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NASA NTRS2000

Coupling Satellite and Ground-Based Instruments to Map Climate Forcing by Anthropogenic Aerosol

Climate forcing by anthropogenic aerosols is a significant but highly uncertain factor in global climate change. Only satellites can offer the global coverage essential to reducing this uncertainty; however, satellite measurements must be coupled with correlative, in situ measurements both to constrain the aerosol optical properties required in satellite retrieval algorithms and to provide chemical identification of aerosol sources. This grant funded the third year of a three-year project which seeks to develop methodologies for combining spaceborne lidar with in-situ aerosol data sets to improve estimates of direct aerosol climate forcing. Progress under this one-year grant consisted in analysis and publication of field studies using a new in-situ capability for measuring aerosol 180 deg backscatter and the extinction-to-backscatter ratio. This new measurement capacity allows definitive lidar/in-situ comparisons and improves our ability to interpret lidar data in terms of climatically relevant quantities such as the extinction coefficient and optical depth. Analyzed data consisted of measurements made along the coast of Washington State, in Central Illinois, over the Indian Ocean, and in the Central Pacific. Thus, this research, combined with previous measurements by others, is rapidly building toward a global data set of extinction-to-backscatter ratio for key aerosol types. Such information will be critical to interpreting lidar data from the upcoming PICASSO-CENA, or P-C, satellite mission. Another aspect of this project is to investigate innovative ways to couple the lidar-satellite signal with target in-situ measurements toward a direct determination of aerosol forcing. This aspect is progressing in collaboration with NASA Langley's P-C lidar simulator.

Charlson, Robert J.↗

NASA NTRSDate not supplied

ATTRACTOR: Toward Trustworthy and Trusted Autonomous Systems

The question of what it means and what it takes for an autonomous system to consider another autonomous system justifiably trustworthy must be addressed by all who seek to integrate intelligent machine agents into real-world operations. A satisfactory answer to this question is an essential component in accepting autonomous machine decision-making in safety-critical and time-critical environments, such as aviation. Historically, simulation platforms for test and evaluation of complex systems have proven to be effective in assessing performance and contributing to decisions on the fitness of systems to operate in current general and commercial aviation airspace. Moreover, simulations have informed the definition of safety-critical constraints. However, as machine systems progressively take on responsibilities for decision-making traditionally supplied by humans, simulations require enhancement. Mixed reality simulation that integrates real-world platforms and data or high-fidelity simulation data in a sim-to-flight paradigm provides insight into agent interaction and the rationale behind autonomous agent decision-making as well as the capacity for seamless integrated implementation, testing, and operation of systems. Strong simulation capabilities are especially important in the presence of algorithms that hold great promise in decision-making yet increase the uncertainty in the system. Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) is a subproject of NASA’s Convergent Aeronautics Solutions (CAS) Project. ATTRACTOR’s objective is to build a basis for understanding trust and trustworthiness in multi-agent autonomous teams, and thus to inform future certification of safety-critical and time-critical autonomous systems in aviation. Because the concepts of trust and trustworthiness must be addressed in a context, ATTRACTOR has chosen Search and Rescue (SAR) in dynamic and unstructured environments, with emphasis on search, as its design reference mission (DRM). During dynamic planning and execution of trajectory-based operations, autonomous agents determine their trajectories given an assigned mission or missions and call for assistance from an appropriate teammate when needed. This experience along with the attendant human-machine and machine-machine interactions, serve as a platform for developing approaches to identifying and measuring trustworthiness and increasing trust. In this paper, we give an overview of some of ATTRACTOR’s research and development activities, findings, and ongoing work.

ATTRACTOR↗

DOE OSTI2024

Bill Savings vs. Backup Power: Evaluating operational tradeoffs for home solar+storage systems [Slides]

Adoption of residential solar photovoltaic+energy storage systems (PVESS) is driven by both bill savings opportunities and customer demand for backup power. Prior work by this team (Gorman et al., 2022; Gorman et al., 2023) explored PVESS backup power capabilities during long-duration power interruptions (e.g., due to severe weather events), when customers are assumed to be able to anticipate the event and charge their batteries in advance. In many cases, however, power interruptions are unpredictable (and often relatively short); for those types of events, a customer will typically set its battery to maintain some minimum capacity in reserve in case of an interruption, which reduces the capacity available for managing utility bills. This study evaluates this operational tradeoff to help customers and installers configure backup reserve settings, and to inform decision-making more generally about the customer value of backup power services compared to utility bill savings. This study utilizes Berkeley Lab’s PRESTO tool to produce stochastic simulations of (predominantly short-duration) power interruption events, and builds on an earlier case-study demonstrating PVESS backup performance during short-duration interruptions (Baik et al., 2023).

