Persistence analysis of velocity and temperature fluctuations in convective surface layer turbulence

Persistence is defined as the probability that the local value of a fluctuating field remains at a particular state for a certain amount of time, before being switched to another state. The concept of persistence has been found to have many diverse practical applications, ranging from non-equilibrium statistical mechanics to financial dynamics to distribution of time scales in turbulent flows and many more. In this study, we carry out a detailed analysis of the statistical characteristics of the persistence probability density functions (PDFs) of velocity and temperature fluctuations in the surface layer of a convective boundary layer, using a field-experimental dataset. Our results demonstrate that for the time scales smaller than the integral scales, the persistence PDFs of turbulent velocity and temperature fluctuations display a clear power-law behaviour, associated with self-similar eddy cascading mechanism. Moreover, we also show that the effects of non-Gaussian temperature fluctuations act only at those scales which are larger than the integral scales, where the persistence PDFs deviate from the power-law and drop exponentially. Furthermore, the mean time scales of the negative temperature fluctuation events persisting longer than the integral scales are found to be approximately equal to twice the integral scale in highly convective conditions. However, with stability this mean time scale gradually decreases to almost being equal to the integral scale in the near neutral conditions. Contrarily, for the long positive temperature fluctuation events, the mean time scales remain roughly equal to the integral scales, irrespective of stability

Hysteretic behavior of spatially coupled phase-oscillators

Motivated by phenomena related to biological systems such as the synchronously flashing swarms of fireflies, we investigate a network of phase oscillators evolving under the generalized Kuramoto model with inertia. A distance-dependent, spatial coupling between the oscillators is considered. Zeroth and first order kernel functions with finite kernel radii were chosen to investigate the effect of local interactions. The hysteretic dynamics of the synchronization depending on the coupling parameter was analyzed for different kernel radii. Numerical investigations demonstrate that (1) locally locked clusters develop for small coupling strength values, (2) the hysteretic behavior vanishes for small kernel radii, (3) the ratio of the kernel radius and the maximal distance between the oscillators characterizes the behavior of the network

RANDOM WALK APPROACH FOR SIMULATION OF PARTICLE DEPOSITION FROM TURBULENT FLOWS

This study deals with a random walk simulation of particle transport and deposition from a stationary, isotropic turbulent flow: This is an inplernentation of the well-known Lagrangian approach. which treats the disperse phase as many particles. The trajectory of each particle is calculated according to the equations of the mottion assuming a discrete eddy-field. The ensemble-ayeraged quantities describe the behavior of the particle-fluid system, and these have been used to validate numerical solutions of a kinetic (probability density function transport) equation which models the same system. In this work we have only considered relatively large particles: particle-particle interactions and the influence of the particle phase on fluid phase have been neglected

LARGE-EDDY SIMULATION OF TURBULENT PLANE COUETTE FLOW

The purpose of this study was to explore the central core region of a plane turbulent Cou- ette flow by means of large-eddy simulations. First it was demonstrated how accurately a low Reynolds number flow could be simulated. After having verified the reliability of the LES approach. simulations were performed at a substantially higher Re. It was observed that the mean velocity exhibited a practically linear variation in the core region. The extent of the core increased with Re, whereas the slope of the mean velocity profile was significantly reduced

Genetic Algorithm for Combinatorial Path Planning: The Subtour Problem

The purpose of this paper is to present a combinatorial planner for autonomous systems. The approach is demonstrated on the so-called subtour problem, a variant of the classical traveling salesman problem (TSP): given a set of possible goals/targets, the optimal strategy is sought that connects ≤ goals. The proposed solution method is a Genetic Algorithm coupled with a heuristic local search. To validate the approach, the method has been benchmarked against TSPs and subtour problems with known optimal solutions. Numerical experiments demonstrate the success of the approach