Engineering Papers⌕ Search

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.

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

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Developing a Vision for Maturing the Heliophysics Infrastructure towards Open Science

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.

Infrastructure↗
NASA NTRSDate not supplied

A Workflow to Rapidly Interrogate Multiscale Model Simulation Results Across Multiple Length Scales

Many tools can be used to visualize field and state variables for a single scale analysis so that the influence of relevant mechanisms can be evaluated. Finite element software is often utilized to simulate a unit cell of a material and visualize results at that scale. Material properties can be homogenized from individual constituents and local deformation, damage, and failure mechanisms can be evaluated within the unit cell due to globally applied boundary conditions. Such solutions can produce satisfactory results if a user is only interested in analyzing a single scale. But materials in general contain features across multiple length scales, and assumptions must be made when attempting to account for lower length scale phenomena within a higher length scale model. Multiscale modeling is an attractive means to model materials because detailed material responses can be tracked across multiple disparate length scales while reducing the amount of required assumptions. However, as the complexity of these models increases, a large amount of data can be produced, and data traceability can become increasingly more difficult. Field and state variables, which are naturally dependent on spatial position, may themselves be calculated from one or more lower length scale unit cell models each with their own appropriate field and state variables. The NASA Multiscale Analysis Tool (NASMAT) is one software that can be used to perform a multiscale analysis efficiently and output requested data at all length scales in the analysis. A companion open-source Python software, NASMAT PrePost, can be used to visualize NASMAT model results and rapidly interrogate multiscale data across multiple length scales. This presentation will demonstrate some of the key features of NASMAT PrePost on two multiscale problems by quickly displaying and demonstrating connectivity among multiscale results from large datasets.

Python↗
NASA NTRS2012

Preliminary Evaluation of MapReduce for High-Performance Climate Data Analysis

MapReduce is an approach to high-performance analytics that may be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. We are particularly interested in the potential of MapReduce to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we are prototyping a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. Our initial focus has been on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. Preliminary results suggest this approach can improve efficiencies within data intensive analytic workflows.

Duffy, Daniel Q.↗
NASA NTRS2018

Analysis of Impact of RTC Errors on CTOP Performance

Adoption of CTOPs in airspace has been hampered by a lack of willingness of a majority of airlines to participate in CTOP as there is significant cost in changing workflows and upgrading technology. No commercial vendors are currently offering TOS generation capability. Simpler, cheaper TOS generators may have more errors in RTC specification as compared to sophisticated TOS generators. This study investigates the impact of RTC errors on CTOP performance.

CTOP↗
NASA NTRS2016

Information Management Workflow and Tools Enabling Multiscale Modeling Within ICME Paradigm

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Fortunately, material information management systems and physics-based multiscale modeling methods have kept pace with the growing user demands. Herein, recent efforts to establish workflow for and demonstrate a unique set of web application tools for linking NASA GRC's Integrated Computational Materials Engineering (ICME) Granta MI database schema and NASA GRC's Integrated multiscale Micromechanics Analysis Code (ImMAC) software toolset are presented. The goal is to enable seamless coupling between both test data and simulation data, which is captured and tracked automatically within Granta MI®, with full model pedigree information. These tools, and this type of linkage, are foundational to realizing the full potential of ICME, in which materials processing, microstructure, properties, and performance are coupled to enable application-driven design and optimization of materials and structures.

Materials Engineering↗
NASA NTRS2014

Coupled Solid Rocket Motor Ballistics and Trajectory Modeling for Higher Fidelity Launch Vehicle Design

Multi-stage launch vehicles with solid rocket motors (SRMs) face design optimization challenges, especially when the mission scope changes frequently. Significant performance benefits can be realized if the solid rocket motors are optimized to the changing requirements. While SRMs represent a fixed performance at launch, rapid design iterations enable flexibility at design time, yielding significant performance gains. The streamlining and integration of SRM design and analysis can be achieved with improved analysis tools. While powerful and versatile, the Solid Performance Program (SPP) is not conducive to rapid design iteration. Performing a design iteration with SPP and a trajectory solver is a labor intensive process. To enable a better workflow, SPP, the Program to Optimize Simulated Trajectories (POST), and the interfaces between them have been improved and automated, and a graphical user interface (GUI) has been developed. The GUI enables real-time visual feedback of grain and nozzle design inputs, enforces parameter dependencies, removes redundancies, and simplifies manipulation of SPP and POST's numerous options. Automating the analysis also simplifies batch analyses and trade studies. Finally, the GUI provides post-processing, visualization, and comparison of results. Wrapping legacy high-fidelity analysis codes with modern software provides the improved interface necessary to enable rapid coupled SRM ballistics and vehicle trajectory analysis. Low cost trade studies demonstrate the sensitivities of flight performance metrics to propulsion characteristics. Incorporating high fidelity analysis from SPP into vehicle design reduces performance margins and improves reliability. By flying an SRM designed with the same assumptions as the rest of the vehicle, accurate comparisons can be made between competing architectures. In summary, this flexible workflow is a critical component to designing a versatile launch vehicle model that can accommodate a volatile mission scope.

