R-3 Repository :: Browsing by Author "Dueñas-Osorio, Leonardo"

Browsing by Author "Dueñas-Osorio, Leonardo"

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A Closed-Form Technique for the Reliability and Risk Assessment of Wind Turbine Systems
(MDPI, 2012) Mensah, Akwasi F.; Dueñas-Osorio, Leonardo
This paper proposes a closed-form method to evaluate wind turbine system reliability and associated failure consequences. Monte Carlo simulation, a widely used approach for system reliability assessment, usually requires large numbers of computational experiments, while existing analytical methods are limited to simple system event configurations with a focus on average values of reliability metrics. By analyzing a wind turbine system and its components in a combinatorial yet computationally efficient form, the proposed approach provides an entire probability distribution of system failure that contains all possible configurations of component failure and survival events. The approach is also capable of handling unique component attributes such as downtime and repair cost needed for risk estimations, and enables sensitivity analysis for quantifying the criticality of individual components to wind turbine system reliability. Applications of the technique are illustrated by assessing the reliability of a 12-subassembly turbine system. In addition, component downtimes and repair costs of components are embedded in the formulation to compute expected annual wind turbine unavailability and repair cost probabilities, and component importance metrics useful for maintenance planning and research prioritization. Furthermore, this paper introduces a recursive solution to closed-form method and applies this to a 45-component turbine system. The proposed approach proves to be computationally efficient and yields vital reliability information that could be readily used by wind farm stakeholders for decision making and risk management.
A new mutually reinforcing network node and link ranking algorithm
(Macmillan Publishers Limited, 2015) Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.
This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.
Competitive percolation strategies for network recovery
(Springer Nature, 2019) Smith, Andrew M.; Pósfai, Márton; Rohden, Martin; González, Andrés D.; Dueñas-Osorio, Leonardo; D’Souza, Raissa M.
Restoring operation of critical infrastructure systems after catastrophic events is an important issue, inspiring work in multiple fields, including network science, civil engineering, and operations research. We consider the problem of finding the optimal order of repairing elements in power grids and similar infrastructure. Most existing methods either only consider system network structure, potentially ignoring important features, or incorporate component level details leading to complex optimization problems with limited scalability. We aim to narrow the gap between the two approaches. Analyzing realistic recovery strategies, we identify over- and undersupply penalties of commodities as primary contributions to reconstruction cost, and we demonstrate traditional network science methods, which maximize the largest connected component, are cost inefficient. We propose a novel competitive percolation recovery model accounting for node demand and supply, and network structure. Our model well approximates realistic recovery strategies, suppressing growth of the largest connected component through a process analogous to explosive percolation. Using synthetic power grids, we investigate the effect of network characteristics on recovery process efficiency. We learn that high structural redundancy enables reduced total cost and faster recovery, however, requires more information at each recovery step. We also confirm that decentralized supply in networks generally benefits recovery efforts.
How Risk Perceptions Influence Evacuations from Hurricanes
(2011) Stein, Robert M.; Dueñas-Osorio, Leonardo; Buzcu-Guven, Birnur; Subramanian, Devika; Kahle, David; James A. Baker III Institute for Public Policy
In this study, we present evidence supporting the view that people’s perceived risk to hurricane-related hazards can be reduced to a single score that spans different hurricane-induced risk types, and that evacuation behavior is strongly dependent on whether one perceives a high risk to any type of hurricane-related hazards regardless of the hazard type. Our analysis suggests that people are less sensitive to risk type than they are to the general seriousness of the risks. Using this single score, representing a composite risk measure, emergency managers can be informed about the severity of the public’s risk perceptions and might better craft their public directives in ways that minimize disruptive evacuations and achieve greater compliance with government directives.
