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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

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↗
DOE OSTI2026

Improving coastal water level estimation by merging nadir-only satellite altimetry data into a hydrodynamic model

Providing robust real time flood warnings is of paramount importance to coastal communities. Although state-of-the-art hydrodynamic models are capable of robustly predicting Coastal Water Levels (CWL), unresolved drivers affecting level fluctuations are often not represented by the model governing equations. This work evaluates a novel method to improve the performance of the ADvanced CIRCulation (ADCIRC) hydrodynamic model by assimilating observations from four nadir-only satellite altimetry missions against a set of National Oceanic and Atmospheric Administration (NOAA) gauge stations located across the entire U.S. East Coast. Two different types of simulations were performed – Open Loop (OL) and Data Assimilation (DA). Five different simulations were performed where four different satellite altimetry observations were assimilated individually and combined with two different scenarios – with and without considering the data quality flags. Results indicate that, despite their limited spatial coverage, merging nadir-only observations into ADCIRC from the newly launched Surface Water and Ocean Topography (SWOT)’s nadir altimeter can improve the model performance at 76% of the gauge locations, whereas Sentinel-6 improves 73% of the total locations, Jason-3 74%, and SARAL 21%. Furthermore, combining observations from SWOT-nadir, Jason-3, and Sentinel-6 can improve the ADCIRC performance at more than 80% of the gauge locations for 107-day simulation. Nadir-only satellite altimetry observations can be useful for improving the model performance even if flagged as “poor quality” near the coast. When the flagged data are disregarded, SWOT can improve ADCIRC at 78%, Sentinel-6 at 73%, Jason-3 at 53%, and SARAL at 21% of the gauge locations. The ability to improve the model simulations largely depends on the availability of a satellite overpass nearby. Therefore, model performance can be further enhanced if satellite observations are available during a storm surge event, stressing the importance of frequent satellite overpasses.

Aafnan Bhuiyan, Soelem↗
DOE OSTI2024

Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis

Abstract Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from the Alcator C-Mod and DIII-D tokamaks by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real time to minimize risk of damage to the device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Increasing risk of cascading hazards in the central Himalayas

Cascading hazards are becoming more prevalent in the central Himalayas. Primary hazards (e.g., earthquakes, avalanches, and landslides) often trigger secondary hazards (e.g., landslide dam, debris flow, and flooding), compounding the risks to human settlements, infrastructures, and ecosystems. Risk management strategies are commonly tailored to a single hazard, leaving human and natural systems vulnerable to cascading hazards. In this commentary, we characterize diverse natural hazards in the central Himalayas, including their cascading mechanisms and potential impacts. A scientifically sound understanding of the cascading hazards, underlying mechanisms, and appropriate tools to account for the compounding risks are crucial to informing the design of risk management strategies. Here, we also discuss the need for an integrated modeling framework, reliable prediction and early warning system, and sustainable disaster mitigation and adaptation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Yesterday’s extremes, today’s new normal: flood risk in the Kathmandu Valley, Nepal

Unplanned urban growth has left many cities increasingly vulnerable to extreme rainfall events, particularly in regions with inadequate drainage infrastructures and development encroaching on natural floodplains. Here, in this perspective paper, we examine the September 2024 floods that struck Central Nepal, triggered by a persistent low-pressure system and enhanced by converging moisture flows from the Arabian Sea and the Bay of Bengal which led to widespread catastrophic damage. In the Kathmandu Valley, floodwaters expanded to more than 2.5 times the bankfull water extent, causing significant damage to housing, transportation network, and critical infrastructure, displacing thousands of residents, and severely disrupting urban services. This event highlights the urgent need for improved flood management strategies that integrate both structural and non-structural measures into the infrastructure development. While early warning systems provided critical lead time, challenges remain in reducing forecasting uncertainties and improving communication across government agencies and with local communities. A forward-looking approach is essential, including probabilistic flood forecasting systems, sustainable floodplain management, risk-sensitive land use planning, climate- and disaster- resilient infrastructure development, and the integration of nature-based solutions like urban green and blue spaces to mitigate flood impacts. By involving local communities in planning and preparedness efforts, particularly through citizen science initiatives, and engagement with underserved and disadvantaged communities, Nepal can better adapt to the growing risks posed by extreme rainfall and urban flooding and enhance long-term disaster resilience in rapidly urbanizing areas like Kathmandu Valley.

