of homogenized data and an extended time base. In our approach, the investigation of different time series concerning the light curve amplitude of Y Oph is not only the reason for the present study, but also a stimulus for developing a coherent methodology for studying long- and short-term variability phenomena in variable stars, taking into account the details of concrete observing conditions: amount of data, data sampling, time base, and individual errors of observational data. The statistical significance of this decreasing trend was estimated by assuming its linearity. We approached the decision-making process by formulating adequate null and alternative hypotheses, and testing the value of the regression line slope for different data sets via Monte Carlo simulations. A variability analysis, through various methods, of the original data and of the residuals obtained after removing the linear trend was performed. We also proposed a new statistical test, based on amplitude spectrum analysis and Monte Carlo simulations, intended to evaluate how detectible is a given (linear) trend in well-defined observing conditions: the trend detection probability. The main conclusion of our study on Y Oph is that, even if the false alarm probability is low enough to consider the decreasing trend to be statistically significant, the available data do not allow us to obtain a reasonably powerful test. We are able to confirm the light curve amplitude decline, and the order of magnitude of its slope with a better statistical substantiation. According to the obtained values of the trend detection probability, it seems that the trend we are dealing with is marked by a low detectibility. Our attempt to find signs of possible variability phenomena at shorter timescales ended by emphasizing the relative constancy of our data, within their precision limits.

Ergodic Simulations for Diffusion in Random Velocity Fields

Ergodic simulations aim at estimating ensemble average characteristics of diffusion in random fields from space averages. The traditional approach, based on large supports of the initial concentration in general fails to obtain ergodic simulations. However, such simulations, using single realizations of the velocity, are shown to be feasible if space averages with respect to the location of the initial concentration support are used to estimate ensemble averages.

Ergodic Estimations of Upscaled Coefficients for Diffusion in Random Velocity Fields

Upscaled coefficients for diffusion in ergodic velocity fields are derived by summing up correlations of increments of the position process, or equivalently of the Lagrangian velocity. Ergodic estimations of the correlations are obtained from time averages over finite paths sampled on a single trajectory of the process and a space average with respect to the initial positions of the paths. The first term in this path decomposition of the diffusion coefficients corresponds to Markovian diffusive behavior and is the only contribution for processes with independent increments. The next terms describe memory effects on diffusion coefficients until they level off to the value of the upscaled coefficients. Since the convergence with respect to the path length is rather fast and no repeated Monte Carlo simulations are required, this method speeds up the computation of the upscaled coefficients over methods based on long-time limit and ensemble averages by four orders of magnitude.

Multiscale structure of time series revealed by the monotony spectrum

Observation of complex systems produces time series with specific dynamics at different time scales. The majority of the existing numerical methods for multiscale analysis first decompose the time series into several simpler components and the multiscale structure is given by the properties of their components. We present a numerical method which describes the multiscale structure of arbitrary time series without decomposing them. It is based on the monotony spectrum defined as the variation of the mean amplitude of the monotonic segments with respect to the mean local time scale during successive averagings of the time series, the local time scales being the durations of the monotonic segments. The maxima of the monotony spectrum indicate the time scales which dominate the variations of the time series. We show that the monotony spectrum can correctly analyze a diversity of artificial time series and can discriminate the existence of deterministic variations at large time scales from the random fluctuations. As an application we analyze the multifractal structure of some hydrological time series.