Ables, Brett↗
NASA NTRSDate not supplied

NASA GeneLab RNASeq Consensus Pipeline: A Nextflow Implementation

The NASA GeneLab project (genelab.nasa.gov) seeks to accelerate space biology research through cataloging and democratizing omics data. Since raw omics data is largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data has greater immediate value to a wide range of users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. Previously, GeneLab developed a standardized pipeline for processing RNAseq data, referred to as the ‘GeneLab RNAseq Consensus Pipeline (RCP)’, in collaboration with GeneLab’s Analysis Working Groups. The work presented here is a Nextflow implementation of GeneLab’s RCP that automates and accelerates data processing of RNASeq datasets hosted on GeneLab. In addition to the core data processing, the workflow also includes staging of GeneLab raw data and a robust verification and validation (V&V) program that runs after each processing step to identify errors in real-time, stop additional downstream computation, and preserve computational resources. The workflow, including the staging and V&V functionality, is open source for others to reuse and modify at https://github.com/nasa/GeneLab_Data_Processing/tree/master/RNAseq.

Jonathan Dejesus Oribello↗
NASA NTRSDate not supplied

NASA GeneLab RNASeq Consensus Pipeline: A Nextflow Implementation

The NASA GeneLab project (genelab.nasa.gov) seeks to accelerate space biology research through cataloging and democratizing omics data. Since raw omics data is largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data has greater immediate value to a wide range of users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. Previously, GeneLab developed a standardized pipeline for processing RNAseq data, referred to as the ‘GeneLab RNAseq Consensus Pipeline (RCP)’, in collaboration with GeneLab’s Analysis Working Groups. The work presented here is a Nextflow implementation of GeneLab’s RCP that automates and accelerates data processing of RNASeq datasets hosted on GeneLab. In addition to the core data processing, the workflow also includes staging of GeneLab raw data and a robust verification and validation (V&V) program that runs after each processing step to identify errors in real-time, stop additional downstream computation, and preserve computational resources. The workflow, including the staging and V&V functionality, is open source for others to reuse and modify at https://github.com/nasa/GeneLab_Data_Processing/tree/master/RNAseq.

Jonathan D Oribello↗
NASA NTRS2014

NASA Tech Briefs, February 2014

Topics include: JWST Integrated Simulation and Test (JIST) Core; Software for Non-Contact Measurement of an Individual's Heart Rate Using a Common Camera; Rapid Infrared Pixel Grating Response Testbed; Temperature Measurement and Stabilization in a Birefringent Whispering Gallery Resonator; JWST IV and V Simulation and Test (JIST) Solid State Recorder (SSR) Simulator; Development of a Precision Thermal Doubler for Deep Space; Improving Friction Stir Welds Using Laser Peening; Methodology of Evaluating Margins of Safety in Critical Brazed Joints; Interactive Inventory Monitoring; Sensor for Spatial Detection of Single-Event Effects in Semiconductor-Based Electronics; Reworked CCGA-624 Interconnect Package Reliability for Extreme Thermal Environments; Current-Controlled Output Driver for Directly Coupled Loads; Bulk Metallic Glasses and Matrix Composites as Spacecraft Shielding; Touch Temperature Coating for Electrical Equipment on Spacecraft; Li-Ion Electrolytes Containing Flame-Retardant Additives; Autonomous Robotic Manipulation (ARM); CARVE Log; Platform Perspective Toolkit; Convex Hull-Based Plume and Anomaly Detection; Pre-Filtration of GOSAT Data Using Only Level 1 Data and an Intelligent Filter to Remove Low Clouds; Affordability Comparison Tool - ACT; "Ascent - Commemorating Shuttle" for iPad; Cassini Mission App; Light-Weight Workflow Engine: A Server for Executing Generic Workflows; Model for System Engineering of the CheMin Instrument; Timeline Central Concepts; Parallel Particle Filter Toolkit; Particle Filter Simulation and Analysis Enabling Non-Traditional Navigation; Quasi-Terminator Orbits for Mapping Small Primitive Bodies; The Subgrid-Scale Scalar Variance Under Supercritical Pressure Conditions; Sliding Gait for ATHLETE Mobility; and Automated Generation of Adaptive Filter Using a Genetic Algorithm and Cyclic Rule Reduction.

