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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

pyQuARC: Open Source Library for Earth Observation Metadata Quality Assessment

Metadata quality is essential to effective data discovery and has become increasingly vital as more Earth Science data sets become available. The Common Metadata Repository (CMR) hosts metadata describing NASA’s Earth Observation data products, which are archived across 12 Distributed Active Archive Centers (DAACs). The Analysis and Review of CMR (ARC) Team, located at Marshall Space Flight Center, conducts metadata quality assessments to ensure that these data products are discoverable, accessible, and usable. To achieve these goals, the ARC team has developed a metadata quality assessment framework to evaluate metadata completeness, correctness, and consistency. ARC uses a combination of manual and automated methods to assess these three components and identify areas of improvement; the team then collaborates with the DAACs to resolve any findings. To streamline this process, ARC is currently developing a host of scripts, known as pyQuARC, to automate metadata quality assessments as much as possible. pyQuARC is an open source library for Earth Observation Metadata Quality Assessment, and the tool utilizes ARC’s metadata quality assessment framework to make basic validation checks, pinpoint inconsistencies between dataset-level (i.e. collection) and file-level (i.e. granule) metadata, and identify opportunities for more descriptive and robust information. Since pyQuARC is also customizable, other users can make modifications as needed, and future metadata standards can also be implemented. Once pyQuARC is fully developed, it will support multiple schema types to serve the broader EOSDIS metadata community. This presentation will provide an overview of pyQuARC and its process of development while showcasing the tool’s valuable features and uses.

Jenny Wood↗
NASA NTRSDate not supplied

Integrated Process-Structure-Property Simulations for Additive Manufacturing Using the Open-Source Materialite Package

The microstructure and properties of additively manufactured (AM) metals are strongly dependent on process conditions. Therefore, process-structure-property (PSP) simulations are a useful tool for exploring process parameter space, studying process variations, and quantifying uncertainty in material properties. However, integrating process-structure and structure-property simulations often involves connecting multiple software packages. Each package may use unique data structures and require substantial domain knowledge. This presentation demonstrates PSP simulation capabilities of Materialite, an open-source package developed at NASA Langley Research Center. Materialite simplifies model linkages by using a common data structure and model interface, enabling straightforward simulation across a PSP model chain. Physics-based models, including kinetic Monte Carlo and crystal plasticity, are implemented within the package. The model interface is also intended to simplify implementation of new models and enable integration with external simulation tools. Example use cases include uncertainty quantification with PSP models and GPU-accelerated powder bed fusion AM process models.

additive manufacturing↗
NASA NTRS2020

NASA Advanced Composites Project

The recently-completed NASA Advanced Composites Project (ACP) successfully developed several new technologies aimed at reducing the timeline to design and certify composite aircraft structures. Advancements in the areas of manufacturing simulation, nondestructive inspection and advanced structural design and analysis methodologies were completed in late 2019 under the Advanced Composites Consortium (ACC) public/private partnership as well as NRA-funded university contracts. In the area of Structures, the ACC made significant improvements in rapid design tools for preliminary sizing of structure, along with developing detailed static and fatigue strength prediction methods and methods for modeling high energy impact events in composite structures. These new methodologies and toolsets were validated against a range of representative test data using a comprehensive verification and validation approach which has been published and is now used extensively on other programs. Specific new tool improvements supported by ACC include NASA’s CompDam code and Floating Node Method code. CompDam is now available from NASA as open source code. The team alsoworked on improved impact modeling using LS-Dyna™ and improved delamination modeling using Abaqus™. Rapid design tool improvements are available through Hypersizer. The ACC team released well over 50 publications documenting these method improvements, and the ACP recently received the prestigious NASA Group Achievement Award for their work.Typical Hat Stiffened Panel Test Article Used for Method Validation

ACC↗
NASA NTRSDate not supplied

Turbo-Design: Open-Source Radial Equilibrium Turbomachinery Solver: Part I - Turbines

