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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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161 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Usability of Pre-Flight Planning Interfaces for Supplemental Data Service Provider Tools to Support Uncrewed Aircraft System Traffic Management

Small uncrewed aircraft systems (sUASs) operate in low-altitude, uncontrolled airspace – where support services for their operators (UASOs) are not currently provided. NASA’s System-Wide Safety (SWS) project is identifying the potential risks and hazards to sUAS operations to provide, inform, and improve the designs of In-time Aviation Safety Management Systems (IASMS). The IASMS will include a suite of data-driven tools that compile and analyze data collected from aviation systems and environmental sources to predict hazards, and provide information to allow operators to mitigate these risks (Young et al., 2020). These risk and hazard services can be run and displayed to operators on graphical user interfaces (GUIs), as they relate to a vehicle(s)’ route of flight. These interfaces offer both a means to present hazard service output and offer an opportunity to test user understanding of the information, user decision making, and the best ways to present such data to an operator. Based on these future technologies and intended missions, it is important to investigate interface requirements and evaluate how operators might use these tools. Presenting salient and meaningful risk assessment information to operators is necessary to increase situation awareness and ultimately safety. Building on previous research (Feldman et al., 2022), a usability study comparing two GUIs was conducted to explore how individuals interacted with different styles of information displays. A series of pre-flight hazard and risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider Consolidated Dashboard and the Human Automation Team Interface System interfaces. Participants were trained to use both GUIs and their performance was analysed across different scenarios involving multiple sUASs. Performance on simple tasks and the System Usability Scale scores were reported by Feldman et al., 2023. Additional analyses and evaluations on more complex tasks (e.g., risk assessment, prioritization), workload and response times are examined in this paper.

sUAV interfaces↗
NASA NTRSDate not supplied

Usability of Pre-flight Planning Interfaces for Supplemental Data Service Provider Tools to Support Uncrewed Aircraft System Traffic Management

Small uncrewed aircraft systems (sUASs) operate in low-altitude, uncontrolled airspace – where support services for their operators (UASOs) are not currently provided. NASA’s System-Wide Safety (SWS) project is identifying the potential risks and hazards to sUAS operations to provide, inform, and improve the designs of In-time Aviation Safety Management Systems (IASMS). The IASMS will include a suite of data-driven tools that compile and analyze data collected from aviation systems and environmental sources to predict hazards, and provide information to allow operators to mitigate these risks (Young et al., 2020). These risk and hazard services can be run and displayed to operators on graphical user interfaces (GUIs), as they relate to a vehicle(s)’ route of flight. These interfaces offer both a means to present hazard service output and offer an opportunity to test user understanding of the information, user decision making, and the best ways to present such data to an operator. Based on these future technologies and intended missions, it is important to investigate interface requirements and evaluate how operators might use these tools. Presenting salient and meaningful risk assessment information to operators is necessary to increase situation awareness and ultimately safety. Building on previous research (Feldman et al., 2022), a usability study comparing two GUIs was conducted to explore how individuals interacted with different styles of information displays. A series of pre-flight hazard and risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider Consolidated Dashboard and the Human Automation Team Interface System interfaces. Participants were trained to use both GUIs and their performance was analysed across different scenarios involving multiple sUASs. Performance on simple tasks and the System Usability Scale scores were reported by Feldman et al., 2023. Additional analyses and evaluations on more complex tasks (e.g., risk assessment, prioritization), workload and response times are examined in this paper.

sUAV interfaces↗
NASA NTRSDate not supplied

The Human Research Program Grant Lifecycle & Data Integration Schedule: Infographic