Source record↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
NASA NTRS2020

Unstructured Grid Development for the Space Launch System Liftoff and Transition Lineloads Computational Analysis

Production of aerodynamic lineloads databases for the Space Launch System (SLS) vehicle at Liftoff and Transition (LOT) conditions has required the development of a Computational Fluid Dynamics (CFD) workflow capable of producing high-quality solutions for this unique phase of flight. Aerodynamic considerations included a wide range of flow angles (from 0°up to 90°total angle of attack), resulting leeside separation, and interaction effects between the three bodies of the integrated SLS vehicle, as well as the nearby launch tower. Computational mesh development for similar problems at the NASA Langley Research Center (such as for the Constellation/Ares launch vehicles) has primarily relied on in-house tools such as VGRID/POSTGRID, with grids designed for NASA-developed and maintained flow solvers such as USM3D and FUN3D. The workflow for such problems has evolved over the development of the various SLS configurations to incorporate new tools such as the Heldenpatch/Heldenmesh grid generator (Helden Aerospace) and CREATE-AV Kestrel (US Department of Defense) flow solver. This paper describes efforts to benchmark a grid generation approach for LOT problems using Heldenpatch/Heldenmesh and Kestrel, verified against prior best practices from VGRID/POSTGRID. Parameters studied include surface grid density, first-layer viscous cell height, and volume grid growth rate parameters. Resulting solutions are compared based on total force and moment values, sectional line loads, and surface pressures, all validated against existing wind tunnel aerodynamic databases where available for the SLS Block 1B Cargo configuration.

Space launch systems↗
NASA NTRS2023

Easy, Scalable Subsetting of GEDI Point Clouds

The GEDI Subsetter, a Python tool developed for NASA’s Multi-mission Algorithm and Analysis Platform (MAAP), optimizes the accessibility and visualization of GEDI point clouds by enabling users to efficiently subset data in a convenient, scalable manner. Complex science data often requires users to learn new software skills and handle many large files. Handling and cleaning large data sets is tedious and error-prone. These challenges significantly impede analysis. One of the goals of NASA's MAAP is to provide a platform that lowers the barrier to conducting research and analysis at scale. When a group of MAAP users wanted to conduct above-ground biomass estimation using GEDI data, we found that their existing workflow for leveraging GEDI data suffered from the barriers mentioned above. Furthermore, their workflow did not scale easily beyond a small number of granules. We found that existing tools related to GEDI data retrieval and subsetting were too limiting, so the GEDI Subsetter was written to support MAAP users’ needs. Being able to run many subsetting jobs simultaneously in the MAAP, and parallelizing the code itself, has led to significant speed improvements in obtaining relevant data, reducing subsetting time from hours to minutes. MAAP users can now more quickly and easily obtain only the data relevant to their research, by choosing which GEDI collection they want to work with (L1A, L2A, L2B, or L4A), and how they want to subset it, by specifying an area of interest, a temporal range, and relevant attributes. This has significantly reduced the feedback loop for users, allowing them to much more quickly subset GEDI data and begin their analysis. Although the GEDI Subsetter originally targeted users of the MAAP, it is generalized such that it can also be used outside of the MAAP and includes a command-line interface for convenience. Furthermore, with minor modifications, it should be possible to use it with non-GEDI data as the general pattern should be applicable to other sparse/track-based sensors.

Charles Daniels↗
NASA NTRSDate not supplied

Modeling NASA’s Procedural Requirement Processes - Implications for Digital Future

The National Aeronautics and Space Administration (NASA) has an ongoing Digital Transformation effort and to leverage and showcase the power of Digital Transformation, an effort is underway to develop an integrated, datacentric, model representing NASA’s key process requirements. The task was divided into three phases: As Is modeling, Analysis, and To Be Planning. As part of this effort, a team has completed the first Phase I of the modeling task and is nearing completion of the second phase. This effort will capture the key elements as requirements, responsibilities, allocations, roles, products, and associated lifecycle elements. The scope of modeling included NASA’s NPR 7120.5 (Project and Program Management), NPR 7123.1 (Systems Engineering) and NPRs 8705.2 (Risk classification for Robotic Missions) and 8705.4 (Human-Rating Requirements for Space Missions). This paper will summarize the approach, scope, parsing patterns applied, metamodel, and associated workflows for the As-Is modeling. It will also summarize the results and insights gleaned during that phase, including the review process. These insights have informed the analysis and will be discussed. The analysis modeling phase will also be summarized including how the stakeholders were engaged, how the common elements were handled and dispositioned, and will also describe some of the plans for the future of NASA NPDs and NPRs.