Advances in 3D Geometrical Designs and Cooling have played a significant role in improving the efficiency of turbomachinery. However, these advancements must be effectively translated back to the modeler. Machine learning can facilitate this transition. Specifically, machine learning–based loss models can bridge the gap between 3D and 1D designs, enabling modelers not only to predict velocity triangles but also to extract additional geometric features. Currently, the design tools used at NASA have not been updated to support such integration—until now. TurboDesign is an open-source, Python-based framework that replaces TD2 (LEW-11029-1) and AXOD2 (LEW-16323-1), both of which are radial equilibrium solvers for axial turbines. The goal of this update is to enable the integration of machine learning loss models into radial equilibrium equations. Additionally, TurboDesign is designed to support radial machines. This paper presents the governing equations, the assumptions underlying the code, the integration of legacy loss models, an example of machine learning model integration, and a validation comparison with CFD. All code, tutorials, and documentation are available at: https://www.github.com/nasa/turbo-design

Radial Equilibrium↗
NASA NTRS2014

Integrated System Modeling for Nuclear Thermal Propulsion (NTP)

Nuclear thermal propulsion (NTP) has long been identified as a key enabling technology for space exploration beyond LEO. From Wernher Von Braun's early concepts for crewed missions to the Moon and Mars to the current Mars Design Reference Architecture (DRA) 5.0 and recent lunar and asteroid mission studies, the high thrust and specific impulse of NTP opens up possibilities such as reusability that are just not feasible with competing approaches. Although NTP technology was proven in the Rover / NERVA projects in the early days of the space program, an integrated spacecraft using NTP has never been developed. Such a spacecraft presents a challenging multidisciplinary systems integration problem. The disciplines that must come together include not only nuclear propulsion and power, but also thermal management, power, structures, orbital dynamics, etc. Some of this integration logic was incorporated into a vehicle sizing code developed at NASA's Glenn Research Center (GRC) in the early 1990s called MOMMA, and later into an Excel-based tool called SIZER. Recently, a team at GRC has developed an open source framework for solving Multidisciplinary Design, Analysis and Optimization (MDAO) problems called OpenMDAO. A modeling approach is presented that builds on previous work in NTP vehicle sizing and mission analysis by making use of the OpenMDAO framework to enable modular and reconfigurable representations of various NTP vehicle configurations and mission scenarios. This approach is currently applied to vehicle sizing, but is extensible to optimization of vehicle and mission designs. The key features of the code will be discussed and examples of NTP transfer vehicles and candidate missions will be presented.

Systems Engineering↗
NASA NTRS2018

F Prime: An Open-Source Framework for Small-Scale Flight Software Systems

Developing flight software for small-scale missions such as CubeSats and SmallSats is challenging. These missions typically have ambitious goals, modest budgets, and tight schedules. To meet these challenges, a good flight software framework is essential. Frameworks can provide an architecture, infrastructure, tools, and reusable software components, all of which can help developers deliver their code on time and on budget. In this paper we present F Prime, a free, open-source flight software framework developed at JPL and tailored to small-scale systems such as CubeSats, SmallSats, and instruments. F Prime comprises several elements: (1) an architecture that decomposes flight software into discrete components with well-defined interfaces; (2) a C++ framework that provides core capabilities such as message queues and threads; (3) tools for specifying components and connections and automatically generating code; (4) a growing collection of ready-to-use components; and (5) tools for testing flight software at the unit and integration levels.We describe the F Prime framework and tools and present our experience using them. We describe several enhancements to the framework currently underway in the areas of software design, software verification, and ground data systems for testing.

Levison, Jeffrey W.↗
NASA NTRS2019

TPSAS-NF1676L-32493-DND

The Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has supported the Open Data Cube (ODC) initiative to provide a data architecture solution that has value to its global users and increases the impact of EO satellite data. ODC is an open-source platform for processing satellite data. We have developed software products and tools around the core ODC that would help users perform machine learning on EO satellite data. The recent United Nations (UN) Sustainable Development Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The CEOS SEO team has recently developed and released a set of innovative Jupyter notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. We have recently incorporated several machine learning techniques in these notebooks. In this paper, we present the lessons learned from our experience on classifying land using supervised and unsupervised machine learning techniques using ODC framework for UN SDGs. We identify the current limitations of ODC to seamlessly support machine learning techniques. We propose features that would help machine learning, specifically within the ODC framework. We propose a thematic indexing/loading of data for both unsupervised learning as well as data annotation/labeling pipeline. Currently, ODC supports machine learning by separating data-management from the analysis process. It works as a mechanism to load cubes of data. ODC does not natively support features that are vital in machine learning such as validation splits, fair/balanced sampling, establishing load size constraints, etc. We believe that our proposed features will empower users by providing features that bring machine learning techniques closed to ODC. Enhancements to ODC to better accommodate machine learning techniques can assist in fulfilling UN SDGs such as 6.3.2, 6.4.2, 6.6.1, 11.3.1, 14.1.1, 15.1.1, 15.3.1, and 15.4.2.