Kathmandu Valley↗
DOE OSTI2025

Impact of Cyber Threat Awareness on Driver Response to an Unexpected Vehicle Cyberattack

Here, the integration of advanced cyber-physical systems in heavy vehicles introduces new vulnerabilities by expanding the possibility of cyberattacks. The objective of this study is to evaluate (1) how threat awareness influences driver response to an unexpected cyberattack, (2) how the provision of a basic cyberattack response protocol influences driver performance, and (3) how professionally trained versus standard drivers compare in their responses to a cyberattack. An on-road driving study (N = 50) was conducted using a medium heavy-duty vehicle. Participants were divided into three groups: Control, which remained unaware of any potential cyberattack; Aware, which was informed about the potential cyberattack; and Aware + Protocol, which received the same warning as the Aware group with the addition of a basic cyberattack response protocol. An instrument cluster cyberattack was executed at the same location for all participants. The findings highlight the essential role of awareness and response protocol in enhancing driver response to an unexpected vehicle cyberattack. The Aware + Protocol group had the highest stop rate (100%) and the shortest stopping distances (224 m for standard drivers and 254 m for professionals), compared to the Control group (828 and 520 m, respectively). Aware + Protocol also had the fastest reaction time, averaging 7.53 s, versus 16.12 s (Aware) and 30.29 s (Control). These results emphasize that awareness alone is insufficient. Providing drivers with clear, actionable protocols significantly improves their ability to react quickly and safely to cyberattacks, enhancing overall road safety.

Cybersecurity↗
DOE OSTI2021

Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment

The availability of large-scale naturalistic driving data provides enormous opportunities for studying relationships between instantaneous driving decisions prior to involvement in safety critical events (SCEs). This study investigates the role of driving instability prior to involvement in SCEs. While past research has studied crash types and their contributing factors, the role of pre-crash behavior in such events has not been explored as extensively. The research demonstrates how measures and analysis of driving volatility can be leading indicators of crashes and contribute to enhancing safety. Highly detailed microscopic data from naturalistic driving are used to provide the analytic framework to rigorously analyze the behavioral dimensions and driving instability that can lead to different types of SCEs such as roadway departures, rear end collisions, and sideswipes. Modeling results reveal a positive association between volatility and involvement in SCEs. Specifically, increases in both lateral and longitudinal volatilities represented by Bollinger bands and vehicular jerk lead to higher likelihoods of involvement in SCEs. Further, driver behavior related factors such as aggressive driving and lane changing also increases the likelihood of involvement in SCEs. Driver distraction, as represented by the duration of secondary tasks, also increases the risk of SCEs. Likewise, traffic flow parameters play a critical role in safety risk. The risk of involvement in SCEs decreases under free flow traffic conditions and increases under unstable traffic flow. Further, the model shows prediction accuracy of 88.1 % and 85.7 % for training and validation data. These results have implications for proactive safety and providing in-vehicle warnings and alerts to prevent the occurrence of such SCEs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Late-time small body disruptions for planetary defense

Here, diverting hazardous small bodies on impact trajectories with the Earth can in some circumstances be impossible without risking disrupting them. Disruption is a much more difficult planetary defense scenario to assess, being linked both to the response of the body to shock loading and the much more complicated gravitational dynamics of the fragments in the solar system relative to pure deflection scenarios. In this work we present a new simulation suite built on N-body gravitational methods that solves fragment orbits in the full gravitational system without recourse to more approximate methods. We assess the accuracy of our simulations and the simplifying assumptions we adopt to make the system tractable, and then discuss in more detail several specific, plausible planetary defense scenarios based on real close encounters. We find that disruption can be a very effective planetary defense strategy even for very late (sub-year) interventions, and should be considered an effective backup strategy should preferred methods, which require long warning times, fail.

42 ENGINEERING↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2021

Transcriptional and translational landscape of Candida auris in response to caspofungin

Candida auris has emerged as a serious worldwide threat by causing opportunistic infections that are frequently resistant to one or more conventional antifungal medications resulting in high mortality rates. Against this backdrop, health warnings around the world have focused efforts on understanding C. auris fungal biology and effective prevention and treatment approaches to combat this fungus. To date, there is little information about the differentially expressed genes when this fungus is treated with conventional antifungals, and caspofungin is a standard echinocandin deployed in the therapy against C. auris. In this work, we treated two distinct strains of C. auris for 24h with caspofungin, and the cellular responses were evaluated at the morphological, translational and transcriptional levels. We first observed that the echinocandin caused morphological alterations, aggregation of yeast cells, and modifications in the cell wall composition of C. auris. Transcriptomic analysis revealed an upregulation of genes related to the synthesis of the cell wall, ribosome, and cell cycle after exposure to caspofungin. Supporting these findings, the integrated proteomic analysis showed that caspofungin-treated cells were enriched in ribosome-related proteins and cell wall, especially mannoproteins. Altogether, these results provide further insights into the biology of C. auris and expands our understanding regarding the antifungal activity of caspofungin and reveal cellular targets, as the mannose metabolism, that can be further explored for the development of novel antifungals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Record-breaking 2020 summer marine heatwaves in the western North Pacific

