Natural disasters such as earthquakes, hurricanes, and other extreme weather pose serious risks to modern critical infrastructure including electrical distribution grids. Recent U.S. government sources [1, 2] suggest that new methodologies for improving system resilience to these events is necessary. Here, we focus on developing methods for designing and upgrading these grids to better withstand and recover from these threats. These tools select from a set of potential distribution grid upgrades proposed by planners, e.g. adding lines, transformers, breakers, and microgrids (modeled as distributed generation), so that the post-event damaged grid meets a minimum standard of service (98% of the critical demand is met and 50% of all other load is served) with minimum upgrade budget. The approach currently addresses events that create relatively homogeneous damage, e.g. ice storms. When the damage is known apriori, a mixed integer programming formulation is tractable, and we present some initial results.

approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

To aid in developing a global biosurveillance program, it is critical to develop a framework to capture and understand the myriad of data streams and evaluate them in context of surveillance goals. Toward this goal, Los Alamos National Laboratory has developed a new method of evaluating the effectiveness of data stream types through the use of a novel concept called the surveillance window, a technique that integrates operational systems analysis, surveillance system analysis and epidemiological analysis. In this presentation application of this methodology to Foot and Mouth Disease, Ebola and Influenza and E.coli related gastrointestinal disease surveillance will be demonstrated.

Multi-Criteria Decision Analysis (MCDA) provides a methodology to approach the problem of biosurveillance system component evaluation. Los Alamos National Laboratory has developed a robust evaluation framework in which we use MCDA to assist in the evaluation of data streams for use in an integrated global biosurveillance system. We identified several potentially useful data streams through this technique and propose that MCDA may be a valuable tool, provided a robust evaluation framework is available.

Living in a closely connected and highly mobile world presents many new mechanisms for rapid disease spread and in recent years, global disease surveillance has become a high priority. In addition, much like the contribution of non-traditional medicine to curing diseases, non-traditional data streams are being considered of value in disease surveillance. Los Alamos National Laboratory is determining the relevance of various data streams through the use of defined metrics and methodologies. An overview of this project will be presented, together with results of data stream evaluation.

In order to assess the utility and effectiveness of different data streams for global disease surveillance, a comprehensive survey of current human, animal, plant, and marine surveillance systems and data streams was undertaken. A relational database has been developed to allow for detailed analyses and searches of data streams and surveillance systems. A need for a biosurveillance one-stop shop has been increasingly called for to help in evaluating what data streams and systems are available and relevant for many different biosurveillance needs and goals. The prototype Biosurveillance Resource Directory is a searchable, dynamic database for biosurveillance system and source information.

In recent years the transmission network expansion planning problem (TNEP) has become increasingly complex. As the TNEP is a non-linear and non-convex optimization problem, researchers have traditionally focused on approximate models of power flows to solve the TNEP. Existing approaches are often tightly coupled to the approximation choice. Until recently these approximations have produced results that are straight-forward to adapt to the more complex (real) problem. However, the power grid is evolving towards a state where the adaptations are no longer easy (e.g. large amounts of limited control, renewable generation) and necessitates new approaches. Recent work on deterministic Discrepancy Bounded Local Search (DBLS) has shown it to be quite effective in addressing this question. DBLS encapsulates the complexity of power flow modeling in a black box that may be queried for information about the quality of proposed expansions. In this paper, we propose a randomization strategy that builds on DBLS and dramatically increases the computational efficiency of the algorithm.

We report experiments wherein groups of particles were allowed to sediment in an otherwise quiescent fluid contained in a large tank. The Reynolds number of the particles, defined as Re = aU /ν, ranged from 93 to 425; here, a is the radius of the spherical particle, U its settling velocity and ν the kinematic viscosity of the fluid. The characteristic size of a cluster, in a plane transverse to gravity, was measured by a ‘cluster variance’(〈 r 2 t 〉); the latter is defined as the mean square of the transverse coordinates of all constituent particles, averaged over a series of runs. The cluster variance, when plotted as a function of time, exhibited two regimes. There was a quadratic growth in the variance at short times(〈 r 2 t 〉 ∝ t 2 ), while for long times, the cluster variance exhibited a slower sublinear growth with 〈 r 2 t 〉 ∝ t 0.67 . A theory, based on isotropic repulsive hydrodynamic interactions between particles, predicts the cluster variance to grow as t 2/3 in the limit of long times. The theoretical framework was originally proposed to describe the long-time self-similar evolution of dilute clusters in the limit Re ≪ 1 Subramanian & Koch ( J. Fluid Mech. , vol. 603, 2008, p. 63), when the probability of wake-mediated interactions between particles remains asymptotically small; the latter requirement is satisfied for homogeneous spherical clusters larger than a critical radius, and is evidently satisfied for planar clusters oriented transversely to gravity. The isotropy of the interactions therefore stems from the isotropy, at large distances, of the disturbance velocity field produced by a single sedimenting particle outside its wake(which contains the compensating inflow to satisfy mass conservation). Herein, the theory is extended to large Re using an empirical correlation for the drag on a sedimenting particle. This allows one to predict, as a function of Re , the numerical prefactors in the expressions for the cluster variance of both spherical and planar clusters; the predictions for the growth exponent remain unchanged. The agreement between the theoretical and experimental growth exponents supports the hypothesis of a self-similar expansion at long times. The prefactor determined from the experimental observations is found to lie between the theoretical predictions for planar and spherical clusters.

