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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 NTRS2018

NASA’s Mid-Atlantic Communities and Areas at Intensive Risk Demonstration: Translating Compounding Hazards to Societal Risk

Remote sensing provides a unique perspective on our dynamic planet, tracking changes and revealing the course of complex interactions. Long term monitoring and targeted observation combine with modeling and mapping to provide increased awareness of hydro-meteorological and geological hazards. Disasters often follow hazards and the goal of NASA’s Disasters Program is to look at the earth as a highly coupled system to reduce risk and enable resilience. Remote sensing and geospatial science are used as tools to help answer critical questions that inform decisions. Data is not the same as information, nor does understanding of processes necessarily translate into decision support for disaster preparedness, response and recovery. Accordingly, NASA is engaging the scientific and decision-support communities to apply remote sensing, modeling, and related applications in Communities and Areas at Intensive Risk (CAIR). In 2017, NASA’s Applied Sciences Disasters Program hosted a regional workshop to explore these issues with particular focus on coastal Virginia and North Carolina. The workshop brought together partners in academia, emergency management, and scientists from NASA and partnering federal agencies to explore capabilities among the team that could improve understanding of the physical processes related to these hazards, their potential impact to changing communities, and to identify methodologies for supporting emergency response and risk mitigation. The resulting initiative, the mid-Atlantic CAIR project, demonstrates the ability to integrate satellite derived earth observations and physical models into actionable, trusted knowledge. Severe storms and associated storm surge, sea level rise, and land subsidence coupled with increasing populations and densely populated, aging critical infrastructure often leave coastal regions and their communities extremely vulnerable. The integration of observations and models allow for a comprehensive understanding of the compounding risk experienced in coastal regions and enables individuals in all positions make risk-informed decisions. This initiative uses a representative storm surge case as a baseline to produce flood inundation maps. These maps predict building level impacts at current day and for sea level rise (SLR) and subsidence scenarios of the future in order to inform critical decisions at both the tactical and strategic levels. To accomplish this analysis, the mid-Atlantic CAIR project brings together Federal research activities with academia to examine coastal hazards in multiple ways: 1) reanalysis of impacts from 2011 Hurricane Irene, using numerical weather modeling in combination with coastal surge and hydrodynamic, urban inundation modeling to evaluate combined impact scenarios considering SLR and subsidence, 2) remote sensing of flood extent from available optical imagery, 3) adding value to remotely sensed flood maps through depth predictions, and 4) examining coastal subsidence as measured through time-series analysis of synthetic aperture radar observations. Efforts and results are published via ArcGIS story maps to communicate neighborhoods and infrastructure most vulnerable to changing conditions. Story map features enable time-aware flood mapping using hydrodynamic models, photographic comparison of flooding following Hurricane Irene, as well as visualization of heightened risk in the future due to SLR and land subsidence.

Rogers, Laura↗
DOE OSTI2025

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Automated nuclear cloud feature extraction from film

Chemical, biological, radiological, nuclear, and explosives incidents require rapid detection and characterization for appropriate response. For a nuclear detonation, visible-light cameras may be used to locate the cloud and characterize fallout deposition when coupled with numerical models. Films from the United States’ nuclear testing era compose the only sizeable collection of imagery depicting high-yield detonations. These films offer unique insights into characteristics of flows involving scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models for nuclear fallout transport, either as part of emergency response or forensic activities. In this work, we implement modern computer vision and machine learning techniques to identify and track the cloud automatically and subsequently determine the time dependence of some of its features. We trained a ResNet-18 image classifier on hundreds of images to categorize nuclear cloud morphology. Each category or cloud regime is determined by early cloud evolution and is associated to constitutive properties of the flow, such as distribution of vorticity. Next, we identified keypoint features using the KAZE algorithm and tracked these keypoints in the images, allowing us to determine the dimensions and velocities of the cloud across film frames. These measurements converted to real-world units provide valuable experimental data that can be used in the development and validation of nuclear cloud models. We compared the results of this method against manual cloud rise measurements from two different films. In one, our automated method accelerated the feature extraction process without sacrificing measurement accuracy.

