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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Current Status of NASA's Fission Surface Power Project

Small nuclear fission systems are powerful and could enable robust space operations for planetary habitation and exploration. On Earth, unless disrupted by storms or grid problems, electrical power for most people is no further than an outlet away. However, the solar system does not provide such easy access to electricity as we know it. Astronauts could take advantage of a reliable power supply to explore both the Moon and Mars. The system will need to be lightweight and capable of running regardless of its location, the weather, or available sunlight and other natural resources. NASA’s Fission Surface Power (FSP) project expands on the efforts of the agency’s Kilopower project, which ended in 2018. Currently, NASA is working with the US Department of Energy and industry to design a fission power system that would provide at least 40 kilowatts of power – enough to continuously run 30 households for ten years. A future lunar demonstration will pave the way for sustainable operations and even base camps on the Moon and Mars. This manuscript will showcase the current state of the FSP project, technical goals and accomplishments, future plans, and how this technology paves the way for exciting future applications.

Nuclear↗
NASA NTRS2024

MAGIK Animation of Robotic Operations for Flight SpaceX-31 to ISS

The MAGIK Robotics analysis team provides kinematic feasibility assessments for the ISS and has produced an animation to showcase Extravehicular Robotics (EVR) activities for Flight SpaceX (SpX)-31. Shown EVR activities were developed by Flight Operations Directorate (FOD) Robotics (ROBO). The animation also displays the current ISS configuration at the time of the flight in Increment 72. Flight SpX-31 includes a SpaceX Cargo Dragon vehicle docked to Node 2 Forward (N2F) and consists of the delivery of the Coronal Observation Experiment (CODEX) payload. Robotic operations shown include utilization the Special Purpose Dexterous Manipulator (SPDM) based on the Space Station Remote Manipulator System (SSRMS), survey of the cargo trunk, extraction of CODEX from the trunk, stow of SPDM, SSRMS walkoff to the Mobile Remote Servicer (MRS) Base System (MBS), SSRMS grapple of SPDM, translate of the Mobile Transporter (MT), and install of CODEX to Express Logistics Carrier 3 (ELC-3) Site 3. There is no disposal payload for this flight.

ISS↗
NASA NTRSDate not supplied

In-Depth Modeling and Simulation Analysis of Artemis Missions Using the Impact Probabilistic Risk Assessment Tool

BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. During an extended simulation modeling phase, IMPACT generated analytical results for medical risks, and the medical resources and capabilities to address those risks, for every segment of every crewed Artemis mission. This presentation will highlight the reliability, consistency and validity of IMPACT’s computational modeling techniques and will showcase the library of analytical outcomes generated for the Artemis missions. OVERVIEW During the early stages of IMPACT’s design, architecture and technical requirements collection, “scenarios” (use cases) - achievement goals required for acceptance testing, were identified by stakeholders. IMPACT successfully completed the scenario testing requirements and undertook an extensive operational run phase utilizing a wide range of input combinations with a goal of delivering a cohesive, trustworthy, reliable, vast, and diverse body of evidence. The intent of these modeling runs was to validate consistency in output, ensure solidity of executable operations and to streamline processes by identifying areas requiring efficiency improvements. Using the many missions of Artemis, IMPACT ran variations of operational runs to assess the output for acceptable, as well as unusual characteristics. This rigorous long-term “shakedown” analysis was implemented to help build a collective body of evidence to aid in securing a high level of confidence, reliability, and validity in the output, whether from the applicational components of IMPACT, or the entirety of the operational process. ANTICIPATED ANALYSIS AND CONCLUSION This presentation will discuss the various categories of input criteria; the comparisons in the application of these input criteria to various Artemis missions; the preparation and collection of the body of evidence, and reliability of the computational modeling techniques. This paper serves as an initial analytical overview of IMPACT’s probabilistic risk assessment (PRA) medical risk outputs covering Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.