Improvement of a fully polynomial randomized approximation scheme (FPRAS) for infrastructure system reliability assessment
(8/6/2017) Fu, Bowen; Dueñas-Osorio, Leonardo
Networked systems make the reliability assessment of critical infrastructure computationally challenging given the combinatorial nature of system-level states. Several methods from numerical schemes to analytical approaches, such as Monte Carlo Simulation (MCS) and recursive decomposition algorithms (RDA), respectively, have been applied to this stochastic network problem. Despite progress over several decades, the problem remains open because of its intrinsic computational complexity. As the structural facilities of infrastructure systems continue to in terconnect in network forms, their study steers analysts to develop system reliability assessment methods based on graph theory and network science. A fully polynomial randomized approximation scheme (FPRAS) based on Karger’s graph contraction algorithm is an approximating method for reliability evaluation, which has a unique property rarely exploited in engineering reliability: that by performing a number of experiments in polynomial time (as a function of system size), it provides an a priori theoretical guarantee that the reliability estimate falls into the ϵ-neighborhood of its true value with (1−δ) confidence. We build upon the FPRAS ideas to develop an s-t reliability version that has practical appeal. Focusing on the relevant-cut enumeration stage of the FPRAS, we find correlations between the recurrence frequencies of links in minimum cuts within the randomization phase of the contraction algorithm, and typical network topological properties. We employ LASSO regression analysis to approximate the relationship between link recurrence frequencies and such topological metrics. With the topology-informed link recurrence frequencies, obtained at a much lower computational cost, we use a new biased contraction probability yielding 16.9% more distinct minimum cuts (MinCuts) than the original random contraction scheme. The biased contraction scheme proposed here can significantly improve the efficiency of reliability evaluation of networked infrastructure systems, while supporting infrastructure systems design, maintenance and restoration given its ability to offer error guarantees, which are ideal for future prescriptive guidelines in practice.
Influence of Vertical Ground Motions on the Seismic Fragility Modeling of a Bridge-Soil-Foundation System
(Earthquake Engineering Research Institute, 2013-08) Wang, Zhenghua; Padgett, Jamie E.; Dueñas-Osorio, Leonardo
This paper explores the effects of vertical ground motions (VGMs) on the component fragility of a coupled bridged-soil-foundation (CBSF) system with liquefaction potential, and highlights the unique considerations on the demand and capacity model required for fragility analysis under VGMs. Optimal intensity measures (IMs) that account for VGMs are identified. Moreover, fragility curves that consider capacity change with fluctuating axial force are derived. Results show that the presence of VGMs has a minor effect on the failure probabilities of piles and expansion bearings, while it has a great influence on fixed bearings. Whether VGMs have an impact on column fragilities depends on the design axial load ratio. Finally, more accurate fragility surfaces are derived, which are compared with results of conventional fragility curves. This study highlights the important role that VGMs play in the selection of optimal IMs, and the capacity and fragility representation of certain components of CBSF systems.
Embargo
Network science algorithms for the reliability and resilience of engineered infrastructure networks
(2023-08-11) Fu, Bowen; Dueñas-Osorio, Leonardo
Critical infrastructure networks are essential engineered systems for modern society, encompassing power grids, telecommunication networks, transportation systems, and water distribution networks, among others. Despite their crucial role in maintaining community normalcy, critical infrastructure networks face a variety of threats, such as aging, extreme weather, intentional attacks, chronic hazards, and catastrophic disasters. Any damage to critical infrastructure networks may be significantly amplified by their interdependencies and the increasing interactions with users and operators, ultimately leading to serious economic losses and potential loss of life. As enhancing the reliability and resilience of critical infrastructure systems is a priority for governance and society’s wellbeing,, different fields of research and practice are needed to develop ideas, test them, and implement them. Traditionally, efforts to improve the reliability and resilience of engineered infrastructure systems have focused on individual components within the systems, such as retrofitting and hardening of bridges in highway networks or substations in power grids. However, infrastructure systems are interconnected in network form, and decisions for retrofit or hardening should consider the broader network perspective. Furthermore, infrastructure systems are interconnected among each other, forming a network of networks (NoNs) due to the existence of cross-network dependencies. Thus, it is necessary to investigate the reliability and resilience of infrastructure systems from a network-level perspective. Network science is a dynamic field that has developed sophisticated techniques and algorithms to investigate the properties of networks, structures in networks, and processes on networks. However, the development of advanced techniques inspired by network science to complex problems in engineered infrastructure systems is still limited. We leverage significant developments in algorithms and techniques from network science and understanding of infrastructure systems to benefit their reliability and resilience. Currently, exact reliability computation methods and optimization-based methods are among the most effective strategies for reliability and resilience management of infrastructure systems, due to their guarantees on the accuracy or optimality of the solution. However, the computational complexity of these algorithms for reliability and resilience, is usually beyond affordable. The conflict between desirable models and practical