Khristy, Joel [ORNL] (ORCID:0000000209963060)↗
NASA NTRSDate not supplied

Ongoing Work: A Prototype Dataset for Low-flying Autonomous Medical UAS Operations

This paper presents ongoing work to create a dataset for low-flying autonomous medical UAS operations, focused on human stance recognition. This is an exploration of the viability of airborne classification for the Drone as a First Responder (DFR) concept in which a UAS arrives at the scene of an incident before emergency response personnel can get there and provides some level of situational awareness for the personnel arriving to the scene. Future incarnations could also see the UAS administer some level of care to injured parties at the scene. The data set, focused on detecting human stance, being developed here is the result of 30 test flights at NASA Langley Research Center in early 2024. In addition to flights where the participant (an anthropomorphic testing device or human) is alone in the viewing area holding a particular stance, two emergency scenes have been fabricated and collected through video - ``bike crash'' and ``difficult camping''. These test flights include four human participants. The contribution of this work upon completion will be a publicly available data set for the development of classification engines focused on human stance, and in the future, even triage.

Uncrewed Aerial Systems↗
DOE OSTI2025

Using satellite data to assess Hurricane Helene’s impact on vegetation by land use in the CSRA

Many regions within Georgia, South Carolina, and North Carolina experienced record-breaking rainfall and catastrophic winds due to Hurricane Helene. Helene made landfall on Florida’s big bend on September 26 th , 2024, as a category 4 hurricane, and tracked northward through Georgia and the southern Appalachian Mountains before dissipating on September 29 th , 2024. One significant impact of the hurricane was severe damage to tree canopies across the southeastern United States. This study utilizes satellite-based remotely sensed data provided by the National Aeronautical and Space Administration (NASA) to examine the resilience of these tree canopies following the hurricane. Specifically, the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Cover Type, Soil Moisture Active Passive (SMAP), and the Global Precipitation Measurement (GPM) mission datasets were employed to study the tree canopy’s response to such events in the Central Savannah River Area (CSRA). A significant increase was observed in the LAI in November of 2024. This increase in LAI was likely influenced by warmer-than-average temperatures throughout October of 2024, along with a second record-breaking rainfall event in the CSRA on November 6th, 2024. The LAI increase was then broken down into land cover type to understand which areas contributed most. The complex nature and resilience of trees became evident, offering opportunities for further exploration and application in urban planning, emergency response, and environmental management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data Interfaces for Automated Vehicle Services - A Municipality Perspective

As Automated Vehicle (AV) services proliferate, data sharing between AV operators and municipal agents is assuming greater importance. Information on the dynamic nature of the road system such as incidents to avoid, weather hazards (such as flooding), construction and detours, as well as active safety concerns (e.g. - riots) is important for AV operators. Such information cannot be directly sensed from a vehicle's sensor array, but instead must be communicated in a timely and trustworthy channel. Municipalities are interested in pushing this information to AV operators to support emergency response efforts, reduce traffic in construction zones, and generally improve operation of the system. Similarly, information on vehicle safety such as disengagements, as well as critical information on the use of roadway system (trips, origin and destination patterns) are important performance factors for municipalities to understand utilization and plan for appropriate infrastructure. As mobility shifts to on-demand options, the need for safe and coordinated pick-up and drop-off zones will increase (potentially reducing parking needs). For all of these reasons, communication flows between AV operators and municipalities are becoming increasingly important. This paper investigates the functions, emerging practices and protocols for sharing of such critical data, and identifies gaps in and challenges in existing practices. Additionally, case studies are used to highlight the impacts of data sharing between AV operators and municipalities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2019

Development of a Point of Care Technology for Military Medical Applications in Austere Environments

