Metric for attractor overlap

We present the first general metric for attractor overlap (MAO) facilitating an unsupervised comparison of flow data sets. The starting point is two or more attractors, i.e., ensembles of states representing different operating conditions. The proposed metric generalizes the standard Hilbert-space distance between two snapshots to snapshot ensembles of two attractors. A reduced-order analysis for big data and many attractors is enabled by coarse-graining the snapshots into representative clusters with corresponding centroids and population probabilities. For a large number of attractors, MAO is augmented by proximity maps for the snapshots, the centroids, and the attractors, giving scientifically interpretable visual access to the closeness of the states. The coherent structures belonging to the overlap and disjoint states between these attractors are distilled by few representative centroids. We employ MAO for two quite different actuated flow configurations: (1) a two-dimensional wake of the fluidic pinball with vortices in a narrow frequency range and (2) three-dimensional wall turbulence with broadband frequency spectrum manipulated by spanwise traveling transversal surface waves. MAO compares and classifies these actuated flows in agreement with physical intuition. For instance, the first feature coordinate of the attractor proximity map correlates with drag for the fluidic pinball and for the turbulent boundary layer. MAO has a large spectrum of potential applications ranging from a quantitative comparison between numerical simulations and experimental particle-image velocimetry data to the analysis of simulations representing a myriad of different operating conditions.Comment: 33 pages, 20 figure

Hydrodynamic Processes in Massive Stars

The hydrodynamic processes operating within stellar interiors are far richer than represented by the best stellar evolution model available. Although it is now widely understood, through astrophysical simulation and relevant terrestrial experiment, that many of the basic assumptions which underlie our treatments of stellar evolution are flawed, we lack a suitable, comprehensive replacement. This is due to a deficiency in our fundamental understanding of the transport and mixing properties of a turbulent, reactive, magnetized plasma; a deficiency in knowledge which stems from the richness and variety of solutions which characterize the inherently non-linear set of governing equations. The exponential increase in availability of computing resources, however, is ushering in a new era of understanding complex hydrodynamic flows; and although this field is still in its formative stages, the sophistication already achieved is leading to a dramatic paradigm shift in how we model astrophysical fluid dynamics. We highlight here some recent results from a series of multi-dimensional stellar interior calculations which are part of a program designed to improve our one-dimensional treatment of massive star evolution and stellar evolution in general.Comment: 10 pages, 4 figures, IAUS 252 Conference Proceeding (Sanya) - "The Art of Modeling Stars in the 21st Century

Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference

Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, there are still difficulties in practical situations where true DOAs are not on the discretized sampling grid. To deal with such an off-grid DOA estimation problem, this paper studies an off-grid model that takes into account effects of the off-grid DOAs and has a smaller modeling error. An iterative algorithm is developed based on the off-grid model from a Bayesian perspective while joint sparsity among different snapshots is exploited by assuming a Laplace prior for signals at all snapshots. The new approach applies to both single snapshot and multi-snapshot cases. Numerical simulations show that the proposed algorithm has improved accuracy in terms of mean squared estimation error. The algorithm can maintain high estimation accuracy even under a very coarse sampling grid.Comment: To appear in the IEEE Trans. Signal Processing. This is a revised, shortened version of version