limitations calls for a rational framework which allows decision-makers to choose an effective alternative option when the optimal solution is not feasible. We address this tension with Simon’s theory of satisficing. By selecting a solution that is ‘good enough’ rather than optimal, satisficing theory is applied to the reliability and resilience of engineered infrastructure systems, on problems such as reliability estimators with guarantees on interval and confidence, and close-to-optimal solutions for optimization models, among others. Inspired by the satisficing theory, we build upon techniques and algorithms from network science and proposed algorithms on multiple computationally intractable problems related to the reliability and resilience of infrastructure systems. Funding allocation on retrofit of bridges in highway networks are usually heavily impacted by the influence of politics, which is at odds to the optimal solution from engineering perspective. Our proposed socio-technical ranking algorithm based on a framework inspired by the Katz centrality from network science can effectively integrate network topology, bridge vulnerability information and impact of politics, and provide a compromise between optimality and practicality. We also proposed a principled recovery algorithm based on network partitioning and percolation process in networks, to restore damaged infrastructure systems quickly with supply-demand balance, which is similar to solutions from mixed-integer optimization models. In addition, we introduce the dimension of resilience into the network dismantling problem in network science to identify a dismantling solution that not only breaks the network, but also delays its recovery. By developing an adversarial-dismantling-retrofit strategy, we reveal critical information for long-term resilience enhancement of infrastructure systems. Finally, we investigate the functional relationship between different components of a transportation network, by extracting its dynamical backbones from real-time traffic data, which supports congestion mitigation, traffic intervention and transportation network design. Overall, inspired by community reliability and resilience challenges, my research builds upon network science methods to develop novel algorithms that address resource allocation to bridges in transportation networks, resilience-based restoration using distributed percolation process, resilience-informed network dismantling algorithms and adversarial-dismantling-retrofit strategies, along with mechanisms of resilience for infrastructure systems via functional decompositions to support dynamics-based design and operation. All these algorithms are implementable in practice, unlike their parent methods which remain intractable for large infrastructure problems of today. By being integrated to the Interdependent Networked Community Resilience Modeling Environment (IN-CORE) platform developed by the National Institute of Standards and Technology (NIST), our proposed algorithms will be implemented to support community resilience. Furthermore, as network science is also at the intersection of physical systems and their information processing capabilities, rendering insights from this research useful for emerging developments in system automation, decentralized consensus, and the development of quantum algorithms implementable in noisy-intermediate scale quantum devices.
Path-Dependent Reliability and Resiliency of Critical Infrastructure via Particle Integration Methods
(IASSAR, 2022) Paredes, Roger; Talebiyan, Hesam; Dueñas-Osorio, Leonardo
Critical infrastructure is the backbone of modern societies. To meet increasing demand under resource-constrained and multihazard conditions, policy-makers are tapping into infrastructure resiliency: its capacity to withstand and recover from disruptions. Thus, resiliency-aware uncertainty quantification is key to identify tipping points, yet it remains computationally inaccessible. This paper maps resiliency measures to well understood time-dependent reliability computations, porting insights and methods from reliability theory to the service of critical infrastructure resiliency and upkeep efforts. For large-scale applications, we use particle integration methods (PIMs)—a family of sequential Monte Carlo methods with wide-ranging applications—and propose their optimal tuning in terms of their variance and number of limit-state function evaluations. We obtain consistent and unbiased probability estimates in applications to dynamical systems, network reliability, and resilience analysis, demonstrating PIMs as practical yet under-appreciated tools. For example, we obtain probability estimates of order 10-14 in networks with over 10,000 random variables.
Predicting wind induced damage to residential structures: a machine learning approach
(2015-05-06) Salazar, Josue E; Subramanian, Devika; Zhong, Lin; Dueñas-Osorio, Leonardo
Hurricane winds can cause significant physical damage to residential properties. Pre-storm prediction of wind damage risk allows residents and city emergency officials to plan actions to reduce loss of life and property. In this thesis, I have developed a data-driven machine learning framework to estimate the probability of structural damage risk to a home subject to hurricane force winds. The modeling framework maps a set of predictor variables with the potential to explain structural damage to actual observations of homes damaged by hurricane winds. Widely used wind damage prediction models are parametric and are based on the physics of a struc- ture responding to a wind load. Using a wind damage dataset gathered from about 700,000 residential buildings after Hurricane Ike in 2008 over Harris County, I have built a hybrid machine learning model that combines classification trees and logistic regression. My model is 23.7% more accurate than the physics-based approach at pre- dicting expected damage at the one-kilometer square block level. I demonstrate the robustness of model by using it to predict wind damage to homes in Harris County for simulated hurricanes of category 1 through 5 on the Saffir-Simpson scale. My model produces more accurate pre-storm predictions of wind damage risk which will enable communities to respond to hurricane threats more effectively.