The Human Research Program (HRP) Grant Lifecycle process can be described using information from several government authoritative sources with overlapping generalizations. It is the responsibility of the HRP Program Planning & Control (PP&C) Office and the Data Management Integration Office (DMIO)to interpret this information into a cohesive process that can be communicated to the human research organization. PP&C and DMIO have been exploring training opportunities to facilitate understanding through the form of infographics. This communication tool combines eye-catching visuals and text making complex information and data more digestible and shareable. The infographic poster tells a short story about a federally awarded grant and is intended to help both the new and experienced HRP workforce understand the timeline for a research procurement and where their specific tasks fall within the 4 phases of the grant lifecycle. The Pre-Award, Award, Post-Award, and Closeout phases are overlayed with business swimlanes so HRP stakeholders can see where they fit into the timeline rather than working in an isolated part. This includes the Chief Scientist Office (CSO), PP&C, the Data Management Integration Office (DMIO), the Principal Investigator (PI), the Element stakeholders, the LSDA Archivists, and the Research & Operations Integration (ROI) team along with the stakeholders in the Human Health and Performance Directorate. Additionally, the poster introduces (1) the HRP Data Integration Schedule identified in the HRP Data Management Plan HRP-48047, Table 8-2 and (2) a Smartsheet Solution to automate the grants tracking business process. Emphasis is given to the Data Integration Schedule which is the basis for the milestone tasks that are to be completed during the Post-Award phase in the grant lifecycle. The poster is also an opportunity to present the Smartsheet Solution, a software collaboration and work management tool used to assign tasks and track project progress. This PP&C FY24 effort will facilitate an end-to-end solution to track grants and provide insight to the health of HRP grant research procurements using metrics, reports, and dashboards.

J Peace↗
NASA NTRS2023

Exploring Interactive Visuals for Orbital Debris Report

NASA often produces reports or memos that include complex data and these findings can often be most effectively communicated using dynamic or interactive visualizations. While newsrooms, such as the New York Times, and academic journals, such as Distill, have demonstrated the power of interactivity within a prose context (i.e., not dashboards), NASA is still lagging in this area. A shift towards more interactive NASA publications will require new technology and expertise. In this presentation, we begin to explore the avenues that the Office of Technology, Policy, and Strategy are using to make its reports interactive by using results from the “Cost and benefit Analysis of Orbital Debris Remediation” as initial examples. We share our current findings and aim to start a discussion with other members of the NASA analytics community interested in or currently adding interactivity to Agency products.

data visualization↗
NASA NTRSDate not supplied

SREDD: Framework to facilitate MLopS related to discovery of commercial satellite data

SREDD or Super Resolution Event Detection Dashboard is a comprehensive data discovery platform designed to facilitate the identification of multiple events. It is equipped to handle data of varying spatial resolutions and from various vendors, acquired by NASA’s Commercial Smallsat Data Acquisition (CSDA) Program, making it a convenient centralized hub for searching event-related information.

Sujit Roy↗
NASA NTRS2021

Covid-19 Impact Monitoring for Climate Environment (Greenhouse Gases)

To track atmospheric CO2 changes resulting from the lockdowns, observations collected by the NASA Orbiting Carbon Observatory-2 (OCO-2) satellite and Japan’s Greenhouse gases Observing SATellite (GOSAT) in 2020 were compared to results collected in previous years. The OCO-2 results were used to search for changes on regional scales over the globe. Targeted observations from GOSAT were used to track changes in large urban areas, such as Beijing and Tokyo. Both types of observations yielded key insights into the CO2 changes accompanying the economic disruptions caused by the COVID-19 lockdowns. ESA, NASA and JAXA developed the dashboard jointly to monitor the climate impacts of COVID-19.

greenhouse gases↗
NASA NTRSDate not supplied

Development of TEMPO Products and Tools to Support Air Quality Management Decisions