Syed R Rizvi↗
NASA NTRSDate not supplied

Tracking Magnetic Perturbations, dB/dt and Geomagnetic Indices for Geospace Storms on a Routine Basis at the CCMC

The first comprehensive assessment of geospace model skill to specify magnetic perturbations (delta-B) on the ground and their time derivative (dB/dt) was performed at the Community Coordinated Modeling Center starting in 2010. This study resulted in the addition of the Space Weather Modling Framework to the suite of operational models run by the NOAA Space Weather Prediction Center (SWPC). Since then, magnetic pertubations have been made available on a larger scale for Run-on-Request simulations in geospace at the CCMC. We will demonstrate recent additions to the suite of analysis tools and model results including magnetic perturbations at more stations and on a grid of positions from both, original model outputs (SWMF using preset run configurations) and post-processed calculations using CalcDeltaB and their analysis and visualization using the open-source Kamodo data access and analysis suite and the Comprehensive Assessment of Models and Events using Library tools (CAMEL) application.

Lutz Rastaetter↗
NASA NTRSDate not supplied

Development of an Extended Reality (XR) Tool for Earth Science Visualization

In this presentation, we will discuss our work in adapting the NASA open source XR software, the Mixed Reality Exploration Toolkit (MRET), to an earth science domain. MRET is a NASA open source XR software for rapidly building extended reality (XR) environments for NASA domain problems, e.g., pulling in CAD models of thermal vac chamber and Roman Space Telescope to do fit checks. Primarily used for hardware integration & test, we have been adapting and extending MRET for science problems. Traditionally, scientists view and analyze the result of calculated or measured observables with static 1-D, 2-D or 3-D plots. It can be difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. Additionally, numerical models, such as the NASA GEOS climate model, are almost exclusively formulated and analyzed on Eulerian grids with points fixed in space and time. However, atmospheric phenomena such as convective clouds, hurricanes and wildfire smoke plumes move with the 3-D flow field, and it is often difficult and unnatural to understand these phenomena in an Eulerian reference frame as opposed to the Lagrangian reference frame in which nature operates. As part of an Earth Science Technology Office (ESTO) proposal, we have been adapting MRET to be a scientific exploration and analysis XR tool with integrated Lagrangian Dynamics (LD) for the Goddard Earth Observing System (GEOS) numerical weather prediction model. We believe this will help scientists identify, track, and understand the evolution of Earth Science phenomena. This presentation will discuss current results in our work in developing this XR tool for Earth Science.

Thomas G Grubb↗
NASA NTRSDate not supplied

Development of an Extended Reality (XR) Tool for Earth Science Visualization

In this presentation, we will discuss our work in adapting the NASA open source XR software, the Mixed Reality Exploration Toolkit (MRET), to an earth science domain. MRET is a NASA open source XR software for rapidly building extended reality (XR) environments for NASA domain problems, e.g., pulling in CAD models of thermal vac chamber and Roman Space Telescope to do fit checks. Primarily used for hardware integration & test, we have been adapting and extending MRET for science problems. Traditionally, scientists view and analyze the result of calculated or measured observables with static 1-D, 2-D or 3-D plots. It can be difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. Additionally, numerical models, such as the NASA GEOS climate model, are almost exclusively formulated and analyzed on Eulerian grids with points fixed in space and time. However, atmospheric phenomena such as convective clouds, hurricanes and wildfire smoke plumes move with the 3-D flow field, and it is often difficult and unnatural to understand these phenomena in an Eulerian reference frame as opposed to the Lagrangian reference frame in which nature operates. As part of an Earth Science Technology Office (ESTO) proposal, we have been adapting MRET to be a scientific exploration and analysis XR tool with integrated Lagrangian Dynamics (LD) for the Goddard Earth Observing System (GEOS) numerical weather prediction model. We believe this will help scientists identify, track, and understand the evolution of Earth Science phenomena. This presentation will discuss current results in our work in developing this XR tool for Earth Science.