Crew Composition↗
NASA NTRSDate not supplied

Introduction to Quantum Computing

Quantum computing offers the potential to revolutionize high-performance computing by providing a means to solve certain computational problems asymptotically faster than any classical computer. Quantum computing has advanced recently from merely a theoretical possibility to engineered reality, including commercial entities offering early prototype quantum processors, both special-purpose quantum annealers and general-purpose gate-model processors. The media have been showcasing each new development and implicitly conveying the message that quantum-computing ubiquity is nigh. Here, we will respond to this hype and provide an overview of the exciting but still early state of the field. In this tutorial, we introduce participants to the computational models that give quantum computing its immense computational power. We examine the thought processes that programmers need to map problems to quantum computers. And we discuss hardware and algorithmic challenges that must be overcome before quantum computing becomes a component of every software developer's repertoire. (Update of 2022 slides)

Quantum computing↗
NASA NTRSDate not supplied

Twenty-Five Years of Background-Oriented Schlieren: Advances and Novel Applications

Since its introduction in the year 2000, background-oriented schlieren (BOS) has become acornerstone technique for visualizing variable-density flows. In this review, we provide a rigorousexamination of the optical principles underpinning BOS and related refractive-index-basedtechniques, complemented by an appendix linking schlieren imaging to Maxwell’s equations.The core sections delve into the practical aspects of BOS, with detailed discussions on imageprocessing algorithms and critical considerations for experimental setups. We then explorerecent advancements and innovations, including extensions of BOS with tomography, dataassimilation, and event-based imaging. Finally, we present notable applications of BOS inchallenging and unconventional environments, showcasing the method’s versatility and offerinspiration for future research directions.

Background-Oriented Schlieren↗
NASA NTRSDate not supplied

A Knowledge Graph Framework for Organizing Heterogeneous Datasets for Utilization in Classical and Quantum Computing: Current Challenges and Future Directions

"The escalating impact of climate change induced extreme weather events in urban, suburban, and rural environments demands a rethink of how we have been using the single event-based or use-case-based knowledge graph models. The lack of representation in interaction within environmental variables found in literature led to the development of a novel framework that reflects the true nature of the interconnectedness in our environment. We propose an Environmental Interaction Knowledge Graph (EIKG) framework. This general EIKG framework works as the basis for interconnected environmental events by knitting interrelated events such as hurricanes leading to storm surges, which lead to flood events that could cause mudslides, landslides, etc., The cascading nature of one event leading to another related event in the environment requires an adequate understanding of each event using contextual information before conducting any data-driven analytics. This vision paper showcases how the EIKG:floods, EIKG:wildfire EIKG:landslides, etc, can be derived from a base case framework of EIKG as those individual events are interconnected with some common denominator variables. As an example, the precipitation variable is used in the flood case study as well as in the wildfire case study, as excessive precipitation levels lead to floods, and lack of precipitation leads to droughts and wildfires. We identify the precipitation variable as a “common-denominator-variable” in extreme weather events that play a key role in modeling the environment leading to different extreme weather events based on the variability of that variable (varying values where low precipitation leads to drought, and high values lead to floods). We use the insights gained from EIKG to conduct classical and Quantum Machine Learning (QML) based data analysis on the research questions developed. Our preliminary study shows how the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC) are used along with the classical machine learning models to compare the model accuracies. Our study elaborates on how a quantitative analysis uses state-of-the-art machine learning techniques that include implementing both classical and quantum machine learning models and developing the knowledge graph. The EIKG is used to organize heterogeneous datasets and integrate the relations to case-specific extreme weather events such as floods. The study uses datasets such as county-to-country residential mobility data, socioeconomic datasets from the US Census Bureau, climate and weather-related Earth Observational data from NASA, and critical infrastructure data from the Homeland Infrastructure datasets."

Knowledge Graphs, Quantum Computing, Heterogenous ↗
NASA NTRSDate not supplied

NASA Giovanni: Analyze, Compare, and Visualize 2000+ Earth Satellite and Model Variables Without Downloading Data and Software

Over vast oceans and remote continents, observations are often scarce and discontinuous. Satellite and model data play a critical role in research and applications. However, finding and accessing satellite and model data can be a daunting task for many, especially those outside the community. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC), one of 12 NASA Science Mission Directorate Data Centers, provides Earth science data, information, and services to everyone such as researchers, application users, educators, and students. GES DISC archives and supports datasets applicable to several NASA Earth Science Focus Areas including Atmospheric Composition, Water & Energy Cycles, Carbon Cycle & Ecosystem, and Climate Variability. To facilitate data discovery, evaluation, and exploration, GES DISC has developed the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni), an online tool to analyze and visualize NASA remote sensing and model data without downloading data and software. As of this writing, over 2000 Earth satellite and model variables are available in Giovanni, including several wellknown NASA satellite missions (e.g., TRMM, GPM) and projects (e.g., MERRA-2, GPCP). Giovanni provides twenty-two plots that can be used to analyze, compare, and explore Earth data across different disciplines. Results can be shared with colleagues and downloaded for further analysis. Over the years, Giovanni has helped publish over 3000 referral papers. In this presentation, we will showcase key variables and plot types in Giovanni with examples. In particular, we will present several popular precipitation products from GPM and CPCP for evaluation and comparison.

