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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Responder: Notification Acknowledgement Android Phone Application

Responder is designed to provide easier viewing and acknowledging of multiple messages. It gives the user a quick look at all recently received messages in an organized and clean manner that prevents notifications from being lost during a large burst of messages. It makes it easier to find and review old messages received and Responder was designed to give the user more flexibility in setting up how and what alerts will notify them when they are received by the phone. Since the implementation of Responder the FOT (Flight Operations Team) members are more informed and are able to focus more on problem solving the reason a notification was received then trying to keep up with sending cumbersome acknowledgements over text message or email. Responder is only able to receive and send SMS (Short Message Service) text messages but by using email-to-text services provided by the cellular carrier it is able to send and receive emails.

application↗
NASA NTRS2014

Investigating the Impacts of a Separation Standard for UAS Operations in Enroute and Transition Airspace

Unmanned aircraft systems will be required to equip with a detect and avoid system in order to satisfy the federal aviation regulations to remain well clear of other aircraft. To comply with regulations in today’s operations manned aircraft must “see and avoid” other aircraft and use subjective judgment to determine whether those aircraft are well clear. For a detect-and- avoid (DAA) system to satisfy the requirement to stay well clear, a quantitative definition of well clear needs to be defined and evaluated. Definitions for the boundary of well clear have been proposed by the Unmanned Aircraft System (UAS) Executive Committee Science and Research Panel (SaRP) and the Radio Technical Commission for Aeronautics (RTCA) Special Committee 228 on Detect and Avoid Systems. This study investigates the interoperability implications of UAS using proposed well clear definitions as a separation standard for conducting operations in the national airspace system. The first analysis in the study focuses on the effect of variations in well clear definition parameters on the rate of losses of well clear per flight hour. The second analysis considers three well clear definitions and presents the relative state conditions of intruder aircraft as they encroach upon the well clear boundary. The third analysis focuses on the definition of the alerting criteria needed to inform the UAS operator of a potential loss of well clear. All three analyses are conducted in a NAS-wide fast-time simulation environment using UAS aircraft models, proposed UAS missions, and historical air defense radar data to populate the background traffic operating under visual flight rules. The results from the three analyses presented in this study inform the safety case, requirements development, and the operational environment for the DAA minimum operational performance standards.

UAS Seperation Standard↗
NASA NTRS2021

Is the Last Inflight Rest Break Really the Best?

As pilots experienced in augmented long-haul flights know, current FAA regulations limit options for scheduling in-flight rest periods, such that on many long-haul operations the landing pilot needs to use the final bunk period, closest to top of descent (TOD). However, the logistics of a particular flight (e.g., timing of the flight, timing of passenger service) and the pilot’s own physiology may mean that the last rest break isn’t always the best. To study this, we conducted a large survey of pilots flying these operations. Pilots completed the survey after eligible flights and provided us with information on which rest break they used, how much sleep they received, factors that disrupted sleep and ratings on the quality of their sleep, and they also provided fatigue and alertness ratings at top-of-descent. Pilots from another US-based carrier were also surveyed, in collaboration with our research partners at Washington State University, to increase the sample size and gather insights between operations.

in-flight rest↗
DOE OSTI2025

Developing Digital Twin Visualizations: A Methodology and Case Study on Chemical Separation Processing

As advances in digital engineering continue to push the technological boundaries, digital twin (DT) visualizations for diagnostics and safeguards advancement become much more feasible and practical. DTs generate large and complex data streams that require effective user interfaces to provide monitoring and diagnostic capabilities. Unfortunately, while these frameworks exist, there is not much research on the systematic documentation of human–computer interaction (HCI) for DT visualization. This work presents a dual-mode visualization methodology (two dimensional [2D] graphical user interface dashboard and 3D mixed reality) designed to support diagnostic tasks in DT systems and building on a validated framework and applying established HCI principles. The methodology is demonstrated through a case study of aqueous processing at Idaho National Laboratory, using experimental data from the chemical solvent extraction runs. Our interfaces display real-time alerts and monitoring to inform users of safeguards anomalies. The interfaces use immersive 3D mixed-reality visualization for further system and experiment investigation. This work demonstrates how the systematic application of HCI principles can inform DT visualization design for diagnostic and safeguards applications. While formal user evaluation studies remain as future work, this paper documents the systematic design methodology and demonstrates a proof-of-concept implementation.