Systems Engineering↗
NASA NTRS2011

Automated X-ray and Optical Analysis of the Virtual Observatory and Grid Computing

We are developing a system to combine the Web Enabled Source Identification with X-Matching (WESIX) web service, which emphasizes source detection on optical images,with the XAssist program that automates the analysis of X-ray data. XAssist is continuously processing archival X-ray data in several pipelines. We have established a workflow in which FITS images and/or (in the case of X ray data) an X-ray field can be input to WESIX. Intelligent services return available data (if requested fields have been processed) or submit job requests to a queue to be performed asynchronously. These services will be available via web services (for non-interactive use by Virtual Observatory portals and applications) and through web applications (written in the Django web application framework). We are adding web services for specific XAssist functionality such as determining .the exposure and limiting flux for a given position on the sky and extracting spectra and images for a given region. We are improving the queuing system in XAssist to allow for "watch lists" to be specified by users, and when X-ray fields in a user's watch list become publicly available they will be automatically added to the queue. XAssist is being expanded to be used as a survey planning 1001 when coupled with simulation software, including functionality for NuStar, eRosita, IXO, and the Wide Field Xray Telescope (WFXT), as part of an end to end simulation/analysis system. We are also investigating the possibility of a dedicated iPhone/iPad app for querying pipeline data, requesting processing, and administrative job control.

Ptak, A.↗
NASA NTRS2013

PROcess Based Diagnostics PROBE

Many of the aspects of the climate system that are of the greatest interest (e.g., the sensitivity of the system to external forcings) are emergent properties that arise via the complex interplay between disparate processes. This is also true for climate models most diagnostics are not a function of an isolated portion of source code, but rather are affected by multiple components and procedures. Thus any model-observation mismatch is hard to attribute to any specific piece of code or imperfection in a specific model assumption. An alternative approach is to identify diagnostics that are more closely tied to specific processes -- implying that if a mismatch is found, it should be much easier to identify and address specific algorithmic choices that will improve the simulation. However, this approach requires looking at model output and observational data in a more sophisticated way than the more traditional production of monthly or annual mean quantities. The data must instead be filtered in time and space for examples of the specific process being targeted.We are developing a data analysis environment called PROcess-Based Explorer (PROBE) that seeks to enable efficient and systematic computation of process-based diagnostics on very large sets of data. In this environment, investigators can define arbitrarily complex filters and then seamlessly perform computations in parallel on the filtered output from their model. The same analysis can be performed on additional related data sets (e.g., reanalyses) thereby enabling routine comparisons between model and observational data. PROBE also incorporates workflow technology to automatically update computed diagnostics for subsequent executions of a model. In this presentation, we will discuss the design and current status of PROBE as well as share results from some preliminary use cases.

PROBE↗
NASA NTRSDate not supplied

Lunar Gateway Charging and Effects on Low-Energy Electron Measurements

The Electron Electrostatic Analyzer (EEA) is part of the HERMES instrument suite on Lunar Gateway and is designed to measure electrons in the energy range from 1 eV to 18 keV. Electrons in this range can be deflected or blocked from entering a detector by electric fields from a charged spacecraft platform, so many heliophysics missions (MMS, Cluster, etc.) use active potential control to minimize stray electric fields which would corrupt low-energy plasma measurements. Gateway, however, does not have such capabilities so the surface charging effects will influence the electron data. Further, some elements of Gateway partially block the instrument’s field of view to space. The effects of the Gateway platform on the low-energy electron population must be understood to maximize the accuracy of the EEA data. To investigate the electrostatic environment near Gateway and its effects on the EEA measurements, we use Nascap-2K to model the expected charging of Gateway under representative solar wind, magnetosheathic, and magnetospheric conditions. Next, we use the potential and electric field outputs from Nascap-2K along with in-house particle tracing codes to investigate how low-energy electrons are perturbed, blocked, or deflected by the spacecraft. We discuss how these perturbations impact the plasma moments computed from the EEA data and possible approaches for correcting the charging effects including application of advanced methods such as neural networks. In addition to electrons from the space environment being influenced by the electrostatic environment around Gateway, photoelectrons generated on Gateway surfaces will also be measured by EEA. We are investigating how knowledge of the potentials of various Gateway surfaces can be extracted from these measured photoelectrons using advanced numerical modeling and analysis. Such information would be valuable for validating charging models and planning mission operations around the platform. Finally, we discuss how the specific workflow developed for Gateway can be extended to a generalized process that is applicable to other future missions. As the space industry prepares for human exploration beyond low-Earth orbit, the need to obtain accurate space weather data from rideshare science payloads is critical. Our process for removing platform effects from the science data serves as a pathfinder for leveraging advanced simulations to maximize science returns and ensure the safety and success of future crewed missions.

Miles Bengtson↗
NASA NTRS2016

IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

visualization↗