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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Extreme events, energy security and equality through micro- and macro-levels: Concepts, challenges and methods

Low-income households face long-standing challenges of energy insecurity and inequality (EII). During extreme events (e.g., disasters and pandemics) these challenges are especially severe for vulnerable populations reliant on energy for health, education, and well-being. However, many EII studies rarely incorporate the micro- and macro-perspectives of resilience and reliability of energy and internet infrastructure and social-psychological factors. To remedy this gap, we first address the impacts of extreme events on EII among vulnerable populations. Second, we evaluate the driving factors of EII and how they change during disasters. Third, we situate these inequalities within broader energy systems and pinpoint the importance of equitable infrastructure systems by examining infrastructure reliability and resilience and the role of renewable technologies. Then, we consider the factors influencing energy consumption, such as energy practices, socio-psychological factors, and internet access. Finally, we propose interdisciplinary research methods to study these issues during extreme events and provide recommendations.

Chen, CF↗
DOE OSTI2020

Improvised Field Expedient Method for Renal Replacement Therapy in a Porcine Model of Acute Kidney Injury

ABSTRACT Objective: Dialysis patients may not have access to conventional renal replacement therapy (RRT) following disasters. We hypothesized that improvised renal replacement therapy (ImpRRT) would be comparable to continuous renal replacement therapy (CRRT) in a porcine acute kidney injury model. Methods: Following bilateral nephrectomies and 2 hours of caudal aortic occlusion, 12 pigs were randomized to 4 hours of ImpRRT or CRRT. In the ImpRRT group, blood was circulated through a dialysis filter using a rapid infuser to collect the ultrafiltrate. Improvised replacement fluid, made with stock solutions, was infused pre-pump. In the CRRT group, commercial replacement fluid was used. During RRT, animals received isotonic crystalloids and norepinephrine. Results: There were no differences in serum creatinine, calcium, magnesium, or phosphorus concentrations. While there was a difference between groups in serum potassium concentration over time ( P < 0.001), significance was lost in pairwise comparison at specific time points. Replacement fluids or ultrafiltrate flows did not differ between groups. There were no differences in lactate concentration, isotonic crystalloid requirement, or norepinephrine doses. No difference was found in electrolyte concentrations between the commercial and improvised replacement solutions. Conclusion: The ImpRRT system achieved similar performance to CRRT and may represent a potential option for temporary RRT following disasters.

Public, Environmental & Occupational Health↗
DOE OSTI2024

A baseline structure inventory with critical attribution for the US and its territories

Leveraging high performance computing, remote sensing, geographic data science, machine learning, and computer vision, Oak Ridge National Laboratory has partnered with Federal Emergency Management Agency (FEMA) to build a baseline structure inventory covering the US and its territories to support disaster preparedness, response, and recovery. The dataset contains more than 125 million structures with critical attribution, and is ready to be used by federal agencies, local government and first responders to accelerate on-the-ground response to disasters, further identify vulnerable areas, and develop strategies to enhance the resilience of critical structures and communities. Data can be freely and openly accessed through Figshare data repository, ESRI’s Living Atlas or FEMA’s Geodata platform.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

LandScan Global 30 Arcsecond Annual Global Gridded Population Datasets from 2000 to 2022

Abstract Oak Ridge National Laboratory (ORNL) annually develops the LandScan Global (LSG) dataset, a 30 arcsecond global gridded population dataset representing global ambient human population distribution. This multivariable dasymetric model disaggregates census counts within administrative boundaries using ancillary data. Each country’s distribution reflects cultural and socioeconomic patterns; manual validations yield a unique global dataset for assessing populations at risk. For over two decades, LSG has been a standard for estimating populations at risk, aiding U.S. federal government, academia and humanitarian organizations. During disasters such as the 2004 Indian Ocean tsunami and the 2010 Haiti earthquake and geopolitical crises such as the Syrian civil war and the 2022 Russian invasion of Ukraine, LSG supported scientific and operational communities in emergency response and recovery. In 2022, LSG datasets from 2000 onward were made publicly available through ORNL’s LandScan Portal. This data descriptor details our methodology and the application of geospatial science and machine learning to geographic and demographic data, highlighting uses in urban resiliency, emergency management, disaster response, and human health and security.

