SEARCH · Engineering Papers

Results for “analysis workflow”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

The NASA SRA Process as it Relates to Open-Source Workflows Developed for GeneLab Data Processing

To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.

Software Release Authorization↗

NASA NTRSDate not supplied

Implementing JEDI into NASA GMAO’s Real Time Production Suite

NASA’s Global Modeling and Assimilation Office (GMAO) has prepared their first production system involving the Joint Effort for Data assimilation Integration (JEDI) framework. In this system the central analysis, that drives the deterministic forecast, will be provided using JEDI. This talk outlines the phased approach to implementing JEDI into production that GMAO has designed, and how this approach will allow for a careful analysis of the system against the existing data assimilation framework (GSI). In the first phase of implementation the existing data assimilation system will perform certain actions that are still under development in JEDI. These include thinning the observations and producing satellite bias correction coefficients. JEDI is hooked up to the existing workflow so a single line switch can activate whether the existing or JEDI-based analysis is cycled. Outside of the monumental effort to construct JEDI that is ongoing at the Joint Center for Satellite Data Assimilation (JCSDA), GMAO have undertaken two areas of considerable effort. The talk will describe these efforts and highlight the main challenges that have been encountered. The first area of work is to implement the background error model from the existing data assimilation system into JEDI. The second is to validate the observing system in JEDI against the one in GSI, which has involved several new features being added to the observation operators in JEDI. While the longer-term plans involve trying to improve on the GSI in these two areas, GMAO is keen to have JEDI start from a trusted baseline. This is also key to implementing JEDI quickly so other priorities, such as increasing the number of model levels, can be easily worked on in parallel. GMAO is actively working on a framework to shepherd in the next generation coupled data assimilation system and model. As JEDI is implemented for the first time the plan is to ambitiously cycle through implementations, frequently bringing JEDI features to production. Details of these plans will be given in the talk and we will highlight key implementation and product milestones that we hope to achieve, as well as touch on the development environment that we will use to support frequent refreshing of the production system.

JEDI↗

NASA NTRSDate not supplied

Kamodo’s Satellite Constellation Mission Planning Tool

Kamodo provides a functional model-agnostic interface to a growing collection of Heliophysics model outputs. The CCMC, in collaboration with the Geospace Dynamics Constellation Science Team, has recently developed Kamodo’s satellite constellation mission planning tool to perform reconstructions in any pair of dimensions, including time. The ‘reconstruction’ tool enables users to fly any 4-dimensional grid of satellites through a given model data set, reconstructing what the given constellation would observe during the mission. This capability facilitates determination of what satellite configuration is best for a given science question, even allowing comparison across multiple models. This tool, written in Python, is built upon Kamodo’s flythrough tool, which in turn depends on a growing network of model-specific interfaces. Since each model interface is designed with model-agnostic syntax, the flythrough tool and the satellite constellation mission planning tool also feature model-agnostic syntax. In this work, we will describe the basic analysis choices available in the tool and provide a variety of sample workflows. The tool is freely available at https://github.com/nasa/Kamodo for the public. We invite the community to use the reconstruction tool and adapt the provided workflows for their mission planning, and to contribute their own workflows to share with others.

software↗

NASA NTRSDate not supplied

SM25C-2002: Kamodo’s Satellite Constellation Mission Planning Tool

python↗

NASA NTRSDate not supplied

Development of Computational Environmental Microbiome Workflows for the Laboratory and the International Space Station