Environmental sensors have been deployed in various cities for early detection of contaminant releases into the atmosphere. Event reconstruction and improved dispersion modeling capabilities are needed to estimate the extent of contamination, which is required to implement effective strategies in emergency management. To this end, a stochastic event reconstruction capability that can process information from an environmental sensor network is developed. A probability model is proposed to take into account both zero and non-zero concentration measurements that can be available from a sensor network because of a sensor's specified limit of detection. The inference is based on the Bayesian paradigm with Markov chain Monte Carlo (MCMC) sampling. Fast-running Gaussian plume dispersion models are adopted as the forward model in the Bayesian inference approach to achieve rapid-response event reconstructions. The Gaussian plume model is substantially enhanced by introducing stochastic parameters in its turbulent diffusion parameterizations and estimating them within the Bayesian inference framework. Additionally, parameters of the likelihood function are estimated in a principled way using data and prior probabilities to avoid tuning in the overall method. The event reconstruction method is successfully validated for both real and synthetic dispersion problems, and posterior distributions of the model parameters are used to generate probabilistic plume envelopes with specified confidence levels to aid emergency decisions.

A simulation environment has been developed for the analysis of the inundation consequences for infrastructure systems from extreme flood events covering wide areas. This decision support architecture includes a GIS-based environment for model building and input parameter estimation, simulation integration tools for meteorological, hydrologic, and infrastructure system models and damage assessment tools for infrastructure systems. The GIS-based environment processes readily-available digital elevation models (30-m from the USGS), land use/cover data (30-m NLCD), stream networks from the National Hydrography Dataset (NHD) and soils data from the NRCS (STATSGO) to create stream network, sub-basins, and cross-section shapefiles for river basins selected for analysis. Rainfall predictions are made by a numerical weather model and ingested in gridded format into the simulation environment. Runoff hydrographs are estimated using Green-Ampt infiltration excess runoff prediction and a I D diffusive wave overland flow routing approach. The hydrographs are fed into the stream network and integrated in a dynamic wave routing module using the EPA's Storm Water Management Model (SWMM) to predict flood depth. The flood depths are then transformed into inundation maps and exported for damage assessment. Wide-area assessment is provided through batch-run capability semi-automatically simulating inundation from extreme rainfall over numerous river basins covering multiple states.

Measurements of Lagrangian single-point and multiple-point statistics in a quasi-two-dimensional stratifed layer system are reported. The system consists of a layer of salt water over an immiscible layer of Fluorinert and is forced electromagnetically so that mean-squared vorticity is injected at a well-defined spatial scale. Simultaneous cascades develop in which enstrophy flows predominately to small scales whereas energy cascades, on average, to larger scales. Lagrangian velocity correlations and one- and two-point displacements are measured for random initial conditions and for initial positions within topological centers and saddles. The behavior of these quantities can be understood in terms of the trapping characteristics of long-lived centers, the slow motion near strong saddles, and the rapid fluctuations outside of either centers or saddles.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Michael K. Rivera, W. Brent Daniel, Robert E. Ecke; Coherent vortices in two-dimensional turbulence. Chaos 1 December 2004; 14 (4): S12. https://doi.org/10.1063/1.1821773 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioChaos: An Interdisciplinary Journal of Nonlinear Science Search Advanced Search |Citation Search

Many questions remain in turbulence research---and related fields---about the underlying physical processes that transfer scalar quantities, such as the kinetic energy, between different length scales. Measurement of an ensemble-averaged flux between scales has long been possible using a variety of techniques, but instantaneous, spatially-local realizations of the transfer have not. The ability to visualize scale-to-scale transfer as a field quantity is crucial for developing a clear picture of the physics underlying the transfer processes and the role played by flow structure. A general technique for obtaining these scale-to-scale transfer fields, called the filter approach, is described. The effects of different filters, finite system size, and limited resolution are explored for experimental and numerical data of two-dimensional turbulence.

We present experimental data on the direct enstrophy cascade in decaying two-dimensional turbulence. Velocity and vorticity fields are obtained using particle tracking velocimetry. From those fields we directly compute the enstrophy and energy flux by using a filtering technique inspired by large-eddy simulations. This allows considerable insight into the physical processes of turbulence when compared with structure-function or spectral analysis. The direct cascade of enstrophy is weakly forward, with almost as much backscatter as down-scale enstrophy transfer, whereas the inverse energy cascade is strongly upscale with a modest amount of backscatter.