Principled Uncertainty Quantification for Resilient Infrastructure Management
(2022-08-15) Paredes Toro, Roger; Dueñas-Osorio, Leonardo
Uncertainty Quantification (UQ) is the prevalent approach to assess urban infrastructure safety and support decision making, as it accounts for randomness and the lack of perfect information about infrastructure components and the environment. This is especially relevant now that urban infrastructure services are becoming more interdependent and automation is becoming integral to their management. Unfortunately, the task of accurate and efficient UQ is believed to be computationally infeasible in general. Understandably, the state-of-the-practice consists of heuristic UQ methods that make computations affordable; however, they give no assurance of correctness or quality of reliability estimates. Thus, the trust in heuristic analyses to reveal and quantify key vulnerabilities in urban infrastructure is limited, especially as the failure modes of infrastructure are poorly understood. For example, system-level collapse can be the result of interdependencies or cascading failures, as opposed to the more traditional view that focused on the integrity of assets and facilities without quantitative system-level effects. This thesis develops and advances UQ methods that enable evidence-based decision making, i.e., for fixed input data and accepted physical models, non-expert users can independently and confidently obtain reliability estimates that are guaranteed to be correct. Thus, this thesis refers to this family of methods as principled UQ methods. While the bulk of the literature devotes attention to the affordability of computations, at the risk of losing correctness, this work focuses on the quality of computations in general applications. We do not abandon the emphasis on speed of computation, as we pay especial attention to the speed of computations in real-world instances. In the process, we stumble upon a surprising finding: principled UQ methods can vastly outperform state-of-the-art heuristic UQ methods in several case studies. We show evidence of this finding by replicating reliability studies on urban infrastructure and fully eliminating the uncertainty brought about by recent heuristic UQ proposals. Moreover, the generality of the techniques we develop and advance allow for resilience quantification; thus, we unify the insights and challenges in both reliability engineering and resilience engineering, especially rare event simulation and estimation, in the context of highly resilient urban infrastructure. All developments in this thesis are exemplified with applications to both synthetic systems and engineered infrastructure systems.
Probabilistic Fragility of Interdependent Urban Systems Subjected to Seismic Hazards
(2012) Hernandez-Fajardo, Isaac; Dueñas-Osorio, Leonardo
Urban service networks have come under increased pressure due to expansion of urban population, decrease of capital investment, growing interdependence, and man-made and natural hazards. This thesis introduces a simulation-based methodology for the estimation of the fragility of urban networks subjected to earthquake perturbation. The proposed Interdependent Fragility Assessment (IFA) algorithm abstracts the steps required for perturbation-induced damage propagation within and between networks through internal and interdependent links, respectively. Damage propagation uncertainty is accounted by considering conditional probabilities of failure for components and interdependent strengths measuring the likelihood of intersystemic failure propagation. The IFA algorithm is used in four applications. The first application subjected two simplified models of real interdependent urban power and water networks to selected seismic scenarios. Test results showed that interdependence presence worsens systemic fragility, but that the features of interdependence effects were jointly influenced by local fragility properties and interdependence strengths. A second application examined the role of cascading failures caused by component overloading in systemic fragility. The results showed that cascading failures worsen interdependence fragility, and that mitigation actions improving local component capacity have limited effect on controlling interdependent-induced fragility. Two additional conceptual mitigation measures, component fragility reduction ( CFR ) and interdependence redundancy enhancement ( IRE ), were explored. CFR , decreases component seismic fragilities while IRE adds interdependence links to dependent nodes. Test results showed that CFR outperforms IRE ; however, their combination achieved comparable fragility reductions. This outcome highlights the potential of synergistic mitigation policies in controlling interdependent systemic fragility. Finally, the IFA methodology was adapted to use a probabilistic seismic description for the estimation of unconditional systemic fragilities. The hazard description was obtained following an existing approach that uses importance sampling for the generation of intensity maps. The value of the hybrid methodology rests on its capacity to generate unconditional fragility estimates for direct use in risk assessment. Topics for future work include the development of more sophisticated models of cascading failure, the analysis of optimal mitigation actions using mitigation cost-structures and life-cycle costs, the extension of the IFA methodology for perturbation such as hurricanes and flooding, and interdependent fragility studies of theoretical network models.