Identification of microorganisms in the spaceflight environment is critical for crew health risk assessment on the International Space Station (ISS). Since 2017, nanopore sequencing technology has been used to support thein situ identification of microbial species during spaceflight. Beginning in 2018, a culture-independent, swab-to-sequencer method was implemented onboard the ISS to provide a more thorough insight of the ISS microbiome. Eliminating microbial culture enables identification of difficult-to-culture organisms, reduces risks associated with potentially pathogenic cultures, and could significantly reduce the time from sample-to-answer. However, this molecular-based approach generates large metagenomic datasets that require substantial computational resources for analysis. To process nanopore-generated sequencing data, the JSC Microbiology Laboratory established a bioinformatics workflow on Amazon EC2 under the security guidance of the NASA Science Managed Cloud Environment (SMCE).This resource allows for the development, testing, and accessing of computational tools for processing large and complex datasets. The work described here will address the downlinking of data from the ISS, the automated pipeline developed to identify targeted bacterial and fungal organisms, and the time from sampling onboard to microbial identification. The pipelines have been enhanced to address high and low biomass samples using optimization based on sample source (air, water, or surface) and type of collection (filter, colony, or swab).The resulting microbiome data can be assessed beyond microbial identifications to gain understanding toward population changes over time, potential selective environmental pressures, and evaluating correlations with a wide range of additional data sets. Metagenome analysis pipelines in development could allow for simultaneous identification of microbial species, gene function, and gene pathways present in the environment. Beyond the ground processing, the developed analysis pipeline is currently deployed onboard the ISS to allow for near real-time assessments of the ISS microbiome. This study serves as a critical foundation for exploration missions, where rapid microbiome analyses will be required.

G. Marie Sharp↗

NASA NTRS2011

Performance Analysis of Cloud Computing Architectures Using Discrete Event Simulation

Cloud computing offers the economic benefit of on-demand resource allocation to meet changing enterprise computing needs. However, the flexibility of cloud computing is disadvantaged when compared to traditional hosting in providing predictable application and service performance. Cloud computing relies on resource scheduling in a virtualized network-centric server environment, which makes static performance analysis infeasible. We developed a discrete event simulation model to evaluate the overall effectiveness of organizations in executing their workflow in traditional and cloud computing architectures. The two part model framework characterizes both the demand using a probability distribution for each type of service request as well as enterprise computing resource constraints. Our simulations provide quantitative analysis to design and provision computing architectures that maximize overall mission effectiveness. We share our analysis of key resource constraints in cloud computing architectures and findings on the appropriateness of cloud computing in various applications.

Stocker, John C.↗

NASA NTRS2010

Observing System Simulation Experiment (OSSE) for the HyspIRI Spectrometer Mission

The OSSE software provides an integrated end-to-end environment to simulate an Earth observing system by iteratively running a distributed modeling workflow based on the HyspIRI Mission, including atmospheric radiative transfer, surface albedo effects, detection, and retrieval for agile exploration of the mission design space. The software enables an Observing System Simulation Experiment (OSSE) and can be used for design trade space exploration of science return for proposed instruments by modeling the whole ground truth, sensing, and retrieval chain and to assess retrieval accuracy for a particular instrument and algorithm design. The OSSE in fra struc ture is extensible to future National Research Council (NRC) Decadal Survey concept missions where integrated modeling can improve the fidelity of coupled science and engineering analyses for systematic analysis and science return studies. This software has a distributed architecture that gives it a distinct advantage over other similar efforts. The workflow modeling components are typically legacy computer programs implemented in a variety of programming languages, including MATLAB, Excel, and FORTRAN. Integration of these diverse components is difficult and time-consuming. In order to hide this complexity, each modeling component is wrapped as a Web Service, and each component is able to pass analysis parameterizations, such as reflectance or radiance spectra, on to the next component downstream in the service workflow chain. In this way, the interface to each modeling component becomes uniform and the entire end-to-end workflow can be run using any existing or custom workflow processing engine. The architecture lets users extend workflows as new modeling components become available, chain together the components using any existing or custom workflow processing engine, and distribute them across any Internet-accessible Web Service endpoints. The workflow components can be hosted on any Internet-accessible machine. This has the advantages that the computations can be distributed to make best use of the available computing resources, and each workflow component can be hosted and maintained by their respective domain experts.