Thomas Grubb↗
NASA NTRSDate not supplied

pyQuARC: Preparing for Full Release

Metadata holds the contextual information about data and is the underlying structure for many data search portals. High quality metadata optimizes search results, allowing users to quickly retrieve the data they need. With the abundant volume and diversity of Earth observation datasets, data discovery and metadata quality are critical for end users. The Common Metadata Repository (CMR), for example, currently hosts metadata for over 9,000 Earth observation data products archived across 12 NASA Distributed Active Archive Centers (DAACs). The Analysis and Review of CMR (ARC) Team, located at Marshall Space Flight Center, assesses the completeness, correctness, and consistency of these metadata records to ensure they are accessible, usable, and discoverable. In 2021, ARC began developing pyQuARC, an open source library for Earth Observation Metadata Quality Assessment to automate this effort. The tool uses ARC’s existing metadata quality framework to provide prioritized recommendations for metadata improvement. During initial testing, pyQuARC automatically identified 58% of metadata findings when compared with a sample of manually reviewed records. Using the results from initial testing, this presentation will focus on recent advancements and improvements of the tool as the ARC team prepares for pyQuARC’s full release. It will also demonstrate pyQuARC's enrichment value, not only for the ARC team, but the broader EOSDIS metadata community as well.

Essence Raphael↗
NASA NTRS2024

Rhode Island Ecological Conservation: Methods for Monitoring Rhode Island Habitats: Contributing to a Framework for Targeted Conservation and Management

Global avian population decline since the 1970s is largely attributable to habitat loss and degradation from anthropogenic disturbances. NASA DEVELOP’s Rhode Island Ecological Conservation team partnered with the Audubon Society of Rhode Island to compute land use land cover (LULC) maps of Rhode Island to aid in the conservation of the state’s 140 bird species. This project aimed to support the partner’s land acquisition strategies with updated and specific LULC classifications showing potential bird-habitat locations across the state. We incorporated remotely sensed data from Landsat 8 and 9 Operational Land Imager (OLI) into LULC maps using unsupervised classification techniques in ArcGIS Pro and supervised classification in Google Earth Engine. We generated six land classifications for 2023, which showed land cover dominated by upland habitats (forests, scrub/shrub, and grasslands), followed by development. We used TerrSet’s Land Change Modeler to forecast LULC change through 2043, using 2011 and 2021 National Land Cover Database (NLCD) land cover maps derived from Landsat 8 and 9 imagery. Project results suggest that non-urban upland and wetland habitats will decrease over time, while development will continue to encroach on non-urban avian habitats. Our maps and associated data will allow for more efficient land acquisition and management efforts to support avian habitat conservation across Rhode Island. Our study shows that data acquisition and processing from open data sources is feasible and further analysis can be done through GIS classification tools. More analysis is needed beyond this study to obtain more detailed land cover maps, though Audubon can aid its targeted conservation efforts with our current, historic, and forecasted LULC maps.

Remote sensing↗
NASA NTRS2009

Coastal Online Analysis and Synthesis Tool 2.0 (COAST)

The Coastal Online Assessment and Synthesis Tool (COAST) 3D geobrowser has been developed to integrate disparate coastal datasets from NASA and other sources into a desktop tool that provides new data visualization and analysis capabilities for coastal researchers, managers, and residents. It is built upon the widely used NASA-developed open source World Wind geobrowser from NASA Ames (Patrick Hogan et al.) .Net and C# version is used for development. It is leveraged off of World Wind community shared code samples and COAST 2.0 enhancement direction is based on Coastal science community feedback and needs assessment (GOMA). The main objective is to empower the user to bring more user-meaningful data into multi-layered, multi-temporal spatial context.