NASA, Lawrence Livermore National Laboratory (LLNL) and the Department of Defense (DoD) share a common interest in compact medical diagnostic technologies—also known as “point of care testing” (POCT) technologies. For NASA, the need is to take care of astronauts on deep space missions. For LLNL, the focus is on technologies needed to support homeland security applications. For DoD, the need is to support warfighters in military theaters of operation, especially for prolonged field care and en route care. A recently patented technology, “Portable Medical Diagnosis Instrument” (U.S. Patent 9824870), co-invented by NASA and LLNL, is being developed further in a partnership between NASA, LLNL and Travis Air Force Base, with a focus on military applications. This instrument, the NASA-LLNL Medical Tricorder, can be used for a wide range of clinical samples including breath, saliva and blood. One component of the Tricorder is the NASA E-Nose, a carbon nanotube-based chemical sensor, integrated into a hand-held smart phone, that can detect and measure “breath biomarkers” in the gas phase of exhaled breath. The response of the NASA E-Nose is rapid, which makes it possible to detect various gas-phase substances in real time. The NASA E-Nose, with modification, can also be used for breath condensate analysis. Another component of the Tricorder is a miniaturized microfluidic analyzer, under development by LLNL, that does not require any pumps. The microfluidic analyzer can detect and measure biomarkers in liquid samples, such as blood, saliva and breath condensate. This microfluidic system requires small quantities of liquid sample and can perform analysis very rapidly (seconds to minutes), which makes it possible to detect relevant biomarkers in near real time in the field. The Clinical Investigation Facility (CIF) at Travis Air Force Base is currently conducting research with the goal to develop improved field-able technologies and procedures for the emergency response to life threatening injuries soldiers may receive in theater. This work includes experimental large animal systems and comprehensive collection of clinical specimens and physiological data. NASA and LLNL are partnering with Travis to test and further develop the Tricorder technology for high priority military applications, initially focused on lung injury models and traumatic brain injury.

Loftus, David J.↗
NASA NTRS2010

A Simulation-Based Approach to Training Operational Cultural Competence

Cultural knowledge and skills are critically important for military operations, emergency response, or any job that involves interaction with a culturally diverse population. However, it is not obvious what cultural knowledge and skills need to be trained, and how to integrate that training with the other training that trainees must undergo. Cultural training needs to be broad enough to encompass both regional (culture-specific) and cross-cultural (culture-general) competencies, yet be focused enough to result in targeted improvements in on-the-job performance. This paper describes a comprehensive instructional development methodology and training technology framework that focuses cultural training on operational needs. It supports knowledge acquisition, skill acquisition, and skill transfer. It supports both training and assessment, and integrates with other aspects of operational skills training. Two training systems will be used to illustrate this approach: the Virtual Cultural Awareness Trainer (VCAT) and the Tactical Dari language and culture training system. The paper also discusses new and emerging capabilities that are integrating cultural competence training more strongly with other aspects of training and mission rehearsal.

Johnson, W. Lewis↗
DOE OSTI2025

A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation

Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modeling building height at scale are hindered by two pervasive issues. First, there is no consistent approach to quantify what a high-rise building is at a macro scale, leaving researchers unable to accurately compare results across geographies and domains. Second, high-rise buildings represent a small fraction of the built environment, implying data imbalance challenges that negatively affect current approaches. This is a problem of practical relevance since information on high-rise buildings is important for studies on urban heat islands, population dynamics, and pollution dispersion. Here, we introduce a novel approach to map building height which first identifies two distinct distributions within the built environment, with one being composed of low-rise buildings and one composed of high-rise buildings. We then develop an ensemble scheme where discrete specialist models are trained for each subset of low-rise buildings and high-rise buildings to infer building height from morphology features. For experiments mapping heights of 4.85 million buildings in Japan, we show an increase of 34 % in accuracy within 3m error when compared to the current state-of-the-art when modeling high-rise buildings, which based on KNN experimentation we define as any building > 12m . Our findings show that such an ensemble framework outperforms the current state-of-the-art approaches, which is especially relevant in relation to inferring height for high-rise buildings, a prominent issue of existing approaches for mapping the built environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

High-performance windows improve thermal survivability of occupants during cold snaps

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Krelling, Amanda F↗
DOE OSTI2024

Evaluation of neutron dosimetry capabilities with the MC-15 portable multiplicity counter