Science & Technology - Other Topics↗
DOE OSTI2022

Characterizing manufacturing sector disruptions with targeted mitigation strategies

It has become clear in recent decades that manufacturing supply chains are increasingly vulnerable to disruptions of varying geographical scales and intensities. These disruptions—whether intentional, accidental, or resulting from natural disasters—cause failures and capacity reductions to manufacturing infrastructure, with lasting effects that can cascade throughout the manufacturing network. An overall lack of understanding of solutions to mitigate disturbances has rendered the challenge of reducing manufacturing supply chain vulnerability even more difficult. Additionally, the variability of disruptions and their impacts complicates policy maker and stakeholder efforts to plan for specific disruptive scenarios. It is necessary to comprehend different kinds of disturbances and group them based on stakeholder-provided metrics to support planning processes and modeling efforts that promote adaptable, resilient manufacturing supply chains. This paper reviews existing methods for risk management in manufacturing supply chains and the economic and environmental impacts of disruptions. In addition, we develop a framework using agglomerative hierarchical clustering to classify disruptions using U.S. manufacturing network data between 2000 and 2021 and characteristic metrics defined in the literature. Our review identifies five groups of disruptions and discusses both general mitigation methods and strategies targeting each identified group. Further, we highlight gaps in the literature related to estimating and including environmental costs in disaster preparedness and mitigation planning. We also discuss the lack of easily available metrics to quantify environmental impacts of disruptions and how such metrics could be included into our methodology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Conditional Experts for Improved Building Damage Assessment Across Satellite Imagery View Angles

Rapid building damage assessment (BDA) is vital in guiding disaster response missions and estimating population distribution across impacted areas. While commercial satellite imagery providers have enabled near-daily monitoring of the Earth, near-realtime assessment of disaster scenarios frequently requires analysis of off-nadir imagery, as satellites are often far from impacted areas for at-nadir post-event imaging to occur Such scenarios are, however, underrepresented in existing BDA datasets and methodologies. With this motivation, we investigate generalization capabilities of current BDA practices across overhead view-angles and strategies for their improvement. Using a labeled dataset of images capturing conflict-related damages, we first train a baseline BDA architecture using imbalanced and balanced datasets with respect to view-angle. Then, we explore conditional convolutions parameterized on image features, image nadir, and their combination as a mechanism for conditioning on view-angles. Experiments demonstrate the limitations of current practice and the potential of conditional mechanisms to increase model robustness to view-angle variations.

Ambrozio Dias, Philipe↗
DOE OSTI2025

Analyzing Infrastructure Interdependencies Using Network-Of-Networks Modeling

Infrastructure networks play a crucial role in our day-to-day lives, and modeling these infrastructure networks can help decisionmakers prepare for and respond to disruptions such as natural disasters or cyberattacks. Because these infrastructure networks depend on each other, it is not sufficient to model a single network in isolation. We build on previous single-network-modeling techniques to develop a methodology for modeling infrastructure interdependencies as a Network-of-Networks. Using distributionlevel data from a real U.S. city on the power grid, road geometry, and hospital locations, we show how to apply this methodology to modeling three of the U.S. Department of Homeland Security's Critical Infrastructure Sectors: Healthcare, Transportation, and Energy. We also analyze three primary metrics before and after a simulated disaster: 1) impact on hospital access; 2) road network impact with the change in betweenness centrality; 3) electric customer outage. We simulate three different disruptions: 1) road flooding from nearby rivers; 2) a malicious actor targeting the road networks; 3) a malicious actor targeting the electric grid. Finally, we discuss how our methodology can be applied to additional infrastructure networks and types of disruption, and how Artificial Intelligence (AI) techniques may be incorporated into this methodology for further research.