Record-breaking marine heatwaves (MHWs) occurred in the western North Pacific during the summer of 2020. These unprecedented MHWs were consistent with favorable large-scale conditions that are linked to an anomalous western North Pacific subtropical high (WNPSH), resulting mainly from sea surface temperature (SST) anomalies across the tropical oceans. In addition, a moderate La Niña-like pattern was also conducive to transporting warm seawater to the western North Pacific. Mixed-layer heat budgets suggest that surface heat flux contributed to the SST anomaly in the western subtropical Pacific. In contrast, oceanic heat advection dominanted in the South China Sea and the western equatorial Pacific. Numerical model experiments indicated that the tropical Indian Ocean SST anomalies were responsible for the enhanced WNPSH. The increased zonal SST gradient across the tropical Pacific also played an important role. Inter-ocean interactions can modulate climate variability through ocean-atmospheric coupling and deserve more attention when predicting MHWs within the context of global warming. Additionally, it is critical to consider MHWs as a powerful tool in detecting acute, intense thermal stress events in the coral bleaching pre-warning system.

Coral bleaching↗
DOE OSTI2026

A hybrid CNN-LSTM surrogate model for hyper-resolution spatiotemporal flood forecasting in Norfolk, Virginia

Study region: Norfolk, Virginia, United States Study focus: Accurate and timely flood forecasting is essential for enhancing resilience in coastal urban areas in the context of increasing frequency and intensity of rainfall, sea level rise and urbanization. This study presents a hybrid deep learning-based surrogate model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enable real-time spatiotemporal flood forecasting. The model leverages CNN to capture spatial features from inputs such as elevation and Topographic Wetness Index (TWI), while LSTM processes time-series inputs of rainfall and tide data to capture temporal features. New hydrologic insights for the region: The hybrid CNN-LSTM model was trained using the physics-based hydrodynamic model simulations obtained from the Two-dimensional Unsteady FLOW (TUFLOW) model for Norfolk, Virginia, and achieved high predictive accuracy across diverse flood-prone areas. The reduced computational time from four to six hours using TUFLOW to 3.2 min per event using CNN-LSTM enables rapid flood inundation mapping and early warning applications. The model effectively captured both spatial flood extents and their temporal evolution across different flooding scenarios, providing forecasts at a 2.5-m spatial resolution and 15-min temporal resolution and a one-hour-ahead prediction horizon. While challenges remain in terms of transferability to new regions and real-time data assimilation, this approach demonstrates strong potential for supporting operational flood risk management in coastal urban environments.

Coastal urban flooding↗
DOE OSTI2020

Certification and prediction of post-disturbance states in dynamic security assessment

Dynamic security assessment usually involves the simulation of system dynamics under large disturbances. The instant large disturbance occurs, the post-disturbance state of the system could deviate significantly from the pre-disturbance one. It is desirable and necessary to develop an efficient method to certify the existence of state and predict the approximate post-disturbance state because: (1) faster warning can be made in online assessment; and (2) the dynamic simulator can be given better initial values to calculate precise solutions, which prevents convergence failure brought on by poor initial values. We propose a novel approach for efficiently certifying and predicting the post-disturbance states in dynamic security assessment. Based on the fixed-point power flow mapping approach applied to salient pole generator models, the criterion for determining the existence and uniqueness of the post-disturbance solution is derived, and the bounds of post-disturbance solution can be rigorously characterized.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Temperature-responsive smart tracers for field-measurement of inter-well thermal evolution: Heterogeneous kinetics and field demonstration

