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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term. The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗
NASA NTRS2020

Eastern Washington Disasters: Integrating NASA Earth Observations to Analyze Spatiotemporal Distributions of Lightning-Caused Wildfires in Eastern Washington

According to the Washington Department of Natural Resources, roughly 36% of large fires in the state since 2010 were caused by lightning. General trends also show a greater increase in the number of lightning-ignited fires over the last three decades. The NASA DEVELOP Eastern Washington Disasters team partnered with The Nature Conservancy’s Washington Chapter to analyze the relationship between lightning strikes and wildfire events in Eastern Washington, with an emphasis on Kittitas and Yakima Counties. Using the International Space Station Lightning Imaging Sensor, the Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager vegetation moisture index, and Washington Department of Natural Resources historical fire data, the team generated a lightning-caused fire vulnerability index for 2001-2019. Climatology maps of lightning, wildfire, and vegetation moisture of the study area, along with an Esri ArcGIS StoryMap, further communicated project findings. The project results demonstrated that spatiotemporal patterns of lightning-ignited wildfires in Eastern Washington can be useful to inform land management practices and better predict areas that may be more vulnerable to these events.

Disasters↗
NASA NTRS2020

Eastern Washington Disasters: Integrating NASA Earth Observations to Analyze Spatiotemporal Distributions of Lightning-Caused Wildfires in Eastern Washington

According to the Washington Department of Natural Resources, roughly 36% of large fires in the statesince 2010 were caused by lightning. General trends also show a greater increase in the number of lightning-ignited fires over the last three decades. The NASA DEVELOP Eastern Washington Disasters team partnered with The Nature Conservancy’s Washington Chapter to analyze the relationship between lightning strikes and wildfire events in Eastern Washington, with an emphasis on Kittitas and Yakima Counties. Using the International Space Station Lightning Imaging Sensor, the Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager vegetation moisture index, and Washington Department of Natural Resources historical fire data, the team generated a lightning-caused fire vulnerability index for 2001-2019. Climatology maps of lightning, wildfire, and vegetation moisture of the study area, along with an Esri ArcGIS StoryMap, further communicated project findings. The project results demonstrated that spatiotemporal patterns of lightning-ignited wildfires in Eastern Washington can be useful to inform land management practices and better predict areas that may be more vulnerable to these events.

Disasters↗
NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission TRMM Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗
NASA NTRSDate not supplied

Application of Earth Observation Data for Improved Environmental and Disaster Monitoring in Central America

Since its establishment in 1991, the Central American Integration System (SICA, in Spanish) has served as a force for integrating the eight countries of Central America and the Dominican Republic, and in recent years, SICA’s General Secretariat and its various technical secretariats have focused on integrating Earth observations into various regional and national processes. In 2019, for instance, a joint statement was signed between SICA’s General Secretariat and NASA, toward strengthening the region’s use of Earth observations. SICA has also pursued cooperation with the Group on Earth Observations’ AmeriGEO regional initiative, the Japanese Aerospace Agency (JAXA), the United Nations Office of Outer Space Affairs (UNOOSA), and the Copernicus program, among others. Just in the framework of the NASA-SICA Joint Statement, and towards the goal of strengthening the region’s use of Earth observations across sectors, a large number of training webinars has been conducted, particularly benefiting national institutions, non-governmental organizations, and universities. Additionally, targeted support has been provided in the areas of environmental monitoring and disaster management. From 2019 through 2021, for instance, a number of feasibility studies were implemented by NASA’s DEVELOP program in collaboration with SICA technical secretariats. In 2020, in response to devastating Hurricanes Eta and Iota, the SERVIR program also provided support for extending the HYDRAFloods algorithm which had been originally developed for the Mekong to Central America. Support is also being provided toward monitoring land cover change across the Mesoamerican Biological Corridor. In the framework of the “observing the Earth for the benefit of all” theme, this presentation will also focus on how Central America can serve as a case study for the development of Earth observation capacity, applicable to other regions of the world.

applied sciences↗
DOE OSTI2012

Managing the risks of extreme events and disasters to advance climate change adaptation. Special report of the Intergovernmental Panel on Climate Change (IPCC)

This Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) has been jointly coordinated by Working Groups I (WGI) and II (WGII) of the Intergovernmental Panel on Climate Change (IPCC). The report focuses on the relationship between climate change and extreme weather and climate events, the impacts of such events, and the strategies to manage the associated risks. This Special Report, in particular, contributes to frame the challenge of dealing with extreme weather and climate events as an issue in decision making under uncertainty, analyzing response in the context of risk management. The report consists of nine chapters, covering risk management; observed and projected changes in extreme weather and climate events; exposure and vulnerability to as well as losses resulting from such events; adaptation options from the local to the international scale; the role of sustainable development in modulating risks; and insights from specific case studies. (LN)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Recovering from Disaster