Turmon, Michael J.↗

NASA NTRSDate not supplied

Science Workflows using Kamodo

Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.

software↗

NASA NTRSDate not supplied

Science Workflows using Kamodo

python↗

NASA NTRS2023

Southern Idaho Ecological Conservation: Investigating the Impact of Targeted Grazing to Improve Wetland Habitat in the Sterling Wildlife Management Area

Wetland ecosystems are vital for biodiversity conservation and ecosystem services. The Sterling Wildlife Management Area in Bingham County, Idaho, has management concerns about decadent and accumulated vegetation growth encroaching on wetland habitat, which presents challenges for wildlife, decreases biodiversity, and limits public access. Targeted grazing has been proposed as a sustainable alternative to chemical herbicides or burning. Land managers introduced targeted cattle grazing in January 2021 to reduce biomass. NASA DEVELOP partnered with the Idaho Department of Fish and Game to determine the impact of grazing using NASA Earth observations from Landsat 8 Operational Land Imager (OLI) in Google Earth Engine (GEE). Images were processed with TerrSet’s Land Change Modeler and ArcGIS Pro’s Change Detection Wizard to understand land changes following grazing. A Normalized Difference Vegetation Index (NDVI) analysis was performed to assess impacts on vegetation productivity and compare variance in biomass before and after grazing. A Normalized Difference Water Index (NDWI) was used to compare changes in the wetland and its vegetation content to evaluate the suitability of the area for migratory birds post-grazing. Results showed a decrease in the vegetation index and an increase in the water index postgrazing. The DEVELOP team’s analysis suggests that grazing helps break down thick, senesced vegetation and increase soil moisture. Providing a workflow model will aid partners in continuing to monitor this management area and other management areas across the state.

change detection↗

NASA NTRS2007

Using Selection Pressure as an Asset to Develop Reusable, Adaptable Software Systems

The Goddard Earth Sciences Data and Information Services Center (GES DISC) at NASA has over the years developed and honed several reusable architectural components for supporting large-scale data centers with a large customer base. These include a processing system (S4PM) and an archive system (S4PA) based upon a workflow engine called the Simple Scalable Script based Science Processor (S4P) and an online data visualization and analysis system (Giovanni). These subsystems are currently reused internally in a variety of combinations to implement customized data management on behalf of instrument science teams and other science investigators. Some of these subsystems (S4P and S4PM) have also been reused by other data centers for operational science processing. Our experience has been that development and utilization of robust interoperable and reusable software systems can actually flourish in environments defined by heterogeneous commodity hardware systems the emphasis on value-added customer service and the continual goal for achieving higher cost efficiencies. The repeated internal reuse that is fostered by such an environment encourages and even forces changes to the software that make it more reusable and adaptable. Allowing and even encouraging such selective pressures to software development has been a key factor In the success of S4P and S4PM which are now available to the open source community under the NASA Open source Agreement

Berrick, Stephen↗

NASA NTRS2013

An Upgrade of the Aeroheating Software "MINIVER"

Many software packages assist engineers with performing flight vehicle analysis, but some of these packages have gone many years without updates or significant improvements to their workflows. One such software, known as MINIVER, is a powerful yet lightweight tool that is used for aeroheating analyses. However, it is an aging program that has not seen major improvements within the past decade. As part of a collaborative effort with Florida Institute of Technology, MINIVER has received a major user interface overhaul, a change in program language, and will be continually receiving updates to improve its capabilities. The user interface update includes a migration from a command-line interface to that of a graphical user interface supported in the Windows operating system. The organizational structure of the preprocessor has been transformed to clearly defined categories to provide ease of data entry. Helpful tools have been incorporated, including the ability to copy sections of cases as well as a generalized importer which aids in bulk data entry. A visual trajectory editor has been included, as well as a CAD Editor which allows the user to input simplified geometries in order to generate MINIVER cases in bulk. To demonstrate its continued effectiveness, a case involving the JAXA OREX flight vehicle will be included, providing comparisons to captured flight data as well as other computational solutions. The most recent upgrade effort incorporated the use of the CAD Editor, and current efforts are investigating methods to link MINIVER projects with SINDA/Fluint and Thermal Desktop.