Scherrer, Evan [Drake University, Iowa]↗
DOE OSTI2020

PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (T b ) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) T b –rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Automated Operational Forecasting of Monsoon Low Pressure Systems

Monsoon low pressure systems (LPSs) are the dominant rain-bearing weather system of South Asia, often producing extreme precipitation and hydrological disasters in a region inhabited by nearly two billion people. Despite the importance of these storms, no operational system has automatically identified and tracked LPS in real time in numerical weather prediction model output; many commonly used vortex-tracking algorithms are ill suited for monsoon LPS because of the weak winds and cold cores of these systems. Here, we describe a new system that uses optimized algorithms to identify monsoon LPS in short- to medium-range forecasts from the U.S. Global Ensemble Forecast System (GEFS) and a version of the deterministic Global Forecast System (GFS) adapted and used operationally by the Indian Institute of Tropical Meteorology (IITM). We also assess the historical performance of these models in forecasting South Asian monsoon LPS, comparing this with the performance of the Integrated Forecasting System of the ECMWF. We assess the accuracy of model predictions of LPS genesis, position, intensity, and precipitation rates for forecast lead times of 1–5 days, yielding quantitative information on model biases to guide operational forecasters and disaster managers. The system we introduce here could be extended to other low-latitude regions affected by dynamically weak, heavily precipitating atmospheric vortices that are often not included in tropical cyclone inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating Ensemble Predictions of South Asian Monsoon Low Pressure System Genesis

Abstract Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.

Suhas, D. L.↗
DOE OSTI2020

Communication-Constrained Expansion Planning for Resilient Distribution Systems

Distributed generation and remotely controlled switches have emerged as important technologies to improve the resiliency of distribution grids against extreme weather-related disturbances. Therefore it becomes important to study how best to place them on the grid in order to meet a resiliency criteria, while minimizing costs and capturing their dependencies on the associated communication systems that sustain their distributed operations. This paper introduces the Optimal Resilient Design Problem for Distribution and Communication Systems (ORDPDC) to address this need. The ORDPDC is formulated as a two-stage stochastic mixed-integer program that captures the physical laws of distribution systems, the communication connectivity of the smart grid components, and a set of scenarios that specifies which components are affected by potential disasters. The paper proposes an exact branch-and-price algorithm for the ORDPDC that features a strong lower bound and a variety of acceleration schemes to address degeneracy. The ORDPDC model and branch-and-price algorithm were evaluated on a variety of test cases with varying disaster intensities and network topologies. The results demonstrate the significant impact of the network topologies on the expansion plans and costs, as well as the computational benefits of the proposed approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

COVID-19 and the Power Sector in Southeast Asia: Impacts and Opportunities

The COVID-19 pandemic is unique among the range of threats encountered over the last century, and its specific impacts do not necessarily resemble those of other disasters. In the near term, the pandemic is causing demand shocks, workforce impacts, supply chain complications, and a global, economic contraction with long-term consequences as yet unknown. In contrast, with natural disasters the principal impacts are more acute. These include infrastructure destruction, displaced populations, and often localized economic impact; however, the long-term response to both of these threats–pandemics and destructive forces of nature–requires similar approaches to building short-term and long-term resilience that prepare power sectors for safe and reliable operation during times of disruption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Coastal Community Resiliency Enhancement Using Marine Hydrokinetic (MHK) Resources and Networked Microgrids

Enormous challenges are inherent in the operation of modern distribution grids. In particular, distribution feeders can be severely compromised under extreme conditions such as large-scale weather events (e.g., hurricanes, floods, and other types of natural disasters). To combat this problem, they turn to local energy support from distributed energy resources, such as solar and wind, marine hydrolinetic (MHK), and other types of energy. In this report, we investigate the actual characteristics of MHK resources to enhance coastal community resiliency. Two typical types of MHK resources (i.e., wave and tidal resources) illustrate the coastal resilience enhancement framework. By identifying their operational characteristics, such as target time-scales and potential power generation variations, we detail a holistic coastal community resiliency enhancement framework that integrates both optimization and control efforts. The optimization problem is formulated to significantly minimize the power outage time at the critical infrastructures in coastal communities by implementing multiple autonomous and dynamic microgrids around MHK resources. Therefore, during severe power outages caused by natural disasters (e.g., floods, hurricanes), the coastal community load profiles can be satisfied using local MHK resources in each microgrid with limited or no power support from upstream power grids.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Battery Storage for Resilience

As the capital costs of battery storage systems are decreasing, new opportunities to cost-effectively deploy the technology, often paired with renewable energy technologies, are emerging. At the same time, the duration and frequency of natural disasters is increasing. As a result, a growing number of institutions are deploying battery storage systems as a resilient energy solution because traditional back-up power solutions, like diesel generators, are not always sufficient, especially during longer-duration and larger-scale disasters. This fact sheet describes how battery storage, along with additional generation sources, can be used both to provide cost savings while grid-connected and to provide backup power when the grid goes down.