data analysis↗
NASA NTRSDate not supplied

Efficient Access to MERRA-2 Reanalysis Data from the Cloud Archives

The Modern-Era Retrospective analysis for Research and Applications, Version (MERRA-2) dataset is now available in the AWS West Region-2 cloud. The free data downloading policy remains unchanged. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) offers cloud services such as OPeNDP subsetting, and time series services. This presentation will showcase use cases that testing the procedures and find the most efficiency workflows to access and process the data for finding climate extremes. The use cases include a) accessing and processing data directly in the cloud S3 bucket without downloading it, and b) downloading subset data to process on the user’s local system.

data access, cloud computing, climate extremes↗
NASA NTRSDate not supplied

Kaona: Deep Searching and Curating Data from Aviation Safety Reporting Systems

Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given to augmenting existing processes for mining these safety reporting system databases. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety database mining activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using publicly available NASA’s ASRS narratives and another using publicly available C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.

ASRS↗
NASA NTRSDate not supplied

Kaona: Deep Searching and Curating Aviation Safety Reporting Systems

Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given in augmenting existing processes of safety reporting systems. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety reporting systems activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using all public NASA's ASRS narratives and another using all public C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.

asrs↗
NASA NTRSDate not supplied

Enhancing accessibility and usability of Algorithm Theoretical Basis Documents through the Algorithm Publication Tool

Effective communication of scientific theories is crucial for transforming raw instrument data into valuable Earth observation products. The NASA Earth science data community disseminates this knowledge through Algorithm Theoretical Basis Documents (ATBDs). Historically, these documents lacked a standardized format, were designed for human readability rather than machine interpretation, and were challenging to locate due to the absence of a centralized repository. The Algorithm Publication Tool (APT) transforms how ATBD content is presented, simplifying the processes of creating, updating, and locating these documents. APT offers authors the option to use its user-friendly cloud-based interface or standardized templates for ATBD development. The primary advantage of the interface is its capability to manage the entire ATBD creation process within a single environment, ensuring comprehensive tracking of all activities and facilitating user tasks. Conversely, the use of standardized ATBD templates allows users to create documents using familiar tools like Google Docs, Microsoft Word, or Overleaf for LaTeX. APT also provides a centralized repository, enabling easy search and discovery of published ATBDs. This presentation showcases APT's functionalities, illustrates its contributions to advancing open science, and highlights potential benefits for broader community adoption.

Bradley Baker↗
NASA NTRS2024

PRODEM: An Annual Series of Summer DEMs (2019 through 2022) of the Marginal Areas of the Greenland Ice Sheet

Surface topography across the marginal zone of the Greenland Ice Sheet is constantly evolving in response to changing weather, season, climate, and ice dynamics. However, current digital elevation models (DEMs) for the ice sheet are usually based on data from a multi-year period, thus obscuring these changes over time. Here we present four 500 m resolution summer DEMs (PRODEMs) of the Greenland Ice Sheet marginal zone for 2019 through 2022. The PRODEMs cover the marginal zone from the ice edge to 50 km inland, hence capturing all Greenland outlet glaciers. Each PRODEM is based on data fusion of CryoSat-2 radar altimetry and ICESat-2 laser altimetry using regionally varying kriging of elevation anomalies relative to ArcticDEM. The PRODEMs are validated using leave-one-out cross-validation, and PRODEM19 is further validated against an external data set, showcasing their ability to correctly represent surface elevations within the associated spatially varying prediction uncertainties. We observe a general lowering of surface elevations during the 4-year PRODEM period, but the spatial pattern of change is highly complex and with annual changes superimposed. The PRODEMs enable detailed studies of the marginal ice sheet elevation changes. With their high spatio-temporal resolution, the PRODEMs will be of value to a wide range of researchers and users studying ice sheet dynamics and monitoring how the ice sheet responds to changing environmental conditions. PRODEMs from summer 2019 through 2022 are available at https://doi.org/10.22008/FK2/52WWHG (Winstrup, 2024), and we plan to annually update the product henceforth.