The TEMPO mission has been observing air pollutants every hour during the daytime across its Field of Regard (FoR) covering greater North America since First Light on August 2, 2023. The highly anticipated public release of TEMPO data occurred on May 20, 2024, consisting of level 2 and level 3 trace gas data products of nitrogen dioxide, formaldehyde, and ozone. Our project at the NASA SPoRT Center is developing value-added products and tools to support the TEMPO mission and Early Adopters program with special attention on the air quality management community. The initial focus of this project is evaluating the TEMPO products over stakeholder target areas using Pandora and surface monitor observations. Methods for oversampling TEMPO data to 1 km resolution are being applied over the target areas to resolve fine-scale emission sources and pollutant gradients. Machine learning techniques using TEMPO, surface monitor, and model data to estimate surface-level nitrogen dioxide concentrations are being developed over the target areas. Our SPoRT viewer has been updated to include visualizations of the TEMPO products and an ArcGIS dashboard is being designed for enabling air quality management stakeholders to efficiently analyze TEMPO data. Training materials including user guides are being developed to ensure the effective and sustained use of TEMPO data in air quality management applications. The major outcome of this project is to support the inclusion of TEMPO data in exceptional event demonstrations by active engagement with stakeholders and ultimately enable more informed air quality management decisions in the future. This talk will provide an update on our project activities and showcase use cases of TEMPO data for monitoring different emission sources including wildland fire smoke.

air quality↗
NASA NTRSDate not supplied

ExMC Systems Engineering Developments

The Exploration Medical Capability (ExMC) Element within the Human Research Program (HRP) applies systems engineering (SE) principles along with the use of Model-Based Systems Engineering (MBSE) tools to identify and communicate the requirements for medical and crew health and performance (CHP) systems. In the past fiscal year, the MBSE approach sought to advance the digital engineering toolset for medical and CHP system representation. These digital artifacts provide enhanced views of the relationships among requirements, standards, functions, and capabilities, to name a few, that is best suited for a user’s objectives. The MBSE tools and SE practices were applied to the development of the revised Earth Independent Medical Operations (EIMO) medical system and the Artemis III and IV CHP System models. In addition, a System of Systems concept was integrated into the EIMO model to facilitate the identification of system interfaces that interact with the medical system. Finally, the ExMC SE team has initiated several efforts to bring operationally relevant digital engineering practices to the Human Health and Performance Directorate (HHPD). This included the development of a pilot program within the directorate to help foster utilization of tools such as MagicDraw for system modeling and Power BI for visualizing extracted data in an easily accessible dashboard format. Additionally, with the increase in complexity of the integration effort for Artemis missions, ExMC has endeavored to bring digital engineering strategies to potentially increase efficiency in review processes. This talk will provide a high-level overview of the ExMC SE team accomplishments since the last Investigators’ Workshop, an introduction to upcoming SE talks, and the ongoing systems engineering work.

systems engineering↗
NASA NTRSDate not supplied

Transitioning TEMPO Data for Air Quality Management Applications at the NASA SPoRT Center

The TEMPO mission has been observing air pollutants every hour during the daytime across its Field of Regard (FoR) covering greater North America since First Light on August 2, 2023. The highly anticipated public release of TEMPO data occurred on May 20, 2024, consisting of level 2 and level 3 trace gas data products of nitrogen dioxide, formaldehyde, and ozone. The NASA SPoRT Center is developing value-added products and tools to support the TEMPO mission and Early Adopters program with special attention on stakeholder from air agencies. One component of our work is focused on evaluating the TEMPO products over stakeholder target areas using Pandora and surface monitor observations to characterize the uncertainties and develop best practices for processing and analyzing TEMPO data. Methods for oversampling TEMPO data to 1 km resolution are being applied over the target areas to resolve fine-scale emission sources and pollutant gradients. Machine learning techniques using TEMPO, surface monitor, and model data to estimate surface-level nitrogen dioxide concentrations are being developed over the target areas. A SPoRT viewer for TEMPO has been launched for providing visualizations of the TEMPO products and an ArcGIS dashboard is being designed for enabling air agency stakeholders to efficiently analyze TEMPO data. Training materials including user guides are being developed to ensure the effective and sustained use of TEMPO data at our stakeholder agencies. One major goal of our TEMPO initiatives at SPoRT is to better enable the inclusion of TEMPO data in air quality management applications such as exceptional event demonstrations through our close engagement with stakeholders. This talk will provide an update on our SPoRT activities and showcase use cases of TEMPO data for monitoring different emission sources including wildland fire smoke.