Pursuing Life-Cycle Sustainability for Bridges Subjected to Multiple Threats
(2014-12-04) Tapia, Citlali; Padgett, Jamie E.; Dueñas-Osorio, Leonardo; Stanciulescu, Ilinca; Blackburn, Jim
Deteriorating bridges, particularly in regions most vulnerable to natural hazards, pose a threat to public safety. With increasing concern regarding sustainability, there is need to consider not only economic, but also environmental and social indicators of performance for bridges under natural hazards. This thesis presents two frameworks: a life-cycle sustainability analysis for quantifying risk-based sustainability indicators of bridge performance and a multi-objective optimization framework for selecting optimal retrofit and repairs based on sustainability objectives. Results highlight impacts retrofits have on reducing expected values of lifetime sustainability indicators and reveal relationships between lifetime environmental, economic, and social sustainability indicators for bridges subject to multiple threats. The frameworks are anticipated to help guide selection of retrofit and repair combinations by providing a set of optimal solutions, which enhance sustainability while ensuring safety and mitigating damage from natural disasters.
Quantification of Lifeline System Interdependencies after the 27 February 2010 Mw 8.8 Offshore Maule, Chile, Earthquake
(Earthquake Engineering Research Institute, 2012-06) Dueñas-Osorio, Leonardo; Kwasinski, Alexis
Data on lifeline system service restoration is seldom exploited for the calibration of performance prediction models or for response comparisons across systems and events. This study explores utility restoration curves after the 2010 Chilean earthquake through a time series method to quantify coupling strengths across lifeline systems. When consistent with field information, cross-correlations from restoration curves without significant lag times quantify operational interdependence, whereas those with significant lags reveal logistical interdependence. Synthesized coupling strengths are also proposed to incorporate cross-correlations and lag times at once. In the Chilean earthquake, coupling across fixed and mobile phones was the strongest per region followed by coupling within and across telecommunication and power systems in adjacent regions. Unapparent couplings were also revealed among telecommunication and power systems with water networks. The proposed methodology can steer new protocols for post-disaster data collection, including anecdotal information to evaluate causality, and inform infrastructure interdependence effect prediction models.
RELIABILITY AND RISK ASSESSMENT OF NETWORKED URBAN INFRASTRUCTURE SYSTEMS UNDER NATURAL HAZARDS
(2013-09-16) Rokneddin, Keivan; Dueñas-Osorio, Leonardo; Padgett, Jamie E.; Rojo, Javier; Wickham, Hadley
Modern societies increasingly depend on the reliable functioning of urban infrastructure systems in the aftermath of natural disasters such as hurricane and earthquake events. Apart from a sizable capital for maintenance and expansion, the reliable performance of infrastructure systems under extreme hazards also requires strategic planning and effective resource assignment. Hence, efficient system reliability and risk assessment methods are needed to provide insights to system stakeholders to understand infrastructure performance under different hazard scenarios and accordingly make informed decisions in response to them. Moreover, efficient assignment of limited financial and human resources for maintenance and retrofit actions requires new methods to identify critical system components under extreme events. Infrastructure systems such as highway bridge networks are spatially distributed systems with many linked components. Therefore, network models describing them as mathematical graphs with nodes and links naturally apply to study their performance. Owing to their complex topology, general system reliability methods are ineffective to evaluate the reliability of large infrastructure systems. This research develops computationally efficient methods such as a modified Markov Chain Monte Carlo simulations algorithm for network reliability, and proposes a network reliability framework (BRAN: Bridge Reliability Assessment in Networks) that is applicable to large and complex highway bridge systems. Since the response of system components to hazard scenario events are often correlated, the BRAN framework enables accounting for correlated component failure probabilities stemming from different correlation sources. Failure correlations from non-hazard sources are particularly emphasized, as they potentially have a significant impact on network reliability estimates, and yet they have often been ignored or only partially considered in the literature of infrastructure system reliability. The developed network reliability framework is also used for probabilistic risk assessment, where network reliability is assigned as the network performance metric. Risk analysis studies may require prohibitively large number of simulations for large and complex infrastructure systems, as they involve evaluating the network reliability for multiple hazard scenarios. This thesis addresses this challenge by developing network surrogate models by statistical learning tools such as random forests. The surrogate models can replace network reliability simulations in a risk analysis framework, and significantly reduce computation times. Therefore, the proposed approach provides an alternative to the established methods to enhance the computational efficiency of risk assessments, by developing a surrogate model of the complex system at hand rather than reducing the number of analyzed hazard scenarios by either hazard consistent scenario generation or importance sampling. Nevertheless, the application of surrogate models can be combined with scenario reduction methods to improve even further the analysis efficiency. To address the problem of prioritizing system components for maintenance and retrofit actions, two advanced metrics are developed in this research to rank the criticality of system components. Both developed metrics combine system component fragilities with the topological characteristics of the network, and provide rankings which are either conditioned on specific hazard scenarios or probabilistic, based on the preference of infrastructure system stakeholders. Nevertheless, they both offer enhanced efficiency and practical applicability compared to the existing methods. The developed frameworks for network reliability evaluation, risk assessment, and component prioritization are intended to address important gaps in the state-of-the-art management and planning for infrastructure systems under natural hazards. Their application can enhance public safety by informing the decision making process for expansion, maintenance, and retrofit actions for infrastructure systems.