Louderback, Pierce M.↗

NASA NTRS2013

A Upgrade of the Aeroheating Software "MINIVER"

Many software packages assist engineers with performing flight vehicle analysis, but some of these packages have gone many years without updates or significant improvements to their workflows. One such software package, known as MINIVER, is a powerful yet lightweight tool used for aeroheating analyses. However, it is an aging program that has not seen major improvements within the past decade. As part of a collaborative effort with the Florida Institute of Technology, MINIVER has received a major user interface overhaul, a change in program language, and will be continually receiving updates to improve its capabilities. The user interface update includes a migration from a command-line interface to that of a graphical user interface supported in the Windows operating system. The organizational structure of the pre-processor has been transformed to clearly defined categories to provide ease of data entry. Helpful tools have been incorporated, including the ability to copy sections of cases as well as a generalized importer which aids in bulk data entry. A visual trajectory editor has been included, as well as a CAD Editor which allows the user to input simplified geometries in order to generate MINIVER cases in bulk. To demonstrate its continued effectiveness, a case involving the JAXA OREX flight vehicle will be included, providing comparisons to captured flight data as well as other computational solutions. The most recent upgrade effort incorporated the use of the CAD Editor, and current efforts are investigating methods to link MINIVER projects with SINDA/Fluint and Thermal Desktop.

Louderback, Pierce↗

NASA NTRSDate not supplied

AI-Enhanced Computational Tools for Entry Systems Modeling

To advance the understanding of complex atmospheric entry phenomena, NASA’s Entry Systems Modeling (ESM) team [1] has developed high-fidelity computational tools addressing multiscale challenges, from material microstructures to full-scale heatshield response. This abstract highlights a subset of ESM tools, focusing on AI integration to enhance workflows and predictive modeling. - PuMA [2] computes effective material properties from high-resolution micro-CT scans, supporting TPS analysis for NASA missions. - TomoSAM [3] automates 3D tomography dataset segmentation for PuMA using the Segment Anything Model, reducing manual effort and improving accuracy. - PATO [4] models porous reactive materials under extreme conditions, with advancements such as unified solvers, mechanical erosion, and TPS coatings for NASA missions. - arcjetCV [5] employs deep learning to analyze arc jet test footage, measuring recession rates, shape changes, and shock standoff distances, bridging simulations, and experiments to reveal TPS ablation behavior. - ARCHeS [6] simulates arc heater plasma flows, modeling turbulence, radiation, and electromagnetic interactions to optimize arc heater performance, validate TPS under extreme conditions, and serve as a foundation for developing digital twins of arc heater facilities. - SPARTA [7] simulates rarefied hypersonic flows and gas-surface interactions for planetary entry missions, leveraging GPU architectures for scalable and efficient aerothermal and ablation analyses. AI-driven solutions, such as deep learning segmentation, have streamlined workflows in ESM tools and still hold significant potential to further accelerate processes and enhance automation in entry systems modeling. [1] Haskins, J.B. (2023), [2] Ferguson, J.C. (2018), [3] Meurisse, J.B.E. (2018), [4] Semeraro, F. (2023), [5] Quintart, A. (2024) [6] Meurisse, J.B.E. (2022), [7] Plimpton, S.J. (2019)

Predictive Modeling↗

NASA NTRS2026

Investigation of Bipropellant Plume-Induced Contamination Effects on Coverglass Materials

Contamination and degradation of external spacecraft materials by unburned and partially combusted species from bipropellant thruster plumes has long been observed as a key component of the induced space environment. Space shuttle flight experiments and returned flight hardware from the International Space Station (ISS) have both experienced microscopic impact features induced by high-velocity thruster plume droplets. Analytical results have shown that droplet impingement angle relative to a receiving surface plays a key role in the surface damage. Although impacts with normal impingement angles contribute more severely to surface degradation than highly oblique angles, surface effects at higher impingement angles should not be dismissed. Thruster plume-induced materials degradation is a complex phenomenon that depends on a variety of parameters, including but not limited to material type, system temperature and pressure, plume composition, and thruster firing specifications such as number of pulses, pulse duration, and sample distance from the thruster. For space applications, attaining the vacuum pressure and temperature conditions necessary for flight-like plume expansion and exposure conditions is not a trivial task. The German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) is a facility uniquely capable of simulating such conditions. Test coupons were exposed to bipropellant thruster firings under high vacuum at the DLR facility. Percent area coverage (PAC) and droplet size distributions were evaluated for the uncoated and coated solar array coverglass materials over a range of impingement angles (0̊ to 75̊). A post-test imaging workflow was developed that aimed to quantify changes in sample surface morphology obtained from scanning electron microscopy (SEM) images using the Image Processing and Analysis in Java (ImageJ) tool; an opensource image processing software. The goal was to create a framework through which to evaluate the effect of bipropellant-induced PAC and droplet size distribution on solar array coverglass optical transmission losses. Understanding this relationship is important because optical transmission losses are known to lead to current reduction in solar power generation systems. In addition to the development of surface characterization workflows, valuable lessons learned as they pertain to future investigations and experiments will be discussed. The authors hope that sharing these lessons will facilitate more utilization of DLR’s unique capabilities as well as open the conversation for how best to address experimental characterization of flight-like plume expansion and its impacts on materials surface degradation effects.

