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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 109 records · Page 6

Target & Propagation Models for the FINDER Radar

Finding persons still alive in piles of rubble following an earthquake, a severe storm, or other disaster is a difficult problem. JPL is currently developing a victim detection radar called FINDER (Finding Individuals in Emergency and Response). The subject of this paper is directed toward development of propagation & target models needed for simulation & testing of such a system. These models are both physical (real rubble piles) and numerical. Early results from the numerical modeling phase show spatial and temporal spreading characteristics when signals are passed through a randomly mixed rubble pile.

propagation↗

DGs for Service Restoration to Critical Loads in a Secondary Network

During a major outage in a secondary network distribution system, distributed generators (DGs) connected to the primary feeders as well as the secondary network can be used to serve critical loads. This paper proposed a resilience-oriented method to determine restoration strategies for secondary network distribution systems after a major disaster. Technical issues associated with the restoration process are analyzed, including the operation of network protectors, inrush currents caused by the energization of network transformers, synchronization of DGs to the network, and circulating currents among DGs. A look-ahead load restoration framework is proposed, incorporating technical issues associated with secondary networks, limits on DG capacity and generation resources, dynamic constraints, and operational limits. The entire outage duration is divided into a sequence of periods. Restoration strategies can be adjusted at the beginning of each period using the latest information. Numerical simulation of the modified IEEE 342-node low voltage networked test system is performed to validate the effectiveness of the proposed method.

Xu, Yin (ORCID:0000000209459024)↗

Rainfall-driven Flooding Capability Development Report

Globally, floods are among the most frequent and costly natural disasters, both socially and economically. Many of these floods result from excess rainfall collecting in streams and rivers, then overtopping banks and flowing overland into urban environments, damaging critical infrastructure. In Fiscal Year 2012, the National Infrastructure Simulation and Analysis Center’s (NISAC) goal was to enhance its existing flood modeling and simulation capability to be able to estimate flooding from rainfall-runoff events. To accomplish this task, NISAC modified the existing flood modeling code-base using commodity high-performance computing hardware, making it more suitable for regional rainfall events. NISAC also implemented new modules within the existing flood code to account for hydrologic processes, such as spatially variable rainfall and infiltration. Analysts subjected the new hydrologic model to a verification and validation process, wherein analysts evaluated the model using a bench-scale experiment and watershed scale rainfall-runoff events. The bench-scale experiment indicated that the new hydrologic model is mass and momentum conserving and can adequately simulate runoff for depths of inches of water. The watershed scale simulations indicated that the hydrologic model is a good predictor of the discharge hydrograph when compared to observations if the input parameters, such as roughness and hydraulic conductivity, are adequately calibrated. The intended use of the hydrologic model is to estimate flood potential during extreme rainfall events. The accuracy of the predictions is largely dependent on the availability of accurate spatial data for soil and roughness parameters. In addition, each simulation should be accompanied with a calibration exercise, which requires historical rainfall and river discharge observations.

13 HYDRO ENERGY↗

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.

Molthan, Andrew L.↗

Perspective: The Climate-Population-Infrastructure Modeling and Simulation Fertile Area for New Research

Managing the risks posed by climate change and extreme weather to energy production and delivery is a challenge to communities worldwide. As climate conditions change, populations will shift, and demand will re-locate; and networked infrastructures will evolve to accommodate new load centers, and, hopefully, minimize vulnerability to natural disaster. Climate effects such as sea level rise, increased frequency and intensity of natural disasters, force populations to move locations. Displaced population creates new demand for built infrastructure that in turn generates new economic activity that attracts new workers and associated households to the new locations. Infrastructures and their interdependencies will change in reaction to climate drivers as the networks expand into new population areas and as portions of the networks are abandoned as people leave. Thus, infrastructures will evolve to accommodate new load centers while some parts of the network are underused, and these changes will create emerging vulnerabilities. Forecasting the location of these vulnerabilities by combining climate predictions and agent based population movement models shows promise for defining these future population distributions and changes in coastal infrastructure configurations. By combining climate and weather data, engineering algorithms and social theory it has been only recently possible to examine electricity demand response to increased climactic temperatures, population relocation in response to extreme cyclonic events, consequent net population changes and new regional patterns in electricity demand. These emerging results suggest a research agenda of coupling these disparate modelling approaches to understand the implications of climate change for protecting the nation s critical infrastructure.