From swords to plowshares—a PNNL-developed technology used to monitor compliance with the Comprehensive Nuclear Test-Ban Treaty was applied by Senior Nuclear Scientist Harry Miley for humanitarian purposes. In 2011, a powerful earthquake violently shook northeast Japan, triggering a massive tsunami with 133-foot-high waves that ravaged the land. These catastrophes set in motion a series of equipment failures, explosions, nuclear meltdowns and releases of radiation materials at the Fukushima Nuclear Power Plant in Japan. More than 80,000 residents vacated the surrounding area. It was the largest nuclear disaster since the 1986 explosion at the Chernobyl Nuclear Power Plant in Ukraine. Soon after the accident at Fukushima, Harry and his colleagues were there to help public officials by determining the impact on North America, the radiation dose to people, and the safety of milk and harvested foods. He used ultra-trace nuclear detection technology to provide crucial information about the nature of the radiological release, its magnitude and its impact on human health in North America.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Defense & Disaster Deployable Turbine Fact Sheet

The U.S. Department of Energy’s (DOE’s) Defense and Disaster Deployable Turbine (D3T) project is evaluating the market potential for rapidly deployable wind energy technologies. The D3T fact sheet outlines the work with military and wind industry stakeholders to develop deployable wind energy technologies that can meet the unique energy needs of defense operations and responses to humanitarian crises.

wind, defense, disaster, deployable, turbine, ener↗
DOE OSTI2022

A treatment-effect model to quantify human dimensions of disaster impacts: the case of Hurricane Maria in Puerto Rico

Here, we propose a supervised learning approach using publicly available panel data to statistically quantify the specific manifestations of human impacts of an extreme event, such as changes number of suicides, substance abuse, excess mortality, and unemployment. This allows us to conceptually focus our framework on human impacts and how by attributing them to disaster events along widely accepted psychological, economic, and social dimensions. Our modified treatment-effect model allows counterfactual baseline conditions to be posited for each manifestation from which an aggregated quantitative multi-faceted measure of human impacts can be determined. The developed statistical methodology could be beneficial to policymakers who must allocate scarce resources to those communities in greater need. We illustrate the applicability of our approach using annual and monthly panel data from 2012 to 2018 encompassing the 2017 Hurricane Maria event across various municipalities in Puerto Rico. Our statistical modeling methodology stands apart since (i) it explicitly and more realistically captures the effect of different human-oriented manifestations of an actual event and (ii) it is flexible enough to accommodate individual preferences of various stakeholders in how they assign importance to multiple manifestations of human impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A software framework for assessing the resilience of drinking water systems to disasters with an example earthquake case study

Water utilities are vulnerable to a wide variety of human-caused and natural disasters. The Water Network Tool for Resilience (WNTR) is a new open source PythonTM package designed to help water utilities investigate resilience of water distribution systems to hazards and evaluate resilience-enhancing actions. In this paper, the WNTR modeling framework is presented and a case study is described that uses WNTR to simulate the effects of an earthquake on a water distribution system. The case study illustrates that the severity of damage is not only a function of system integrity and earthquake magnitude, but also of the available resources and repair strategies used to return the system to normal operating conditions. While earthquakes are particularly concerning since buried water distribution pipelines are highly susceptible to damage, the software framework can be applied to other types of hazards, including power outages and contamination incidents.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Utilising demand response for distribution service restoration to achieve grid resiliency against natural disasters

The increased frequency of power outages due to natural disasters in recent years has highlighted the urgency of enhancing distribution grid resilience. The effective distribution service restoration (DSR) is an important measure for a resilient distribution grid. In this work, the authors demonstrate that DSR can be significantly improved by leveraging the flexibility provided by the inclusion of demand response (DR). The authors propose a framework for this by considering integrated control of household-level flexible appliances to vary the load demand at the distribution-grid level to improve DSR. The overall framework of the proposed system is modelled as a three-step method considering three optimization problems to (i) calculate feasible controllable aggregated load range for each bus, (ii) determine candidate buses to perform DR and their target load demand, and (iii) maintain the load level in each house through home energy management during the DSR, considering uncertainties in load and solar generation sequentially. The optimization problems are formulated as linear programming, mixed-integer linear programming, and multistage stochastic programming (solved using the stochastic dual dynamic programming) models. Finally, case studies performed in the IEEE 123-node test feeder show improvements in resilience in terms of energy restored compared to the restoration process without DR.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Machine learning based energy management system for grid disaster mitigation

The recent increase in infiltration of distributed resources has challenged the traditional operation of power systems. Simultaneously, devastating effects of recent natural disasters have questioned the resilience of power infrastructure for an electricity dependent community. In this study, a solution has been presented in the form of a resilient smart grid network which utilizes Distributed Energy Resources (DERs) and Machine Learning (ML) algorithms to improve the power availability during disastrous events. In addition to power electronics with load categorization features, the presented system utilizes ML tools to use the information from neighbouring units and external sources to make complicated logical decisions directed towards provide power to critical loads at all times. Furthermore, provided model encourages consideration of ML tools as a part of smart grid design process together with power electronics and controls, not as an additional feature.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