Brown, Richard B.↗
NASA NTRS2016

Using Docker Containers to Extend Reproducibility Architecture for the NASA Earth Exchange (NEX)

NASA Earth Exchange (NEX) is a data, supercomputing and knowledge collaboratory that houses NASA satellite, climate and ancillary data where a focused community can come together to address large-scale challenges in Earth sciences. As NEX has been growing into a petabyte-size platform for analysis, experiments and data production, it has been increasingly important to enable users to easily retrace their steps, identify what datasets were produced by which process chains, and give them ability to readily reproduce their results. This can be a tedious and difficult task even for a small project, but is almost impossible on large processing pipelines. We have developed an initial reproducibility and knowledge capture solution for the NEX, however, if users want to move the code to another system, whether it is their home institution cluster, laptop or the cloud, they have to find, build and install all the required dependencies that would run their code. This can be a very tedious and tricky process and is a big impediment to moving code to data and reproducibility outside the original system. The NEX team has tried to assist users who wanted to move their code into OpenNEX on Amazon cloud by creating custom virtual machines with all the software and dependencies installed, but this, while solving some of the issues, creates a new bottleneck that requires the NEX team to be involved with any new request, updates to virtual machines and general maintenance support. In this presentation, we will describe a solution that integrates NEX and Docker to bridge the gap in code-to-data migration. The core of the solution is saemi-automatic conversion of science codes, tools and services that are already tracked and described in the NEX provenance system, to Docker - an open-source Linux container software. Docker is available on most computer platforms, easy to install and capable of seamlessly creating and/or executing any application packaged in the appropriate format. We believe this is an important step towards seamless process deployment in heterogeneous environments that will enhance community access to NASA data and tools in a scalable way, promote software reuse, and improve reproducibility of scientific results.

earth exchange↗
NASA NTRS2018

Restructuring Big Data to Improve Data Access and Performance in Analytic Services Making Research More Efficient for the Study of Extreme Weather Events and Application User Communities

By developing and enhancing various services and tools, the GES DISC provides users with the capability to access and visualize data, and to make comparisons of data from multiple sensor and models via a number of cross-discipline projects. Discovering Data via Faceted Web Interface Web interface to data products and services Search and Download mechanisms Dataset Landing Pages Accessing Data through Interoperable Services: GDS – GrADS Data Server OPeNDAP - Open-source Project for a Network Data Access Protocol WMS – OGC service GIS connector – allowing IS tools to access data easier (coming soon) HTTPS -- direct online access Downloading Data Basics: Subset and egridding Service – Parameter, Spatial, Time, Vertical, Mean averaging, format conversion, and regridding for L3/L4 gridded data Swath Data Subsetter – Parameter, spatial subset of L2 /L1 data. Visualizing Data Online: Giovanni –Visualization and Analysis L3/L4 gridded data AIRS NRT Viewer – AIRS near-real-time DQVis – L2 data quality visualization

data cube↗
NASA NTRS2012

World Wind Tools Reveal Environmental Change

Originally developed under NASA's Learning Technologies program as a tool to engage and inspire students, World Wind software was released under the NASA Open Source Agreement license. Honolulu, Hawaii based Intelesense Technologies is one of the companies currently making use of the technology for environmental, public health, and other monitoring applications for nonprofit organizations and Government agencies. The company saved about $1 million in development costs by using the NASA software.

Source record↗
NASA NTRS2018

Earthdata User Interface (EUI) Library

As more remote sensing data moves to the cloud, the design tools we use to build user experiences and visualizations need to change and adapt to make the best use this new data reality. EUI 2.0, the next major iteration of the Earthdata User Interface Library, aims to make creating rich user experiences around NASA's Earth Observation System Data and Information System (EOSDIS) data easy and user-friendly. Building on a solid framework of design components, EUI 2.0 will be open source and have off-the-shelf integration with the Common Metadata Repository (CMR), basic mapping and visualization tools, a refreshed design toolkit for building apps and websites that fit the Earthdata design theme and guidelines. EUI 2.0 will allow for the rapid development of websites and applications based on EOSDIS tools and data holdings. It will also make use of cloud data availability to enable data visualization and analysis without the need to download and sync data.

Siarto, Jeff↗