There is considerable research interest on the meaning and measurement of resilience from a variety of research perspectives including those from the hazards/disasters and global change communities. The identification of standards and metrics for measuring disaster resilience is one of the challenges faced by local, state, and federal agencies, especially in the United States. This paper provides a new framework, the disaster resilience of place (DROP) model, designed to improve comparative assessments of disaster resilience at the local or community level. A candidate set of variables for implementing the model are also presented as a first step towards its implementation.

A place-based model for understanding community resilience to natural disasters

This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol’s method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent. Mathematical modeling of complex systems often requires sensitivity analysis to determine how an output variable of interest is influenced by individual or subsets of input variables. A traditional local sensitivity analysis entails gradients or derivatives, often invoked in design optimization, describing changes in the model response due to the local variation of input. Depending on the model output, obtaining gradients or derivatives, if they exist, can be simple or difficult. In contrast, a global sensitivity analysis (GSA), increasingly becoming mainstream, characterizes how the global variation of input, due to its uncertainty, impacts the overall uncertain behavior of the model. In other words, GSA constitutes the study of how the output uncertainty from a mathematical model is divvied up, qualitatively or quantitatively, to distinct sources of input variation in the model [1].

Reliability Engineering and System Safety

Aspects concerning the structure and behaviours of individual networks have been studied intensely in the past decade, but the exploration of interdependent systems in the context of complex networks has started only recently. This article reviews a general framework for modelling the percolation properties of interacting networks and the first results drawn from its study.

/pdf/networks-formed-from-interdependent-networks-56ypv3825y.pdf

Networks formed from interdependent networks

Preface Introduction 1. Fluid field equations and modal analysis in rigid containers 2. Linear forced sloshing 3. Viscous damping and sloshing suppression devices 4. Weakly nonlinear lateral sloshing 5. Equivalent mechanical models 6. Parametric sloshing (Faraday's waves) 7. Dynamics of liquid sloshing impact 8. Linear interaction of liquid sloshing with elastic containers 9. Nonlinear interaction under external and parametric excitations 10. Interactions with support structures and tuned sloshing absorbers 11. Dynamics of rotating fluids 12. Microgravity sloshing dynamics Bibliography Index.

/pdf/liquid-sloshing-dynamics-4apevf1yp2.pdf

Liquid Sloshing Dynamics

Energy is an essential ingredient of socio-economic development and economic growth. Renewable energy sources like wind energy is indigenous and can help in reducing the dependency on fossil fuels. Wind is the indirect form of solar energy and is always being replenished by the sun. Wind is caused by differential heating of the earth's surface by the sun. It has been estimated that roughly 10 million MW of energy are continuously available in the earth's wind. Wind energy provides a variable and environmental friendly option and national energy security at a time when decreasing global reserves of fossil fuels threatens the long-term sustainability of global economy. This paper reviews the wind resources assessment models, site selection models and aerodynamic models including wake effect. The different existing performance and reliability evaluation models, various problems related to wind turbine components (blade, gearbox, generator and transformer) and grid for wind energy system have been discussed. This paper also reviews different techniques and loads for design, control systems and economics of wind energy conversion system.

A review of wind energy technologies

In this paper, we outline a method to characterize the behavior of networked infrastructure for natural hazard events such as hurricanes and earthquakes. Our method includes resilience and interdependency measures. Because most urban infrastructure systems rely on electric power to function properly, we focus on the contribution of power delivery systems to post-event infrastructure recovery. We provide a brief example of our calculations using power delivery and telecommunications data collected post-landfall for Hurricane Katrina. The model is an important component of a scheme to develop design strategies for increased resilience of urban infrastructure for extreme natural hazard scenarios.

Methodology for Assessing the Resilience of Networked Infrastructure

This paper develops an analytical framework with empirical applications to characterize infrastructure failure interdependencies (IFIs). It uses major electrical power outages as the context for understanding how extreme events (within or external to the power system) lead to failures of other infrastructure systems, given a major electrical power outage. The paper takes an empirical approach by examining the patterns of IFIs that occurred in three kinds of events: the August 2003 northeastern North American blackout, the 1998 Quebec ice storm, and a set of three 2004 Florida hurricanes. Data sources include media reports and official ex post assessments of the events. The results characterize IFIs in terms of the sectors affected, and the consequences for society. We developed scales to characterize the consequences of IFIs in terms of impact and extent indices. A comparison is provided of IFIs arising in all five events discussed in the paper, as a basis for considering priorities for risk mitigation. The most significant IFIs in all five events included effects on HVAC in buildings; effects on water systems; effects on health systems, including hospitals and public health efforts, and effects on road transportation systems.

Empirical Framework for Characterizing Infrastructure Failure Interdependencies

The behavior of tuned liquid dampers (TLD) was investigated through laboratory experiments and numerical modeling. Large amplitude excitation is the primary focus, as previous research was limited to small amplitude motion. Time histories of the base shear force and water-surface variations were measured by precisely controlled shaking table tests. The results are compared with a numerical model. The random-choice numerical method was used to solve the fully nonlinear shallow-water wave equations. The results suggest that the model captures the underlying physical phenomenon adequately, including wave breaking, for most of the frequency range of interest and over a wide range of amplitude excitation. It was found that the response frequency of tuned liquid dampers increases as excitation amplitude increases, and the TLD behaves as a hardening spring system. To achieve the most robust system, the design frequency for the damper, if it is computed by the linearized water-wave theory, should be set at the value lower than that of the structure response frequency; hence, the actual nonlinear frequency of the damper matches the structural response. It was found that, even if the damper frequency had been mistuned slightly, the TLD always performed favorably; we observed no adverse effect in the wide range of experimental parameters tested in this study.

Investigation of Tuned Liquid Dampers under Large Amplitude Excitation

The Tuned Liquid Damper (TLD) is modelled numerically as an equivalent tuned mass damper with non-linear stiffness and damping. These parameters are derived from extensive experimental results described in References 1 and 2. This Non-linear Stiffness and Damping (NSD) model captures the behaviour of the TLD system adequately under a variety of loading conditions. In particular, the NSD model incorporates the stiffness hardening property of the TLD under large amplitude excitation.

Dorothy A. Reed

Papers

Methodology for Assessing the Resilience of Networked Infrastructure

Empirical Framework for Characterizing Infrastructure Failure Interdependencies

Investigation of Tuned Liquid Dampers under Large Amplitude Excitation

A non‐linear numerical model of the tuned liquid damper

Analysis of uniformly and linearly distributed mass dampers under harmonic and earthquake excitation