3D visualization↗
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 OSTI2025

Development of copper thiolate organometallic compound as thermal sensitive coating for energy storage system safety

Safety and reliability are primary concerns for the deployment of lithium-ion batteries, especially in electric vehicles (EV) and larger-scale energy storage systems (ESS). Current technology in battery management systems (BMS) includes cell voltage monitoring and positioning temperature sensors in selected locations. For a system with hundreds to thousands of individual batteries, single-point temperature monitoring is inadequate to detect hot spots and cell overheating, which could lead to thermal runaway. Here, we have developed a temperature-sensitive copper-thiol compound that can be directly coated onto battery pouch foils to enable early detection of thermal runaway. Upon reaching specific temperatures, this compound releases a sulfur-containing detectable gas, which can be identified using chemically specific gas sensors to trigger an early warning signal. Such a signal propagate through air offers broad signal coverage and enables a more comprehensive approach to large-area temperature monitoring. The Cu-ethanethiol coating is designed to release volatile gases when the substrate surface temperature exceeds 70 °C, with continuous outgassing as the temperature increases. The compound is composed of Cu, S, Cl, hydrocarbons and trace amounts of oxygen. Upon heating, the oxidation state of Cu(I) transitions to Cu (II), accompanied by gas release. Thermogravimetric analysis coupled with mass spectrometry correlated well with the onset of gas release temperature and emission of sulfur-containing volatile gases. Additionally, an acrylic overcoat is applied to enhance the adhesion of the thermally sensitive compound film to the battery pouch foil. This coating is expected to offer an additional safety layer for ESS, alerting possible thermal runaway events before a failure occurs, thereby allowing sufficient time to implement a mitigation plan.

Early warning systems↗
DOE OSTI2026

Synthesis and application of thermally responsive nanofiber coatings for overtemperature monitoring

This study presents a one-pot synthesis route to organometallic nanofibers based on copper thiolate, exhibiting distinctive chemical and physical characteristics. Electron microscopy analysis of morphology and composition revealed 2-10 μm-long, 50-90 nm-diameter hollow and non-hollow fibers composed of copper, sulfur, oxygen, hydrocarbon, and chlorine. Thermogravimetric analysis showed a pronounced mass loss within 120°C-135°C. To elucidate the thermal responsive pathways, the nanofibers were characterized before and after heating. X-ray photoelectron spectroscopy indicates that an initially mixed Cu(I)/Cu(II) oxidation states transition to predominantly Cu(I) upon heating. A layer of nanofiber was coated on battery pouch foil and evaluated as a candidate thermally sensitive coating. At elevated temperature (100-130°C), nanofiber coating released volatile organic compounds, sulfide and sulfur dioxide as detected using multiple gas sensors. This thermally responsive gas release/sensing approach provides a potential large-area temperature monitoring strategy, which is particularly relevant where direct temperature measurements of individual batteries is impractical. The results established proof of concept for nanofiber-coated battery pouch foil as overtemperature warning platform that can provide alerts when surface temperatures exceed a critical threshold. More broadly, the ability to form interconnected fiber networks positions copper thiolate nanofiber coatings as promising materials for advanced applications.

Ihala Gamaralalage, Chanaka [ORNL] (ORCID:00000002↗
DOE OSTI2025

Model-driven prediction for accelerator magnet diagnostics to improve operation reliability

Reliability is one of the most critical metrics for accelerator operation, especially in user facilities. To reduce costly facility downtime and provide an operational environment where system performance can be reliably predicted in support of scientific studies, we are developing a model-driven approach for prediction and anomaly detection. Here, in this study, we present the application of a model-driven method that employs a linear regression model to predict the future temperature, in real time, of accelerator magnets at the NSLS-II light source. This approach enables proactive identification of magnet-heating issues, facilitating magnet flushing prior to the occurrence of permanent damage without interrupting machine operation. The implementation of this method in the NSLS-II control room is described and the analysis of the online results is presented. The results demonstrate the model’s effectiveness in providing early alerts to engineers and improving the reliability of accelerator operations.

36 MATERIALS SCIENCE↗
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 OSTI2025

Complexity in the Photofunctionalization of Single-Wall Carbon Nanotubes with Hypochlorite

The reaction of aqueous suspensions of single-wall carbon nanotubes (SWCNTs) with UV-excited sodium hypochlorite has previously been reported to be an efficient route for doping nanotubes with oxygen atoms. Here, we have investigated how this reaction system is affected by pH level, dissolved O 2 content, and radical scavengers and traps. Products were characterized with near-IR fluorescence, Raman, and XPS spectroscopy. The reaction is greatly accelerated by removal of dissolved O 2 and strongly suppressed by TEMPO, a radical trap. Alcohols added as radical scavengers alter the reaction efficiency and the product peak emission wavelengths. Photofunctionalization with 300 nm irradiation is substantially less efficient at pH levels low enough to protonate the OCl – ion to HOCl. We deduce that in mildly treated high pH samples, the main product is sp 2 hybridized O-doped adducts formed by reaction of SWCNTs with atomic oxygen in its 3 P (ground) level. By contrast, treatment under low pH conditions leads to sp 3 hybridized SWCNT adducts formed by the addition of secondary radicals from reactions of • OH and • Cl. There is also evidence for additional photoreactions of product species under stronger irradiation. Researchers using photoexcited hypochlorite for SWCNT functionalization should be alert to the range of products and the sensitivity to reaction conditions in this system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Developing Scenario‐Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