Gateway↗

NASA NTRSDate not supplied

Entwine Point Tiles for 3D Visualization and Querying of ICESat-2

Point Cloud data from non-optical sensors present challenges in scientific computing in both volume of data and files, even for cloud services environments. As part of the Multi-Mission Algorithm and Analysis Platform (MAAP), a joint open science platform for global biomass modelling, we’ve developed a cloud optimized workflow for using ATL08 (ICESat-2) data as a point cloud. For MAAP, the ATL08 data product is published as Entwine Point Tiles (EPT), allowing users to visualize and query the full extent of this collection interactively without pre-downloading, or preprocessing. The EPT format is a cloud-optimized point cloud data format which re-organizes points into a cloud friendly spatially indexed data structure. MAAP uses AWS S3 to store these point clouds and serves them over OGC specified APIs, 3DTiles for visualization, and WFS for querying. This workflow allows for interactive 3D visualizations in a web browser, including notebook environments and facilitates on the fly subsetting for interactive data exploration, all of which can be applied to other similar sensors.

Alex Mandel↗

NASA NTRSDate not supplied

Preliminary Results From GMAO’s JEDI Based Ocean/Sea-ice Data Assimilation System

Preparations for GMAO’s transition to next generation coupled DA framework includes MOM6 for the ocean and CICE6 for sea ice, alongside assembling the data assimilation systems. The coupled DA system will primarily rely on the Joint Effort for Data Assimilation Integration (JEDI) and involves the use of the Sea-ice Ocean and Coupled Analysis (SOCA) package. The early stages of the work involved developing a coupled, cycling (ocean-only) DA workflow named “Swell”. Some other highlights from this transdisciplinary work: Collaborated with NOAA/EMC to ensure direct comparisons using the same observations and time (July -September 2021) during model development. Conducted in-kind work with the JCSDA to improve various JEDI components. Swell coupled DA framework will be used to assimilate observations from NASA’s PACE and SWOT missions

Dorukhan Ardag↗

NASA NTRSDate not supplied

Developing a Vision for Maturing the Heliophysics Infrastructure towards Open Science: The DIARieS Analysis Ecosystem

In the dawn of open science and the upcoming requirements, we speak about the existing state of Heliophysics infrastructure and detail the evolution required to address capability or interconnection shortcomings. Such a daunting barrier calls for an analysis ecosystem with multi-faceted capability. We propose such an ecosystem, called DIARieS, to be built upon five conceptual pillars: Discovery, Implementation, Analysis, Reproducibility, and Sharing of results. The combination of these concepts in a single platform will enable users to more intuitively combine recent advances in technology to create ‘DIARieS’ of their workflows, which can be easily made open to others in the community. The DIARieS ecosystem will also increase our efficiency by streamlining our various workflow processes, including automatic incorporation of the impending requirements of open science. The various components of the ecosystem will simplify software installation and data implementation, including automatically generated citation lists based on the components included. Automatic containerization and version control of the ecosystem will make the custom workflows easily reproducible. Employing widget technology will ease the difficulty of producing publication and commercial quality visualizations and applying common analyses techniques. Incorporating multiple technologies will streamline the various sharing methods common in our work environments today. Overall, the totality of capabilities to be offered by this analysis ecosystem will drastically simplify the application of open science principles to our work in addition to improving our efficiency and ease of collaboration. This talk summarizes a vision of the proposed ecosystem, which is described in more detail in Ringuette et al. (2022: https://doi.org/10.1016/j.asr.2022.05.012).

infrastructure↗