This work proposes a preliminary neutron dose rate estimation method for a neutron multiplicity detector through measurement- and simulation-based analyses. Uncharacterized neutron-emitting sources may be encountered in situations such as nuclear emergency response, safeguards, and treaty verification. These circumstances may present irradiation risk to personnel conducting field assay, search, and characterization measurements. It is therefore of interest to provide a field neutron dosimetry capability with the existing neutron multiplicity counting (NMC) capabilities. To date, no commercially-available neutron detection systems are capable of both accurate NMC and real-time neutron dosimetry. This work will focus on estimating dose rate using input from a single fielded NMC called the MC-15. The energy-dependent neutron detection efficiency response of the MC-15 was quantified in monoenergetic neutron simulations and evaluated in response to two neutron-emitting sources and to a polyethylene-moderated source. The results were compared to existing neutron dosimeters and established the proof of concept for further investigation of the MC-15 for dose estimation. Measurement results were also replicated in simulations; additional simulations were then conducted to expand upon the limited empirical data. The initial empirical results provided a conversion factor appropriate for use when measuring 252 Cf neutrons that is independent of polyethylene shielding presence and thickness. The simulated data sets were then used to evaluate a fit equation allowing estimation of the neutron dose rate for less restricted geometric configurations and dependent only on the distance between the source and the detector. Additionally, the energy dependence of the efficiency response indicates that further empirical evaluations could provide energy-dependent conversion factors for broader neutron dosimetry capabilities with a wider range of neutron-emitting sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Mapping heat vulnerability in cities: A tale of two california cities

Extreme heat is a major cause of weather-related deaths in the United States. To address this, a heat vulnerability index (HVI) is crucial for assessing heat risk and identifying vulnerable urban areas and populations, supporting city planning and emergency response. Current HVI studies often use Principal Component Analysis (PCA) on environmental, socioeconomic, and medical data to aggregate vulnerability indicators into a single index. However, these fixed aggregation weights struggle to adapt to different use cases, which may require varying focuses. Moreover, existing tools primarily consider outdoor heat exposure, providing an incomplete picture of actual exposure, as people spend most of their time indoors. Our research introduces an HVI web mapping tool that addresses these gaps in the literature by: (1) allowing flexible weights to adapt to different use cases, and (2) uniquely integrating both outdoor and indoor heat exposure by considering building characteristics for a more comprehensive risk assessment. We demonstrated this tool in two California cities with contrasting climates: Fresno (inland, arid, hot summers) and Oakland (temperate coastal). This HVI mapping tool provides essential decision support for policymakers and stakeholders in both short-term heat mitigation and long-term urban planning for building interventions and infrastructure development.

BES↗
DOE OSTI2024

Storm surges and extreme sea levels: Review, establishment of model intercomparison and coordination of surge climate projection efforts (SurgeMIP).

Coastal flood damage is primarily the result of extreme sea levels. Climate change is expected to drive an increase in these extremes. While proper estimation of changes in storm surges is essential to estimate changes in extreme sea levels, there remains low confidence in future trends of surge contribution to extreme sea levels. Alerting local populations of imminent extreme sea levels is also critical to protecting coastal populations. Both predicting and projecting extreme sea levels require reliable numerical prediction systems. The SurgeMIP (surge model intercomparison) community has been established to tackle such challenges. Efforts to intercompare storm surge prediction systems and coordinate the community 's prediction and projection efforts are introduced. An overview of past and recent advances in storm surge science such as physical processes to consider and the recent development of global forecasting systems are briefly introduced. Selected historical events and drivers behind fast increasing service and knowledge requirements for emergency response to adaptation considerations are also discussed. The community 's initial plans and recent progress are introduced. These include the establishment of an intercomparison project, the identification of research and development gaps, and the introduction of efforts to coordinate projections that span multiple climate scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Shear wave propagation in a liquid crystal: An inelastic X-ray scattering study

For this work, we investigated the spectrum of density fluctuations of a liquid crystal, CB7CB, in two different orientations by using high-resolution inelastic x-ray scattering. Our analysis, based on Bayesian principles, revealed that high-frequency collective excitations propagate through this mesoscale-ordered sample in a peculiar manner that lies somewhere between those observed in liquids and crystalline systems. Interestingly, when we probed longer length scales, a more pronounced solid-like response emerged. This was mainly characterized by anomalously sharp inelastic excitations and the onset of shear mode propagation. Comparison with previous x-ray diffraction results suggests a correlation between the observed behavior and the mesogen arrangement.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Use of High-Density UN Fuel in Heat-Pipe Microreactors