Rendezvous and Proximity Operations at the Earth-Moon L2 Lagrange Point: Navigation Analysis for Preliminary Trajectory Design
(2014-02-20) Mand, Kuljit; Spanos, Pol D.; Meade, Andrew J; Dueñas-Osorio, Leonardo; Woffinden, David C
The Earth-Moon L2 point has attracted attention for potential space exploration, and rendezvous is required for several critical operations. The relative dynamics at this location are vastly different from locations rendezvous are typically completed. Rendezvous trajectories must be revised or created anew and a crucial element to consider for any trajectory are the navigation requirements. Working within an LVLH frame, Linearized-Relative (LR) targeting is developed. LR targeting provides the means to create three preliminary trajectories: an L2 adaption of the Double Co-Elliptic, the Line-of-Sight Corridor, and the Line-of-Sight Glide. Through extending the capabilities of linear covariance analysis, the navigation requirements necessary for rendezvous and docking at L2 are derived. These navigation requirements are subsequently utilized for a sensor suite analysis, and an optimal sensor suite for each trajectory is determined.
Resilience Assessment of Electric Grids and Distributed Wind Generation under Hurricane Hazards
(2015-04-21) Mensah, Akwasi Frimpong; Dueñas-Osorio, Leonardo; Padgett, Jamie E; Subramanian, Devika; Stein, Robert M
Modern society is increasingly dependent on high quality electricity for its economy, security, cultural life, public health, safety and governance. As such, ensuring the resilience of electric power systems against deliberate attacks and natural disasters is critical to the continuous function, particularly of urban cities. This thesis proposes an efficient for assessing the resilience of electric power systems under hurricane hazards. The thesis also explores the use of wind turbines as distributed generation to provide back-up electricity during hurricane-induced outages. The study develops computationally efficient models for evaluating outages in electric grids, while demonstrating their applicability through modeling a large real system subject to natural hazards, structural and system responses, and restoration processes. It employs a Bayesian networks approach and uses influence networks constructed via N-1 contingence Direct Current (DC) flow analyses to make the framework computationally tractable, time-efficient and amenable for real-time updating of information via data fusion in the future. The framework computes hurricane-induced customer outages in distributed 1 km2 blocks across the entire system, and simulates system restoration according to resource mobilization practices and sequences identified from historic events. The study uses the Harris County electric grid in Texas under Hurricane Ike in 2008 to illustrate the framework’s application. The framework yields system responses that are in agreement with observed outages, with a mean error of 15.4% in outages aggregated at the ZIP code level. Performance comparison of the proposed framework with two previously existing models shows that the model has a better prediction accuracy and requires a significantly lower computation time than the existing models. The model takes minute and half as compared to more than an hour required by the previous models to run 50 simulations. Having observed widespread outages in the electric power system, with some lasting several days or weeks before power restoration, the study also looked at the reliability of wind turbines to support their integration in the form of distributed generation in power systems. The study introduced a closed-form methodology for computing the system failure probabilities of wind turbines considering different failure event definitions. The methodology is enhanced to incorporate consequences such as downtimes and repair costs of individual component failures, and to determine the turbine unavailability or cost risks. It yields vital reliability information that could be readily used for planning maintenance and forecasting wind power outputs necessary for widespread distributed wind generation. Furthermore, the study examines the use of tuned liquid column dampers (TLCDs) to increase the reliability of wind turbines. Comparison of results for wind turbines with and without the damper shows that a baseline TLCD of 1% mass ratio significantly reduces the structural vibrations (by as much as 47%), and considerably decreases the unavailability probability of a turbine (by up to 8%). Armed with the resilience assessment model for power systems and the reliability analysis tools for wind turbines, the study also develops a probabilistic model for quantifying the impact of distributed wind generation (DWG) on an electric grid during hurricane-induced outage periods. The model incorporates energy adequacy assessment principles while accounting for the uncertainty in electricity demands and in power output due to variability in the wind resource, unavailability of DWG units owing to turbine failures, as