Temperature-responsive smart tracers enable advanced warning of a “premature thermal breakthrough” and may therefore improve reservoir management and reduce financial risk. A successful calculation of inter-well fluid temperatures requires that temperature-dependent kinetics are known, which typically result from homogeneous batch reactor experiments. However, recent meso-scale field experiments at the Altona Field Laboratory involving fluid flow in a discretely-fractured reservoir suggest that silica-fluid interfaces may accelerate hydrolysis kinetics. Here, the Arrhenius parameters of phenyl acetate hydrolysis are investigated under heterogeneous reaction conditions in packed-bed column experiments. The breakthrough curve of the reaction product is compared to an inert reference tracer (carbon-cored nanoparticles). Temperatures experienced during field testing ranging from 10 to 40 °C were studied using a phosphate buffer solution to maintain a near-neutral pH, as found in the reservoir. The empirically-determined pre-exponential factor and activation energy values are subsequently used in a first-order kinetic model to improve calculations of effective reservoir temperatures for these meso-scale field tests. Here, the results suggest that hydrolysis reactions can directly monitor inter-well reservoir temperatures, but only if the influence of solid-fluid interactions are carefully accounted for.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Application of emerging monitoring techniques at the Illinois Basin – Decatur Project

The Illinois Basin – Decatur Project is a large-scale carbon capture and storage demonstration project located in Decatur, Illinois, USA. In this project, one million metric tons of carbon dioxide (CO 2 ) was captured from an ethanol production facility and successfully injected into a deep saline reservoir over a period of three years. The scale of this project presented an opportunity to explore emerging technologies for effective long-term monitoring of the carbon capture and storage site. This research documents the application of three emerging monitoring techniques: (1) a prototype “open-path” sensor, a method of continuously monitoring atmospheric CO 2 by applying tunable diode laser absorption spectroscopy to provide a warning system for personnel safety along pipelines and at wellheads; (2) the Greenhouse Gas Laser Imaging Tomography Experiment (GreenLITE), an automated system for measuring two-dimensional spatial distribution of atmospheric CO 2 concentrations; and (3) periodic aerial imagery, a method of documenting surface-vegetation dynamics to detect vegetative responses to CO 2 leaks. The objective of this work was to assess the advantages and limitations of these monitoring techniques and quantify their reliability over an extended deployment at an active industrial site. These results will aid in determining viability of long-term monitoring on a commercial scale.

42 ENGINEERING↗
DOE OSTI2020

Development of risk mitigation guidance for sensor placement inside mechanically ventilated enclosures – Phase 1

Guidance on Sensor Placement was identified as the top research priority for hydrogen sensors at the 2018 HySafe Research Priority Workshop on hydrogen safety in the category Mitigation, Sensors, Hazard Prevention, and Risk Reduction. This paper discusses the initial steps (Phase 1) to develop such guidance for mechanically ventilated enclosures. This work was initiated as an international collaborative effort to respond to emerging market needs related to the design and deployment equipment for hydrogen infrastructure that is often installed in individual equipment cabinets or ventilated enclosures. The ultimate objective of this effort is to develop guidance for an optimal sensor placement such that, when integrated into a facility design and operation, will allow earlier detection at lower levels of incipient leaks, leading to significant hazard reduction. Reliable and consistent early warning of hydrogen leaks will allow for the risk mitigation by reducing or even eliminating the probability of escalation of small leaks into large and uncontrolled events. To address this issue, a study of a real-world mechanically ventilated enclosure containing GH2 equipment was conducted, where CFD modeling of the hydrogen dispersion (performed by AVT and UQTR, and independently by the JRC) was validated by the NREL Sensor laboratory using a Hydrogen Wide Area Monitor (HyWAM) consisting of a 10-point gas and temperature measurement analyzer. In the release test, helium was used as a hydrogen surrogate. Expansion of indoor releases to other larger facilities (including parking structures, vehicle maintenance facilities and potentially tunnels) and incorporation into QRA tools, such as HyRAM is planned for Phase 2. It is anticipated that results of this work will be used to inform national and international standards such as NFPA 2 Hydrogen Technologies Code, Canadian Hydrogen Installation Code (CHIC) and relevant ISO/TC 197 and CEN documents.

08 HYDROGEN↗
DOE OSTI2025

Development and testing of a continuous maritime monitor for radionuclide aerosols

Monitoring airborne concentrations of radionuclide activity may provide a timely warning to sea-based assets to avoid contamination from a radioactive plume. The development and testing of an automated aerosol monitoring system that can capture and detect radioactive particulate from marine air is presented. A custom electrostatic precipitator (ESP) was designed to capture particulate onto a reusable collection media. The collection efficiency of the ESP system for radon progeny was determined to be ~23%. A conservative calculation of the minimum detectable concentration of 214 Bi was estimated as 0.3-8 Bq/m 3 . The system was demonstrated in continuous operation, without consumables and limited maintenance, in a marine environment at the PNNL campus in Sequim, Washington. In conclusion, a successful 2-month deployment indicates the feasibility of the approach for continuous maritime monitoring for radionuclide aerosols.

Moore, Michael E. [Pacific Northwest National Labo↗