Air Quality↗
NASA NTRSDate not supplied

Dynamic Anomaly Response and Integrated Analysis (DARIA): A Fault Investigation Toolset Supporting Earth-Independent Operations in Future Crewed Mars Missions

NASA's Moon to Mars Objectives outline a strategic vision for human spaceflight culminating in crewed missions to Mars. A critical component of this objective is the development of systems that are capable of being Earth-Independent Operated (EIO). A key aspect of EIO is the ability to rapidly detect, diagnose, and respond to anomalies in crew-supporting habitat and connected systems. To address this, the Dynamic Anomaly Response and Integrated Analysis (DARIA) architecture has been developed. DARIA incorporates a network of compact, wireless sensing devices called the System for Telemetry Amalgamation of Multimodal PrognosticS (STAMPS) for increased state awareness of EIO habitats. Able to perform on-the-fly data acquisition, STAMPS are connected to integrated anomaly data dashboards for crew visualization. DARIA is designed to seamlessly integrate into various off-world environments, including the International Space Station, Lunar Gateway, Artemis Base Camp, and future Mars habitats. By leveraging Commercial Off-The-Shelf (COTS) hardware, the NASA Internet of Things (NASA IoT) framework, and EIO fault detection methods, DARIA provides a cost-effective and adaptable solution for anomaly detection and response in EIO habitats.

Diagnostics↗
DOE OSTI2025

Workflow for Process Automation of Soil Gas Results from an Automated Soil Gas-Sampling System for Application in Carbon Storage Projects

Extended abstract for Geoconvention, Calgary, Alberta, Canada, May 12–14, 2025. The Energy & Environmental Research Center (EERC) developed an automated workflow for processing soil gas measurements collected from the automated soil gas-sampling systems deployed across the project site. Raw soil gas measurements are collected from each station every 4 hours and automatically uploaded to a cloud database. The workflow begins by writing code to download the data to a workstation automatically, then the data are published to an online dashboard that visualizes the measurements in time-series plots and a process-based decision-making framework. This automated workflow accelerates the time from data acquisition to decision-making. It supports carbon storage project operators by preparing and delivering a live, standardized dataset for quick analysis and source attribution to provide assurance of containment and overall permit compliance.

02 PETROLEUM↗
DOE OSTI2026

Optimal Operation of Residential High Performance Water Heater for Reduction of Electricity Cost and Peak Demand Through Field Validation

Water heating accounts for about 18% of a typical US home’s energy use. Modern water heaters have enabled control options through APIs, offering customers the opportunity to reduce their energy cost and peak demand by dynamically adjusting settings. A water heater’s capacity to store energy using its storage tank makes it an asset for peak demand reduction and energy cost savings. For this reason, a mixed-integer linear programming model is proposed to minimize the energy cost of a high-performance water heater while also reducing the peak demand of the residential household under a time-of-use utility rate by dynamically changing the water heater’s running mode. Specifically, a multi-objective optimization model is formulated to determine the mode settings of the water heater considering hot water use, time-of-use rate, and peak demand limit of the residential household. The mode settings are associated with different dead bands of water temperature for triggering on/off action of the heat pump and heating element. A 66-gal hybrid electric high performance water heater was used for numerical simulation and practical experiments. The simulation results were well aligned with measurements of practical experiments, validating the soundness of the thermodynamic model. In addition, reductions of energy cost, enabling affordability, and reducing peak demand are demonstrated. The research team also developed a software framework with dashboards to automatically and continuously monitor and manage devices.