Abadi, Azar M. [University of Alabama, Birmingham,↗
DOE OSTI2026

The Value of Forecasters‐in‐the‐Loop in Real‐Time Flood Forecasting in the Age of Machine Learning

Machine learning (ML) applications in hydrological forecasting are increasingly prevalent and show great potential. However, many previous studies have only evaluated performance through reanalysis or retrospective simulations compared to simplified baselines. This study provides the first assessment of ML performance against actual operational forecasting systems operated by the California Nevada River Forecast Center (CNRFC), which combines the Community Hydrologic Prediction System (CHPS) with forecasters-in-the-loop. Results demonstrate that forecasters-in-the-loop systems consistently outperform ML models in both general forecasts and flood alerting across lead times up to 96 hr, even when ML models use observed forcings, while CNRFC operational process relies on biased weather forecasts. Our analysis reveals that forecaster expertise maintains forecast reliability despite inaccurate precipitation inputs, with human-guided systems showing superior performance degradation characteristics at extended lead times. These findings highlight the irreplaceable value of human expertise in operational forecasting and caution against overstating current ML capabilities in real-world applications.

Tran, Vinh Ngoc [Univ. of Michigan, Ann Arbor, MI ↗
DOE OSTI2025

Low responsiveness of machine learning models to critical or deteriorating health conditions

Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess a model’s ability to recognize critical patient conditions. We develop multiple medical ML testing approaches, including a gradient ascent method and neural activation map. We systematically assess these machine learning models’ ability to respond to serious medical conditions using additional test cases, some of which are time series. Guided by medical doctors, our evaluation involves multiple machine learning models, resampling techniques, and four datasets for two clinical prediction tasks. We identify serious deficiencies in the models’ responsiveness, with the models being unable to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our study identifies similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models. Using generated test cases, we find that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Most of the ML models tested fail to respond adequately to critically ill patients. How to incorporate medical knowledge into clinical machine learning models is an important future research direction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Decentralised Reinforcement Learning for Dynamic Cyberattack Response in Microgrid Networks

Microgrids rely on communication networks for reliable operation, which makes them inherently vulnerable to cyberattacks. Such attacks can destabilise system dynamics and drive states away from their nominal operating trajectories. Although several physics-informed and machine learning-based strategies have been developed to counter these threats, the rapidly evolving cyber landscape enables adversaries to bypass static defences or rules-based mitigation approaches. This paper proposes a dynamic, online-trained and fully decentralised reinforcement learning (RL)-based cyberattack response framework to protect microgrids from evolving cyberattacks. The proposed framework deploys multiple deep Q-networks (DQNs), each associated with a distributed energy resource (DER), to enable localised and adaptive attack mitigation. In this framework, each DQN processes local voltage and frequency measurements—combined with intrusion detection system (IDS) alerts—as observations and rewards to guide decision-making. Extensive simulation studies demonstrate the robustness of the proposed framework under diverse attack scenarios and varying IDS-induced detection delays. Comparative analysis highlights its superiority over existing static or preexisting rules-based mitigation approaches. Finally, we present an analysis that shows the framework's scalability to real-life microgrids with more interacting agents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem

The widespread adoption of electric vehicles (EVs) in recent years has necessitated the development of effective charging infrastructures. However, charging infrastructure expansion is a multifaceted problem that requires careful consideration of the existing infrastructure, spatiotemporal distribution of charging demands, power-grid capacity, and budget constraints. Here, to approach this complex problem, we present CHARGE-MAP, a data-driven simulation-optimization framework, focused on ensuring meaningful charging experience for individual EV owners. CHARGE-MAP integrates three modules: an agent-based simulation module that estimates spatiotemporal distribution of charging demands by modeling EV adopter mobility and charging behavior; an optimization module that determines optimal new charging station/charger locations and capacities, while minimizing expected detour distances and wait-times with a limited number of new stations; and a power module that determines how to connect the stations to the power grid while maintaining its stability. Using the state of Virginia (consisting of 95 counties and 38 independent cities) as a case study, our results show that CHARGE-MAP can meet the demand of ~198,600 predicted EVs with 1,305 new public charging stations and 2,164 new chargers. It reduces average detour distances for charging by 66% and wait-times at stations by 72% compared to the existing infrastructure. Furthermore, transformer capacity requirement analysis reveals that only 1.8% of residential transformers require upgrades, while over 80% of commercial charging locations can be supported with modest transformer infrastructure (25 to 50 kVA). This indicates that targeted investments can facilitate cost-effective EV integration. Consequently, CHARGE-MAP provides policymakers and urban planners with crucial data-driven insights for effective EV charging infrastructure expansion. Sign up for PNAS alerts.