25 ENERGY STORAGE↗
DOE OSTI2022

AI-Based Protective Relays for Electric Grid Resiliency

The protection systems (circuit breakers, relays, reclosers, and fuses) of the electric grid are the primary component responding to resilience events, ranging from common storms to extreme events. The protective equipment must detect and operate very quickly, generally <0.25 seconds, to remove faults in the system before the system goes unstable or additional equipment is damaged. The burden on protection systems is increasing as the complexity of the grid increases; renewable energy resources, particularly inverter-based resources (IBR) and increasing electrification all contribute to a more complex grid landscape for protection devices. In addition, there are increasing threats from natural disasters, aging infrastructure, and manmade attacks that can cause faults and disturbances in the electric grid. The challenge for the application of AI into power system protection is that events are rare and unpredictable. In order to improve the resiliency of the electric grid, AI has to be able to learn from very little data. During an extreme disaster, it may not be important that the perfect, most optimal action is taken, but AI must be guaranteed to always respond by moving the grid toward a more stable state during unseen events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Long-Term Chemical Analyses in Biota Collected Upstream of the Pajarito Canyon Flood Retention Structure and Los Alamos Canyon Weir at Los Alamos National Laboratory

The Cerro Grande wildfire was a major disaster that burned during the summer of 2000 in the Jemez Mountains and parts of Los Alamos National Laboratory (LANL or Laboratory). As an immediate response to the fire, the United States Department of Energy generated a list of post-fire actions needed to mitigate the impacts of this disaster, including flooding and the potential for erosion. Two structures built post-fire include the Pajarito Canyon flood retention structure (FRS) and the Los Alamos Canyon weir. Environmental monitoring at these two structures has occurred since the early 2000s as part of ongoing efforts to evaluate impacts of the fire.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Utilizing HYSPLIT for Emergency Response Modeling at SRS

The threat of a disaster happening at the Savannah River Site (SRS) is low, but not zero. When an emergency arises, emergency responders utilize a variety of tools to detect, track, and mitigate the disaster. HYSPLIT is a community atmospheric dispersion model that is highly configurable and has been used operationally to simulate particle, chemical, and radionuclide releases. We have developed a preliminary user interface (UI) that configures HYSPLIT to run for a variety of Emergency Action Level (EAL) scenarios at SRS. Using a hypothetical EAL scenario of an accidental release of Pu-238 in H-area and K-area onsite, an analysis was done to show how a preliminary UI framework allows HYSPLIT to be run on a terminal without the need form an active network connection. The study was successful in multiple aspects. For the preliminary UI, users were able to input certain characteristics of the hypothetical EAL such as hours of emission (how long was Pu-238 being released), curies of release, release start time (when does the model run start), and type of radionuclide directly into the terminal. For HYSPLIT, the model was successful in being able to take the UI input, calculate the results from the inputs and output multiple figures of both total volumetric airborne concentrations and surface deposition. We used the Pu-238 Derived Intervention Level (DIL) deposition thresholds for produce, dairy, and beef for visualization of the surface deposition, showing how a release would impact ingestion pathways.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Introduction to Microgrids

A microgrid is simply a typical power system that has distributed energy resources (DERs), like small diesel generators, with loads in the network. The resiliency offered by microgrids to retain service during system events makes them a suitable support for the electric grid during natural disasters. One key factor that differentiates microgrids from a typical distribution grid is its ability to disconnect itself in the event of a blackout and sustain its loads through DERs. Microgrid systems will reconnect to the main grid when the grid comes back up. The resiliency offered by microgrids to retain service during system events makes them a suitable support for the electric grid during natural disasters. This presentation discusses the typical key steps involved in microgrid planning, installation, operation in a interoperable and interconnectable fashion.

24 POWER TRANSMISSION AND DISTRIBUTION↗