Digital Elevation Models↗
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

Kaona: Deep Searching and Curating Safety Reporting Systems

Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given in augmenting existing processes of safety reporting systems. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety reporting systems activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using all public NASA's ASRS narratives and another using all public C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.

asrs↗
NASA NTRSDate not supplied

Overview of the NASA Terrestrial Environments Support Role: Data and Applications

This presentation focuses on the terrestrial environment component of the Natural Environments Branch’s (NEB’s) support role and highlights the instrumentation and data sources used, how the data are quality controlled and archived, and the analyses and use-cases of these datasets. A detailed overview is provided about some individual data sources that are used by the branch, which includes a network of instruments (e.g., wind towers, balloons, and profilers) and models, such as the Earth Global Reference Atmospheric Model (Earth-GRAM). Additionally, the data archival and quality control processes that maintain the high-quality climatological datasets from these sources are discussed, as are the implemented and pending updates to this process. Examples of the different applications for these datasets are presented to showcase the analytical and operational capabilities of the NEB and demonstrate the culmination of the entire process. This includes the Range Reference Atmospheres created and maintained by the NEB, sea-state analyses for crewed return, certification of observation systems for space flight, and the day-of-launch support process. The NEB at NASA Marshall Space Flight Center (MSFC) supports the NASA Engineering Directorate (ED) and Exploration Systems Development Mission Directorate (ESDMD) by providing the necessary environmental constraints to facilitate the safe design and operation of assets required for space travel. This includes, but is not limited to, the environmental considerations for assets residing at the launch sites, temporarily stationed at a launch site, traversing the [extra-]terrestrial atmosphere and open space, and arriving at their destination. As such, the Terrestrial and Planetary Environments Team within the NEB is but a part of the whole, delegated to quantify the environment that assets may encounter whilst on Earth and within its atmosphere. However, nearly all ESDMD assets are subjected to terrestrial conditions, which cover a diverse range of meteorological regimes and extremes. The team also supports day-of-launch operations for the Space Launch System (SLS), providing real-time monitoring of the winds and thermodynamics along the launch path using an amalgamation of observed, remotely sensed, and modeled output.

Systems Engineering↗
NASA NTRSDate not supplied

Considerations for High-Speed Background-Oriented Schlieren Visualization Capability for Ground Test Facilities

Since its introduction in the year 2000, background-oriented schlieren (BOS) has become a cornerstone technique for visualizing variable-density flows. In this review, we provide a rigorous examination of the optical principles underpinning BOS and related refractive-index-based techniques, complemented by an appendix linking schlieren imaging to Maxwell’s equations. The core sections delve into the practical aspects of BOS, with detailed discussions on image processing algorithms and critical considerations for experimental setups. We then explore recent advancements and innovations, including extensions of BOS with tomography, data assimilation, and event-based imaging. Finally, we present notable applications of BOS in challenging and unconventional environments, showcasing the method’s versatility and offer inspiration for future research directions.

Brett Bathel↗
NASA NTRSDate not supplied

Developing Natural Language Processing and Supervised Learning Techniques to Classify Mars Tasks

As NASA's Human Research Program (HRP) prepares for long-duration Mars missions, understanding astronaut tasks is crucial. This study, conducted at NASA Glenn Research Center (GRC), employed Natural Language Processing (NLP) and machine learning techniques to analyze and classify Mars tasks. A list of 1,058 Mars tasks was provided by HRP experts including binary labeling of 18 Human System Task Categories (HSTCs). We developed an NLP model using Google's BERT language model to capture the semantic and syntactic nuances of these tasks. Supervised training was initially applied to a subset of the NLP-analyzed tasks to assess the model's effectiveness in classifying the remaining tasks. Incorporating HSTC descriptions significantly enhanced the classification accuracy for 9 out of the 18 HSTCs and reduced training time. To address the issue of severe class imbalance in the HSTC data, we introduced innovative weighting and sampling techniques for data augmentation. We then fine-tune BERT to implement a pairwise relatedness scoring method, allowing us to cluster tasks based on their relatedness and similarity, getting a step closer to labeling the tasks without supervision. In this presentation we guide you through data preprocessing, deciphering key syntax components using BERT, and performing supervised classification of the Mars tasks. This work showcases the potential use of advanced NLP techniques to analyze Mars missions to be incorporated into various crew health and performance analyses.

GenAI↗