Climate↗

Return period of extreme rainfall substantially decreases under 1.5 °C and 2.0 °C warming: a case study for Uttarakhand, India

In June 2013, Uttarakhand experienced a hydro-meteorological disaster due to a 4 d extreme precipitation event of return period more than 100 years, claiming thousands of lives and causing enormous damage to infrastructure. Using the weather@home climate modelling system and its Half a degree Additional warming, Prognosis and Projected Impacts simulations, this study investigates the change in the return period of similar events in a 1.5 °C and 2 °C warmer world, compared to current and pre-industrial levels. We find that the likelihood of such extreme precipitation events will significantly increase under both future scenarios. We also estimate the change in extreme river flow at the Ganges; finding a considerable increase in the risk of flood events. Our results also suggest that until now, anthropogenic aerosols may have effectively counterbalanced the otherwise increased meteorological flood risk due to greenhouse gas (GHG) induced warming. Disentangling the response due to GHGs and aerosols is required to analyses the changes in future rainfall in the South Asia monsoon region. More research with other climate models is also necessary to make sure these results are robust.

54 ENVIRONMENTAL SCIENCES↗

Optimal Sizing of Resilience Solutions for the U.S. Army Reserve

Power or water outages in buildings threaten the ability of the military to support surrounding communities during natural disasters. Outages can last for days, weeks or months. Typical solutions include very expensive batteries and onsite generation that are sized based on historical power needs. The U.S. Army Reserve (USAR) has teamed up with the Pacific Northwest National Laboratory (PNNL) to develop a simulation framework that optimizes future power needs to reduce the cost of resilience solutions. The approach starts with the building. Power needs during an emergency event are simulated at the building end-use level, then loads are reduced through the selection of life cycle cost-effective building-level technology improvements. These new optimized loads are then fed into a microgrid sizing tool that dynamically constructs many different combinations of solar photovoltaic, battery storage, and generator resources to meet the load for hundreds of statistically generated outage scenarios. Six site assessments completed by USAR and PNNL in 2019 have resulted in a 1-14% reduction in overall investment when optimizing the buildings first before determining the generation requirements. This approach helps the Army secure critical missions and provide a 14 day-minimum supply of necessary energy and water in the most efficient manner.

buidlings, efficiency, resilience, FEDS, MCOR↗

Wildfire monitoring using Unmanned Aerial Vehicles operating under UTM (STEReO)

STEReO (Scalable Traffic Management for Emergency Response Operations) project at NASA Ames is designed to provide UTM (UAS Traffic Management) services to unmanned aerial vehicles (UAVs) used for natural disaster response scenarios like wildfire and hurricanes. This will facilitate the use of unmanned aerial vehicles in regions where UAVs are currently prohibited to fly. In this paper we describe a complete architecture of using UAVs for wild fire monitoring in this STEReO environment. We simulate a complete fire monitoring scenario in an high fidelity simulation environment. The simulation consists of a fire drill in the vicinity of Redding airport, one of the test sites for CAL-FIRE. The autonomous vehicle connects to the STEReO systems and gathers information of other operation in the vicinity. The vehicle then uses on-board path planners and decision making algorithms for fire monitoring and mapping. In this paper the vehicle on-board architecture is described in details and the requirements to fly and interact with the STEReO system is discussed.

UAV UTM↗

Space-Based Ornithology - Studying Bird Migration and Environmental Change in North America

Natural fluctuations in the availability of critical stopover sites coupled with anthropogenic destruction of wetlands, land-use change, and anticipated losses due to climate change present migratory birds with a formidable challenge. Space based technology in concert with bird migration modeling and geographical information analysis yields new opportunities to shed light on the distribution and movement of organisms on the planet and their sensitivity to human disturbances and environmental changes. At the NASA Goddard Space Flight Center, we are creating ecological forecasting tools for science and application users to address the consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration. We use an individual-based bird biophysical migration model, driven by remotely sensed land surface data, climate and hydrologic data, and biological field observations to study migratory bird responses to environmental change in North America. Simulation allows us to study bird migration across multiple scales and can be linked to mechanistic processes describing the time and energy budget states of migrating birds. We illustrate our approach by simulating the spring migration of pectoral sandpipers from the Gulf of Mexico to Alaska. Mean stopover length and trajectory patterns are consistent with field observations.