Liu, Guodong [ORNL] (ORCID:0000000213498608)↗
DOE OSTI2026

A Physics-Informed Reinforcement Learning Framework for Economic-Thermal Co-Optimization of Crypto Mining Data Centers: Preprint

The rapid expansion of cryptocurrency mining has created a new class of high-density data centers characterized by extreme thermal flux and high sensitivity to volatile economic markets. Traditional thermal management strategies, typically reliant on rule-based control, maintain static setpoints that fail to account for fluctuating electricity prices and cryptocurrency values - factors critical to mining profitability. To address this, we present a physics-informed reinforcement learning (PIRL) framework for economic-thermal co-optimization in crypto mining data centers. This framework consists of a proximal policy optimization (PPO) agent, a virtual testbed powered by high-fidelity physics-based models, and an interactive frontend dashboard. The PPO agent is trained using the virtual testbed and strict hardware safety limits. This physics-informed approach allows the agent to learn a stochastic policy that dynamically balances mining revenue against operational costs by co-optimizing HVAC cooling setpoints and IT computational hashrate. The simulation results demonstrate that the integrated framework achieved an 8.62% increase in net operational profit compared to traditional baseline strategies while strictly adhering to safety-critical temperature constraints (coolant supply temperature < 32 degrees C). This work provides a scalable template for the deployment of reinforcement learning in mission critical facilities where economic volatility and physical safety must be managed simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A Science Gateway for the Repeatable Analysis of Machine Learning Predicted Gravity Anomalies

In recent years, deep learning has become an increasingly popular alternative for modeling in geoscience applications due to its scalability and efficiency. However, the interpretability, compute, data volume, and hyperparameter tuning requirements of deep learning models make development and monitoring difficult. Furthermore, model explainability and communicating results obtained by these models to users or domain experts is a challenge, as domain experts in geoscience also need to have a deep understanding of how those models function in order to support their scientific works. Here, we describe a science gateway and machine learning pipeline for predicting gravity anomalies from geophysical data. The gateway, built on open-source technologies, provides a holistic view of the pipeline through interactive visualizations aimed at enabling efficient exploratory data analysis. The repeatability, reproducibility, and monitoring capabilities of this overall system allow us to iterate and analyze at scale. Using this pipeline and gateway, we can repeatedly produce accurate high-resolution gravity anomaly datasets. By describing the underlying technologies, implementation, and results, here we provide a foundation for the broader adoption of science gateways into cross-cutting geoscience and machine learning research projects as a means to improve the scientific discovery and collaboration in the geophysics and computational sciences community.

58 GEOSCIENCES↗
DOE OSTI2024

eGridGPT: Trustworthy AI in the Control Room

This report outlines the synergy between human decision making and generative artificial intelligence (GenAI), where GenAI supports power system operators by analyzing procedures, suggesting actions, simulating scenarios with physics-based digital twins, and recommending optimal decisions. This report is the first research effort to apply large language models (LLMs), a type of GenAI, in the power grid control room. The authors describe the Electric Grid Generative Pretrained Transformer (eGridGPT), an LLM that virtually assists system operators. Developed with cybersecurity and regulatory requirements in mind, eGridGPT represents an opportunity to responsibly evolve control room technologies to meet the needs of a rapidly changing grid. As an innovative concept, eGridGPT seeks to spearhead productive discussions about the advanced technologies in the control room of the future amid the transition to clean energy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2017

Metadata Evaluation and Improvement: Evolving Analysis and Reporting

ESIP Community members create and manage a large collection of environmental datasets that span multiple decades, the entire globe, and many parts of the solar system. Metadata are critical for discovering, accessing, using and understanding these data effectively and ESIP community members have successfully created large collections of metadata describing these data. As part of the White House Big Earth Data Initiative (BEDI), ESDIS has developed a suite of tools for evaluating these metadata in native dialects with respect to recommendations from many organizations. We will describe those tools and demonstrate evolving techniques for sharing results with data providers.

metadata recommendations↗