The Fukushima nuclear disaster and its effects on media framing of fission and fusion energy technologies

This paper presents results of a comparison of media coverage of fusion and fission energy technologies in three countries (Germany, Spain and Portugal) and in the English language international print media addressing transnational elite, from 2008 to 2012. The analysis showed that the accident in Fukushima in March 2010 did not have significant impact on media framing of nuclear fusion in the major part of print media under investigation. In fact, fusion is clearly dissociated from traditional nuclear (fission) energy and from nuclear accidents. It tends to be portrayed as a safe, clean and unlimited source of energy, although less credited when confronted with research costs, technological feasibility and the possibility to be achieved in a reasonable period of time. On the contrary, fission is portrayed as a hazardous source of energy, expensive when compared to research costs of renewables, hardly a long-term energy option, susceptible to contribute to the proliferation of nuclear weapons or rogue military use. Fukushima accident was consistently discussed in the context of safety problems of nuclear power plants and in many cases appeared not as an isolated event but rather as a reminder of previous nuclear disasters such as Three Miles Island and Chernobyl. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Postearthquake Damage Mapping via Remote Sensing: Lessons From the 2023 Türkiye Disaster

This review addresses the urgent need for scalable, accurate, and reproducible remote sensing solutions following the February 2023 Türkiye earthquakes. It synthesizes the contributions of five peer-reviewed studies published in the IEEE JSTARS Special Issue on postearthquake damage and risk assessment. These studies cover areas such as damage classification with deep learning, fusion of multisource remote sensing data, creation of benchmark datasets, detailed damage mapping, and analysis of geophysical signals using outgoing longwave radiation. The article summarizes the methodological approaches and the practical relevance of the reviewed studies for detecting, evaluating, and quantifying damage, and outlines key challenges, including model generalization, class ambiguity, and data integration. It also discusses emerging trends, including explainable artificial intelligence, multimodal data fusion, and open-data platforms. This synthesis provides a foundation for building robust, interpretable, and real-time disaster response systems and aims to guide future research in earthquake-related Earth observation and rapid damage assessment.

Taskin, Gulsen [Istanbul Technical University] (OR↗
DOE OSTI2023

Developing a Disaster-Ready Power Grid Agent Through Geophysically-Informed Fault Event Scenarios

Management of the nation’s power grid over the coming decades will need to factor multiple climate-driven threats to the power system that can be stressful or even detrimental to its operations. During disasters, grid operators suffer from cognitive overload where the electric grid is severely impacted by the weather, yet grid operators have limited awareness of these factors. Meanwhile, the electric grid is facing an explosion of data coming from a variety of sources which can enable the operator to evaluate the risks and develop mitigation strategies against hazardous events. In this work, we processed heterogeneous environmental and power grid data to learn and model grid behavior caused by extreme weather events. In this study, we focused on two weather-driven hazards, hurricanes and wildfires, which were analysed for the electric grid of Texas. We the used this data to train a Reinforcement Learning (RL) agent by analysing and predicting future behaviour of the grid during those hazard events. All these parts are incorporated in one framework to have a geophysically informed power simulators that can be used to train RL agents.

electic grid, reinforcement learning, climate adap↗
DOE OSTI2016

Resilient Distribution System by Microgrids Formation After Natural Disasters

Microgrids with distributed generation provide a resilient solution in the case of major faults in a distribution system due to natural disasters. This paper proposes a novel distribution system operational approach by forming multiple microgrids energized by distributed generation from the radial distribution system in real-time operations, to restore critical loads from the power outage. Specifically, a mixed-integer linear program (MILP) is formulated to maximize the critical loads to be picked up while satisfying the self-adequacy and operation constraints for the microgrids formation problem, by controlling the ON/OFF status of the remotely controlled switch devices and distributed generation. A distributed multi-agent coordination scheme is designed via local communications for the global information discovery as inputs of the optimization, which is suitable for autonomous communication requirements after the disastrous event. The formed microgrids can be further utilized for power quality control and can be connected to a larger microgrid before the restoration of the main grids is complete. Numerical results based on modified IEEE distribution test systems validate the effectiveness of our proposed scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Next-generation geospatial-temporal information technologies for disaster management

Traditional geographic information systems (GIS) have been disrupted by the emergence of Big Data in the form of geo-coded raster, vector, and time-series Internet-of-Things data. This article discusses the application of new scalable technologies that go far beyond relational databases and file-based storage on spinning disk or tape to incorporate both storage and processing data in the same platform. The roles of the Apache Hadoop Distributed File Systems and NoSQL key-value stores such as the Apache Hbase are discussed, along with indexing schemes that optimally support geospatial-temporal use. Here, we highlight how this new approach can rapidly search multiple GIS data layers to obtain insights in the context of early warning, impact evaluation, response, and recovery to earthquake and wildfire disasters.

97 MATHEMATICS AND COMPUTING↗