14 SOLAR ENERGY↗

NASA NTRS1984

Design concept definition study for an improved shuttle waste collection subsystem

A no-risk approach for developing an Improved Waste Collection Subsystem (WCS) for the shuttle orbiter is described. The GE Improved WCS Concept builds on the experience of 14 Shuttle missions with over 400 man-days of service. This concept employs the methods of the existing flight-proven mature design, augmenting them to eliminate foreseen difficulties and to fully comply with the design requirements. The GE Improved WCS Concept includes separate storage for used wipes. Compaction of the wipes provides a solution to the capacity problem, fully satisfying the 210 man-day storage requirement. The added feature of in-flight serviceable storage space for the wipes creates a variable capacity feature which affords redundancy in the event of wipes compaction system failure. Addition of features permitting in-flight servicing of the feces storage tank creates a variable capacity WCS with easier post-flight servicing to support rapid turnaround of the Shuttle orbiter. When these features are combined with a vacuum pump to evacuate wipes and fecal storage tanks through replaceable odor/bacteria filters to the cabin, the GE Improved WCS satisfies the known requirements for Space Station use, including no venting to space.

Source record↗

NASA NTRS2000

Next Generation CTAS Tools

The FAA's Free Flight Phase 1 Office is in the process of deploying the current generation of CTAS tools, which are the Traffic Management Advisor (TMA) and the passive Final Approach Spacing Tool (pFAST), at selected centers and airports. Research at NASA is now focussed on extending the CTAS software and computer human interfaces to provide more advanced capabilities. The Multi-center TMA (McTMA) is designed to operate at airports where arrival flows originate from two or more centers whose boundaries are in close proximity to the TRACON boundary. McTMA will also include techniques for routing arrival flows away from congested airspace and around airspace reserved for arrivals into other hub airports. NASA is working with FAA and MITRE to build a prototype McTMA for the Philadelphia airport. The active Final Approach Spacing Tool (aFAST) provides speed and heading advisories to help controllers achieve accurate spacing between aircraft on final approach. These advisories will be integrated with those in the existing pFAST to provide a set of comprehensive advisories for controlling arrival traffic from the TRACON boundary to touchdown at complex, high-capacity airports. A research prototype of aFAST, designed for the Dallas-Fort Worth is in an advanced stage of development. The Expedite Departure Path (EDP) and Direct-To tools are designed to help controllers guide departing aircraft out of the TRACON airspace and to climb to cruise altitude along the most efficient routes.

Erzberger, Heinz↗

DOE OSTI2025

Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

Hansson, Frederik G↗

DOE OSTI2026

Toluene adsorption and capacity regeneration using zeolite-based monolith and activated carbon fiber felt

Common adsorbents studied to remove volatile organic compounds (VOCs) from indoor air can release previously adsorbed VOCs back into the indoor space when the adsorbent is saturated or if the VOC concentration fluctuates. A durable adsorbent with a continuous regeneration strategy could prevent this recontamination of the indoor air and reduce adsorbent disposal waste. A deeper understanding of the adsorption and desorption behavior of VOC adsorbents is needed to create an efficient regeneration strategy. This study investigated the adsorption and thermal desorption behavior of toluene on two different adsorbents, (1) a zeolite-based adsorbent and (2) an activated carbon fiber (ACF) felt. Consistent adsorption behavior across a series of toluene concentrations was used to experimentally determine effective adsorption capacity. When the cumulative adsorption between thermal regeneration steps was maintained below the maximum effective capacity with 0% breakthrough, the zeolite-based adsorbent was found to effectively minimize passive desorption. The impact of regeneration feed conditions, such as inert, oxidizing, humidified air, on thermal desorption was examined. These conditions influenced desorption of the zeolite-based adsorbent but had minimal impact on the ACF felt adsorbent, which would lead to different regeneration schedules and methods for applications in buildings' heating, ventilation, and air conditioning (HVAC) systems.

Moses-DeBusk, Melanie [Oak Ridge National Laborato↗

NASA NTRS1978

Solar heating and hot water system installed at Listerhill, Alabama

The Solar system was installed into a new building and was designed to provide 79% of the estimated annual space heating load and 59% of the estimated annual potable hot water requirement. The collectors are flat plate, liquid manufactured by Reynolds Metals Company and cover a total area of 2344 square feet. The storage medium is water inhibited with NALCO 2755 and the container is an underground, unpressurized steel tank with a capacity of 5000 gallons. This report describes in considerable detail the solar heating facility and contains detailed drawings of the completed system.