Smith, James A.↗

Simulation of Deepwater Horizon oil plume reveals substrate specialization within a complex community of hydrocarbon degraders

The Deepwater Horizon (DWH) accident released an estimated 4.1 million barrels of oil and 10 1 0 mol of natural gas into the Gulf of Mexico, forming deep-sea plumes of dispersed oil droplets and dissolved gases that were largely degraded by bacteria. During the course of this 3-mo disaster a series of different bacterial taxa were enriched in succession within deep plumes, but the metabolic capabilities of the different populations that controlled degradation rates of crude oil components are poorly understood. We experimentally reproduced dispersed plumes of fine oil droplets in Gulf of Mexico seawater and successfully replicated the enrichment and succession of the principal oil-degrading bacteria observed during the DWH event. We recovered near-complete genomes, whose phylogeny matched those of the principal biodegrading taxa observed in the field, including the DWH Oceanospirillales (now identified as a Bermanella species), multiple species of Colwellia, Cycloclasticus, and other members of Gammaproteobacteria, Flavobacteria, and Rhodobacteria. Metabolic pathway analysis, combined with hydrocarbon compositional analysis and species abundance data, revealed substrate specialization that explained the successional pattern of oil-degrading bacteria. The fastest-growing bacteria used short-chain alkanes. The analyses also uncovered potential cooperative and competitive relationships, even among close relatives. We conclude that patterns of microbial succession following deep ocean hydrocarbon blowouts are predictable and primarily driven by the availability of liquid petroleum hydrocarbons rather than natural gases.

54 ENVIRONMENTAL SCIENCES↗

Investigating Resiliency of Transportation Network under Targeted and Potential Climate Change Disruptions

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change-driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. Here, we use the data from the U.S. Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate-driven infrastructure failure, using higher resolution downscaled multiple Earth System Models’ simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30% with about 75% of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in transportation networks.

Climate Change Disruptions↗

Modeling Occupant Core Temperatures Across the Boston Building Stock to Advance Public Health

In recent history, extreme heat has been the cause of most deaths from a natural disaster. Exposure to extreme heat can aggravate preexisting conditions, increase hospitalization, and even cause death. Thus, modeling the thermal resilience of households across the United States will allow for a quantitative assessment of the health and safety risks posed by extreme heat. For the first time, we simulate occupant comfort in representative households across Boston by combining the granular results of the ResStock(TM) model with a two-node heat strain model. This model calculates occupants' core body temperature in each simulated household over a year. We compare the simulated core temperatures to two public health metrics: hyperthermia and heat stroke. Our results show for households in Boston that do not have or use aid conditioning, thousands potentially experience many dangerous heat events each summer and these events can last for more than a day at a time. These heat events peak during the late afternoon and evening, just as residents are coming home, cooking meals, and trying to go to sleep. We have found that multi-family buildings, renters, and low-income homes in more airtight and insulated homes are the most at risk for these events. These results demonstrate the scale and urgency of exposure to extreme heat.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Climate science to inform adaptation policy: Heat waves over India in the 1.5°C and 2°C warmer worlds

Developing a better scientific understanding of anthropogenic climate change and climate variability, especially the prediction/projection of climate futures with useful temporal and geographical resolution and quantified uncertainties, and using that knowledge to inform adaptation planning and action will become crucially important in the coming years. Generating such policy-relevant knowledge may be particularly important for developing countries such as India. It is with this backdrop that, in this paper, we analyze future heat waves in India by using observations and a large number of model simulations of historical, + 1.5 °C, and + 2.0 °C warmer worlds. In both the future scenarios, there is an increased probability of heat waves during June and July when the Indian monsoon is in full swing and humidity is high, which makes the heat events even more of a health risk. While the highest temperatures in heat waves may not increase much in future climates, the duration and areal extent of the heat waves will most likely increase, leading to the emergence of new heat wave-prone zones in India. The results indicate that the joint frequencies of the longest duration and large area events could be nearly threefold greater in the + 1.5 °C and fivefold greater in the + 2.0 °C future scenarios compared to historical simulations. Thus, overall, the study indicates a substantial increase in the risk of heat events that typically elicit warnings from forecasters. The likely widespread and persistent nature of heat wave events in the future, as revealed by this study, will require planning and adaptation measures beyond the short-term disaster planning frameworks currently in place. Exploring what these measures might look like is beyond the scope of this study, but it underlines the importance of developing climate knowledge with high temporal and geographical resolution capable of informing adaptation policy and planning.