well as component failures in the main utility system. An application of the model to Harris County’s power system equipped with turbines of total rated capacity of 1.8 GW shows that the DWG can provide back-up power to up to 85% of the customers in a distribution area which directly connects a DWG unit, while reducing the overall outages in the entire county by 8.5%. Thus, DWG can help improve the resilience of electric grids, support the rapid recovery of hurricane-affect communities and reduce economic losses associated with widespread and prolonged outages. In summary, the study provides computationally efficient tools for exploring a wide range of what-if scenarios in large real energy systems. The models can be readily adapted to consider other emerging technologies such as storage systems, vehicle grids, smart grids and micro grids in electric grid resilience assessments. Thus, they can support resilience-based decisions for hurricane preparedness and mitigation, and restoration strategies that could ensure rapid recovery of the systems. They support efforts in ensuring a reliable and a sustainable supply of electricity during normal conditions or in the immediate aftermath of hurricane events. The outage assessment model, for instance, is directly implemented in the City of Houston’s Storm Risk Calculator, an online tool that informs resident users about the local risks they face from an in-coming hurricane, and the city’s emergency managers in hurricane disaster management. '
Resilience optimization of systems of interdepedent networks
(2017-08-11) Gonzalez Huertas, Andres David; Dueñas-Osorio, Leonardo
Critical infrastructure systems such as water, gas, power, telecommunications, and transportation networks, among others, are constantly stressed by aging and natural disasters. Just since 2001, adverse events such as earthquakes, landslides, and floods, have accounted for economic losses exceeding USD 1.68 trillion. Thus, governments and other stakeholders are giving priority to mitigate the effects of natural disasters over such critical infrastructure systems, especially when considering their increasing vulnerability to interconnectedness. Studying the failure and recovery dynamics of networked systems is an important but complex task, especially when considering the emerging multiplex of networks with underlying interdependencies. In particular, designing optimal mitigation and recovery strategies for networked systems while considering their interdependencies is imperative to enhance their resilience, thus reducing the negative effects of damaging events. Considering this, the present thesis describes a comprehensive body of work that focuses on modeling, understanding, and optimizing the resilience of systems of interdependent networks. To approach these concepts, we have introduced a problem denominated the Interdependent Network Design Problem (INDP), which focuses on optimizing the resource allocation and recovery strategies of interdependent networks after a destructive event, while considering limited resources and operational constraints. To solve the INDP, we describe a mathematical model denominated the time-dependent INDP (td-INDP), which finds the least-cost recovery strategy for a system of physically and geographically interdependent systems, while accounting for realistic operational constraints associated with the limited availability of resources and the finite capacity of the system's elements, among others. Additional analytical and heuristic solution strategies are also introduced, in order to extend and enhance the td-INDP solving capabilities. Additionally, we propose diverse methodological approaches to incorporate uncertainty in the modeling and optimization processes. Particularly, we present the stochastic INDP (sINDP), which can be seen as an extension of the td-INDP that considers uncertainty in parameters of the model, such as the costs, demands, and resource availability, among others. Finally, we explore different multidisciplinary techniques to allow modeling decentralized systems, as well as to enable compressing the main recovery dynamics of a system of interdependent networks by using a time-invariant linear recovery operator. The proposed methodologies enable studying and optimizing pre- and post-event decisions, to improve the performance, reliability, and resilience of systems of coupled infrastructure networks, such that they can better withstand normal demands and damaging hazards. To illustrate each of these methodologies, we study a realistic system of interdependent networks, composed of streamlined versions of the water, power, and gas networks in Shelby County, TN. This problem is of interest, since it does not only describe physical and geographical interdependencies, but is also subject to earthquake hazards due to its proximity to the New Madrid Seismic Zone (NMSZ). We show that the proposed methodologies represent useful tools for decision makers and stakeholders, which can support optimal mitigation and recovery planning.