Source record↗

NASA NTRS2005

Developing a Framework for Effective Network Capacity Planning

As Internet traffic continues to grow exponentially, developing a clearer understanding of, and appropriately measuring, network's performance is becoming ever more critical. An important challenge faced by the Information Resources Directorate (IRD) at the Johnson Space Center in this context remains not only monitoring and maintaining a secure network, but also better understanding the capacity and future growth potential boundaries of its network. This requires capacity planning which involves modeling and simulating different network alternatives, and incorporating changes in design as technologies, components, configurations, and applications change, to determine optimal solutions in light of IRD's goals, objectives and strategies. My primary task this summer was to address this need. I evaluated network-modeling tools from OPNET Technologies Inc. and Compuware Corporation. I generated a baseline model for Building 45 using both tools by importing "real" topology/traffic information using IRD's various network management tools. I compared each tool against the other in terms of the advantages and disadvantages of both tools to accomplish IRD's goals. I also prepared step-by-step "how to design a baseline model" tutorial for both OPNET and Compuware products.

Yaprak, Ece↗

NASA NTRS1994

Design of a high capacity long range cargo aircraft

This report examines the design of a long range cargo transport to attempt to reduce ton-mile shipping costs and to stimulate the air cargo market. This design effort involves the usual issues but must also include consideration of: airport terminal facilities; cargo loading and unloading; and defeating the 'square-cube' law to design large structures. This report reviews the long range transport design problem and several solutions developed by senior student design teams at Purdue University. The results show that it will be difficult to build large transports unless the infrastructure is changed and unless the basic form of the airplane changes so that aerodynamic and structural efficiencies are employed.

Weisshaar, Terrence A.↗

DOE OSTI2025

The Artificial Scientist: in-Transit Machine Learning of Plasma Simulations

Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.

Kelling, Jeffrey [Helmholtz-Zentrum Dresden Rossen↗

NASA NTRS2005

Development of the Surface Management System Integrated with CTAS Arrival Tools

The Surface Management System (SMS) developed by NASA Ames Research Center in coordination with the Federal Aviation Administration (FAA) is a decision support tool to help tower traffic coordinators and Ground/Local controllers in managing and controlling airport surface traffic in order to increase capacity, efficiency, and flexibility. SMS provides common situation awareness to personnel at various air traffic control facilities such as airport traffic control towers (ATCT s), airline ramp towers, Terminal Radar Approach Control (TRACON), and Air Route Traffic Control Center (ARTCC). SMS also provides a traffic management tool to assist ATCT traffic management coordinators (TMCs) in making decisions such as airport configuration and runway load balancing. The Build 1 of the SMS tool was installed and successfully tested at Memphis International Airport (MEM) and received high acceptance scores from ATCT controllers and coordinators, as well as airline ramp controllers. NASA Ames Research Center continues to develop SMS under NASA s Strategic Airspace Usage (SAU) project in order to improve its prediction accuracy and robustness under various modeling uncertainties. This paper reports the recent development effort performed by the NASA Ames Research Center: 1) integration of Center TRACON Automation System (CTAS) capability with SMS and 2) an alternative approach to obtain airline gate information through a publicly available website. The preliminary analysis results performed on the air/surface traffic data at the DFW airport have shown significant improvement in predicting airport arrival demand and IN time at the gate. This paper concludes with recommendations for future research and development.

Jung, Yoon C.↗

DOE OSTI2026

Enhancing Room-Temperature Hydrogen Storage Properties in Ti–V–Cr–Nb–Mo/Fe Multielement Alloys via Substitutional Tuning

Building upon previously reported high-entropy alloys (HEA) in the Ti−V−Cr−Nb−Mo/Fe compositional space, this study presents a novel bcc alloy containing 6 at. % of the earthabundant element Fe, exhibiting enhanced hydrogen storage properties at room temperature (25 °C). The optimized bcc Ti 23 V 30 Nb 10 Cr 31 Fe 6 alloy rapidly absorbs hydrogen at room temperature with a maximum gravimetric capacity of 3.4 wt % and an enthalpy of hydride formation of −34 kJ/mol H 2 . This value is among the lowest reported to date for bcc HEAs. The reversible capacity of this alloy remains stable at 2.0 wt % upon 20 absorption/desorption cycles at room temperature, which is among the highest reported values for this material class. Collectively, these advances highlight the potential of Fecontaining multielement alloys as efficient and economical candidates for next-generation hydrogen storage systems.