+1.5 °C and +2.0 °C↗

Modeling Bird Migration in Changing Habitats: Space-based Ornithology using Satellites and GIS

Understanding bird migration and avian biodiversity is one of the most compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties presenting challenges in both geographic space and time. Space based technology, coupled with geographic information systems, yields new opportunities to shed light on the distribution and movement of organisms on the planet and their sensitivity to human disturbances and environmental changes. At NASA, we are creating ecological forecasting tools for science and application users to address the consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration. In our work, we use individual organism biophysical models and drive these models with satellite observations and numerical weather predictions of the spatio-temporal gradients in climate and habitat. Geographic information system technology comprises one component of our overall simulation framework, especially for characterizing the changing habitats and conditions encountered by en-route migratory birds. Simulation provides a tool for studying bird migration across multiple scales and can be linked to mechanistic processes describing the time and energy budget states of migrating birds. Such models yield an understanding of how a migratory flyway and its component habitats function as a whole and link stop-over ecology with biological conservation and management. We present examples of our simulation of shorebirds, principally, pectoral sandpipers, along the central flyways of the United States and Canada from the Gulf of Mexico to Alaska.

Smith, James A.↗

Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future.

Hong, Yang↗

What if Things Get Worse? Really Grand Challenges for Modeling and Simulation in a Risky and Complex World or Modeling and Simulation for the "Greater Good"?

Today Modeling and Simulation-- ---as an important practice or industry or area of expertise ----- is at a complex crossroad - a sort of cyber-highway--where these complexities meet-technical, economic, environmental, geopolitical and cultural. They may converge or collide. Let's not kid ourselves. It is all too much for anyone person or organization Malcolm Gladwell said it. "We have constructed a world in which the potential for high tech catastrophe is embedded in the fabric of everyday life." We are surrounded by problems that scream at us from our television, Internet and social networks along with billboards and protest signs. We face not just high tech catastrophes but, also, landslides, earthquakes, tornados, floods and hurricanes and large-scale criminality. Evil, war, famine and pestilence have not gone away. It is all too much to think about. My friend, George Peabody, who taught me everything I know about power said that addressing such issues requires that we constantly build our network, information resources and the credibility and visibility of our work. That is how we will build the power of simulation so it can change the world --even maybe, save it. We need all the help we can get and give one another because our human early warning systems appear to be out of kilter. We seem to have trouble imagining how small failings can continue to lead to catastrophic disaster. Think about O-rings and blowout preventers. One is reminded of the old nursery rhyme, "For want of a nail, a shoe was lost! for want of a shoe the horse was lost! for want of a rider the battle was lost and so the kingdom fell." Although the investigation will take more time for real answers, it is worrisome that a rig worker reported to the BBC that-- weeks before the explosion of Deep Ocean Horizon. -he identified a leak in the oil rig's safety equipment -the Control Pod of the blowout preventer which has giant shears designed to cut and seal off the well's main pipe. With both electronics and hydraulics, these are effectively the brains of the blowout preventer. No one fixed it, he alleges, they just shut it down and relied on the other control pod -an act deemed unacceptable by petroleum expert, Tad Patzek, at the University of Texas. The US Congress has identified numerous other problems with the blowout preventer, including design problems and unexpected modifications.

Fifrey, Priscilla↗

Impacts of Fully Coupling Land Surface and Flood Models on the Simulation of Large Wetlands’ Water Dynamics: The Case of the Inner Niger Delta

It is known that representing wetland dynamics in land surface modeling improves models ‘capacity to reproduce fluxes and land surface boundary conditions for atmospheric modeling in general circulation models. This study presents the development of the full coupling between the Noah-MP land surface model (LSM) and the HyMAP flood model in the NASA Land Information System and its application over the Inner Niger Delta (IND), a well-known hot-spot of strong land surface-atmosphere interactions in West Africa. Here, we define two experiments at 0.02o spatial resolution over 2002-2018 to quantify the impacts of the proposed developments on simulating IND dynamics. One represents the one-way approach for simulating land surface and flooding processes (1-WAY), i.e., Noah-MP neglects surface water availability, and the proposed two-way coupling (2-WAY), where Noah-MP takes surface water availability into account in the vertical water and energy balance. Results show that accounting for two-way interactions between Noah- MP and HyMAP over IND improves simulations of all selected hydrological variables. Compared to 1-WAY, evapotranspiration derived from 2-WAY over flooding zones doubles, increased by 0.8mm/day, resulting in an additional water loss rate of ~18,900km(exp 3)/year, ~40% drop of wetland extent during wet seasons and major improvement in simulated water level variability at multiple locations. Significant soil moisture increase and surface temperature drop were also observed. Wetland outflows decreased by 35%, resulting in a substantial a Nash-Sutcliffe coefficient improvement, from -0.73 to 0.79. It is anticipated that future developments in water monitoring and water-related disaster warning systems will considerably benefit from these findings.

Augusto Getirana↗