Resilience-informed infrastructure network dismantling
(9/13/2022) Fu, Bowen; Dueñas-Osorio, Leonardo
Large-scale networked infrastructure systems contribute significantly to modern society. Highly intra- and interconnnected systems enable communities to be more productive, at the expense of becoming more vulnerable to extreme events, cascading failures, and operational demands, including random failures and even targeted attacks. The resilience of infrastructure systems against common but random failure and rare but intentional attacks is critical for safe communities, as it covers multiple other types of contingencies in between. Network dismantling is a process to make the network dysfunctional by removi ng a fraction of components, which provides insights for robustness and resilience under many events, from common to rare. In particular, to protect networks from uncertain dismantling, we need to understand how to optimally fragment networks into small clusters by removing a fraction of their assets with minimal cost. Approximation methods are desirable because finding the optimal dismantling strategy is NP-hard, thus impractical on infrastructure networks. First attempts rely on iterative removal of the nodes with the highest adaptive importance, either from basic centralities, such as degree and betweeness, or from some more advanced metrics like collective influence. However, the additive nature of such methods fails to capture the synergistic nature of the dismantling problem. An algorithm connecting network dismantling problems with network decycling problems, identifies better the collective dismantling set. Other recent strategies add realism by adopting nonuniform node remo val costs, and applying a bisecting algorithm based on weighted spectral approximations iteratively. Despite these efforts, the combinatorial optimization nature of the network dismantling problem still requires global solutions, even if approximated. Additionally, the cost to remove components is the only factor considered in most previous methods. Network resilience, which can inform what to protect from dismantling to facilitate recovery, is seldom included as part of the cost. In this work, we propose a method employing Karger`s contraction algorithm and node-transferring heuristic optimization to approximate the optimal dismantling set, considering both component removal cost and network resilience after dismantling. The proposed method, resilDism, obtains good performance compared to state-of-the-art network dismantling methods, and provides valuable insights to guide network design and resilience enhancement in practice.
Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures. I: Methodology
(Earthquake Engineering Research Institute, 2013) Ghosh, Jayadipta; Rokneddin, Keivan; Padgett, Jamie E.; Dueñas-Osorio, Leonardo
The state-of-the-practice in seismic network reliability assessment of highway bridges often ignores bridge failure correlations imposed by factors such as the network topology, construction methods, and present-day condition of bridges, amongst others. Additionally, aging bridge seismic fragilities are typically determined using historical estimates of deterioration parameters. This research presents a methodology to estimate bridge fragilities using spatially interpolated and updated deterioration parameters from limited instrumented bridges in the network, while incorporating the impacts of overlooked correlation factors in bridge fragility estimates. Simulated samples of correlated bridge failures are used in an enhanced Monte Carlo method to assess bridge network reliability, and the impact of different correlation structures on the network reliability is discussed. The presented methodology aims to provide more realistic estimates of seismic reliability of aging transportation networks and potentially helps network stakeholders to more accurately identify critical bridges for maintenance and retrofit prioritization.
Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures. II: Application
(Earthquake Engineering Research Institute, 2013) Rokneddin, Keivan; Ghosh, Jayadipta; Dueñas-Osorio, Leonardo; Padgett, Jamie E.
The Bridge Reliability in Networks (BRAN) methodology introduced in the companion paper is applied to evaluate the reliability of part of the highway bridge network in South Carolina, USA, under a selected seismic scenario. The case study demonstrates Bayesian updating of deterioration parameters across bridges after spatial interpolation of data acquired from limited instrumented bridges. The updated deterioration parameters inform aging bridge seismic fragility curves through multidimensional integration of parameterized fragility models, which are utilized to derive bridge failure probabilities. The paper establishes the correlation structure among bridge failures from three information sources to generate realizations of bridge failures for network level reliability assessment by Monte Carlo analysis. Positive correlations improve the reliability of the case study network, also predicted from the network topology. The benefits of the BRAN methodology are highlighted in its applicability to large networks while addressing some of the existing gaps in bridge network reliability studies.

Browsing by Author "Dueñas-Osorio, Leonardo"

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