08 HYDROGEN↗

NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗

NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

Erika Munshi↗

NASA NTRSDate not supplied

From NASA's EOS to ESO: Advancing Applications of the Future Atmosphere Observing (AOS) Mission

The NASA Earth System Observatory (ESO) Atmosphere Observing System (AOS) is being designed to explore the fundamental questions of how interconnections between aerosols, clouds and precipitation impact our weather and climate, addressing real-world challenges to benefit society. A fundamental component of the AOS mission is ensuring that applications for economic and societal benefit are considered to the greatest extent possible in mission design. As a result, the AOS Applications Impact Team (AIT) was formed to address this objective. The overarching goal of the AIT is to help improve capacity for transitioning science to applications to make it possible to more quickly and effectively inform decisions that will directly benefit society. We seek to maximize AOS benefit to impact decisions through early engagement in the mission development phase in order to prepare stakeholders to apply observations as soon as AOS mission data become available. A critical component of preparing future users of AOS mission data is building on the successes of applications of NASA’s existing Earth Observing System (EOS), A-Train, and sub-orbital campaigns with the goal of advancing current mission applications activities and preparing for innovative AOS mission observations. NASA’s GPM mission forms a framework to enhance AOS precipitation applications while AOS health and air quality applications benefit from the heritage of CALIPSO and MODIS. Additionally, AOS will likely benefit from current and future missions such as TROPICS, MAIA, TEMPO, and PACE which launch before AOS. Additionally, current sub-orbital field campaigns, such as NASA IMPACTS and ACTIVATE, provide rich data sources to highlight future AOS capabilities. Engaging with existing missions and sub-orbital field campaigns helps to identify and understand data needs, gaps, and opportunities for current and future stakeholders, determine what data products are of highest value and use, and connect stakeholders with current mission data that can serve as AOS proxy data, among others. In this presentation, we provide an overview of AOS AIT activities, initiatives, and the AOS Applications Seminar Series to highlight how existing EOS, A-Train, and sub-orbital missions can play a critical role in advancing AOS applications prior to launch.

Emily B. Berndt↗

NASA NTRS2016

Behavioral, Brain Imaging and Genomic Measures to Predict Functional Outcomes Post - Bed Rest and Spaceflight

Astronauts experience sensorimotor disturbances during their initial exposure to microgravity and during the re-adaptation phase following a return to an Earth-gravitational environment. These alterations may disrupt crewmembers' ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from spaceflight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual spaceflight, which crewmembers are likely to experience the greatest challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures. Our approach includes: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features, using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; and 3) assessment of genotypic markers of genetic polymorphisms in the catechol-O-methyl transferase, dopamine receptor D2, and brain-derived neurotrophic factor genes and genetic polymorphisms of alpha2-adrenergic receptors that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate that these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration spaceflight and exposure to an analog bed rest environment. We will be conducting a retrospective study, leveraging data already collected from relevant ongoing or completed bed rest and spaceflight studies. These data will be combined with predictor metrics that will be collected prospectively (as described for behavioral, brain imaging and genomic measures) from these returning subjects to build models for predicting post-mission (bed rest - non-astronauts or space flight - astronauts) adaptive capability as manifested in their outcome measures. To date we have completed a study on 15 normal subjects with all of the above measures. In this presentation we will discuss the optimized set of tests for predictive metrics to be used for evaluating post mission adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures against decrements in post-mission adaptive capability that are customized for each crewmember's sensory biases, adaptive capacity, brain structure and functional capacities, and genetic predispositions. The ability to customize adaptability training will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to ensure expected outcomes.

Mulavara, A. P.↗

NASA NTRS2011

Experimental Studies of NaK in a Simulated Space Environment

Space fission power systems are being developed at the National Aeronautics and Space Administration (NASA) and Department of Energy (DOE) with a short term goal of building a full scale, non-nuclear, Technology Demonstration Unit (TDU) test at NASA's Glenn Research Center. Due to the geometric constraints, mass restrictions, and fairly high temperatures associated with space reactors, liquid metals are typically used as the primary coolant. A eutectic mixture of sodium (22 percent) and potassium (78 percent), or NaK, has been chosen as the coolant for the TDU with a total system capacity of approximately 55 L. NaK, like all alkali metals, is very reactive, and warrants certain safety considerations. To adequately examine the risk associated with the personnel, facility, and test hardware during a potential NaK leak in the large scale TDU test, a small scale experiment was performed in which NaK was released in a thermal vacuum chamber under controlled conditions. The study focused on detecting NaK leaks in the vacuum environment as well as the molecular flow of the NaK vapor. This paper reflects the work completed during the NaK experiment and provides results and discussion relative to the findings.

Gibons, Marc↗