charging infrastructure↗
DOE OSTI2025

The overlooked interaction of emerging contaminants and microbial communities: a threat to ecosystems and public health

Abstract Context and aims Emerging contaminants (ECs) and microbial communities should not be viewed in isolation, but through the One Health perspective. Both ECs and microorganisms lie at the core of this interconnected framework, as they directly influence the health of humans, animals, and the environment. The interactions between ECs and microbial communities can have profound implications for public health, affecting all three domains. However, these ECs-microorganism interactions remain underexplored, potentially leaving significant public health and ecological risks unrecognized. Therefore, this article seeks to alert the scientific community to the overlooked interactions between ECs and microbial communities, emphasizing the pivotal role these interactions may play in the management of ‘One Health.’ Results The most extensively studied interaction between ECs and microbial communities is biodegradation. However, other more complex and concerning interactions demand attention, such as the impact of ECs on microbial ecology (disruptions in ecosystem balance affecting nutrient and energy cycles) and the rise and spread of antimicrobial resistance (a growing global health crisis). Although these ECs-microbial interactions had not been extensively studied, there are scientific evidence that ECs impact on microbial communities may be concerning for public health and ecosystem balance. Conclusions So, this perspective summarizes the impact of ECs through a One Health lens and underscores the urgent need to understand their influence on microbial communities, while highlighting the key challenges researchers must overcome. Tackling these challenges is vital to mitigate potential long-term consequences for both ecosystems and public health.

Gomes, Inês B. (ORCID:0000000207313662)↗
DOE OSTI2024

The variability structure function of the highest luminosity quasars on short time-scales

ABSTRACT The stochastic photometric variability of quasars is known to follow a random-walk phenomenology on emission time-scales of months to years. Some high-cadence rest-frame optical monitoring in the past has hinted at a suppression of variability amplitudes on shorter time-scales of a few days or weeks, opening the question of what drives the suppression and how it might scale with quasar properties. Here, we study a few thousand of the highest luminosity quasars in the sky, mostly in the luminosity range of $L_{\rm bol}$$=[46.4, 47.3]$ and redshift range of $z=[0.7, 2.4]$. We use a data set from the NASA/Asteroid Terrestrial-impact Last Alert System facility with nightly cadence, weather permitting, which has been used before to quantify strong regularity in longer term rest-frame-UV variability. As we focus on a careful treatment of short time-scales across the sample, we find that a linear function is sufficient to describe the UV variability structure function. Although the result can not rule out the existence of breaks in some groups completely, a simpler model is usually favoured under this circumstance. In conclusion, the data are consistent with a single-slope random walk across rest-frame time-scales of $\Delta t=[10, 250]$ d.

Tang, Ji-Jia (ORCID:0000000218600886)↗
DOE OSTI2026

NeuDiff Agent: a governed AI workflow for single-crystal neutron crystallography

Large-scale facilities increasingly face analysis and reporting latency as a limiting step in scientific throughput, particularly for structural studies that require iterative reduction, integration, refinement and validation. To improve the time to result and analysis efficiency, NeuDiff Agent is introduced as a governed, tool-using AI workflow for TOPAZ at the Spallation Neutron Source. NeuDiff Agent takes instrument data through reduction, integration, refinement and validation to a validated crystal structure and a publication-ready CIF. NeuDiff Agent coordinates established crystallographic tools under explicit governance by restricting actions to allowlisted tools, enforcing fail-closed verification gates at key workflow boundaries, and capturing complete provenance for inspection, auditing and controlled replay. The present benchmark is limited to structural crystallography for periodic structures; magnetic structure analysis and incommensurate or superspace refinement are outside the scope of the current workflow. Performance is assessed using a fixed prompt protocol and repeated end-to-end runs with two large language model backends, with user and machine time partitioned and intervention burden and recovery behaviors quantified under gating. In a reference-case benchmark, NeuDiff Agent reduces wall time from 435 min (manual) to 86.5 ± 4.7 to 94.4 ± 3.5 min (4.6–5.0× faster) while producing a validated CIF with no checkCIF level A or B alerts. These results establish a practical route to deploy agentic AI in facility crystallography while preserving traceability and publication-facing validation requirements.

Xiao, Zhongcan [ORNL] (ORCID:0000000220761961)↗