Heat-pipe microreactors (HPMRs) are very small-scale nuclear reactors that employ heat pipes (HPs) for heat removal. HPMRs can be easily integrated with other forms of renewable energies, can be used for emergency responses to disaster relief zones, can be deployed in remote locations not connected to the grid, and can be removed from sites and replaced by new ones. HPMRs can also be used for space missions as HPs do not rely on gravity for heat transfer. Conventional fuel materials, such as uranium oxide (UO 2 ) and uranium oxycarbide (UCO), are currently considered in most existing HPMR designs, but ceramic uranium nitride (UN) fuel that has high uranium density, high thermal conductivity, and high melting point may become a better fuel candidate. Through neutronics calculations, this paper assesses the impact of using UN fuel in HPMRs with two different neutron spectra (fast and thermal) and two different fuel forms [traditional solid fuel pellets and TRi-structural-ISOtropic (TRISO) fuel compacts]. It was concluded that retrofitting HPMRs with UN fuel has the potential to reduce the initial 235 U enrichment requirement by ~3 wt% (to keep the same cycle length) or increase the cycle length (by keeping the same initial 235 U enrichment), which enables more compact and transportable HPMR core designs. However, using UN fuel decreases the control element worth [by up to 20% for the Special Purpose Reactor (SPR) and 5% for HP-MR] and is up to 80% more costly. Increasing 15 N enrichment can further decrease the initial 235 U enrichment requirement and increase the control element worth but is more costly. In conclusion, compared to fast-spectrum HPMRs fueled with solid pellet fuels, retrofitting UN fuel is more suitable for thermal-spectrum HPMRs fueled with TRISO fuel compacts, where the neutron spectrum hardening caused by using UN is less significant.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Emergency Radiation Dose Rate Monitoring During Prolonged Armed Conflict

The 2022 Russian full-scale invasion of Ukraine has introduced unprecedented challenges for the nuclear power generation and radiological safety communities, including occupation and disturbance of highly-contaminated areas, occupation of a nuclear power plant, and strikes near and within boundaries of nuclear sites. The war has necessitated the implementation of a supplementary dose rate sensor network to provide resilient measurement data for public protection and leadership awareness. This paper discusses the implementation of such a system, the factors determining what equipment is best suited for the purpose, and practical factors regarding deployment of the system and data management. The crucial factors for operating a supplementary dose rate sensing network are backup power and communications options for dose rate sensors to make the network resilient to the effects of military operations. The most important implementation factor is to plan for extended operations beyond those typically considered for emergency response given the unpredictable nature of warfare.

resilience↗
DOE OSTI2025

Spatiotemporal predictions of toxic urban plumes using deep learning

Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets. In emergency response situations, alternative methods are needed that can run quickly and adequately capture important spatiotemporal features. Here, we present a novel deep learning model called ST-GasNet inspired by the mathematical equations that govern the behavior of plumes as they disperse through the atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set of temporal sequences of ground-level toxic urban plumes generated by a high-resolution large eddy simulation model. On independent sequences, ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the early-time behavior as an input, even when a building splits a large plume into smaller plumes. By incorporating large-scale wind boundary condition information, ST-GasNet achieves a prediction accuracy of at least 90% on test data for the entire prediction period.

Civil and Environmental Engineering↗
DOE OSTI2025

A Novel Machine Learning Workflow to Classify Mobile Home Parks at Scale

Understanding the built environment is essential to the overall study of population dynamics, grid infrastructure, emergency response, among others. In the United States there are multiple classifications for buildings within the built environment such as residential, signifying family homes while commercial buildings consist of apartments or larger structures which are multi-purpose. While there is a high level of understanding of where these aforementioned structures are located, there is a third class of structures, mobile home parks (MHP) which have been under-represented in the literature despite there being an estimated 2.7 million of them within the United States. Research has shown that individuals who reside in MHP are at higher risk to extreme events due to their location and structural integrity of residence. Attention must now turn to identifying MHP at scale to help first responders and policy makers understand where these at risk populations reside. To address for this gap, we develop a novel methodology to infer MHP at scale based off morphologies derived at a building level. Here, we show that across 3 million buildings in 6 states within the United States it is possible to identify MHP with 83% accuracy. This novel approach to identify MHP from other structures within the built environment using a machine learning approach provides a new tool to leverage in relation to helping at-risk populations.

Stipek, Clinton [Oak Ridge National Laboratory (OR↗