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NASA NTRS2009

Multi-Mission Automated Task Invocation Subsystem

Multi-Mission Automated Task Invocation Subsystem (MATIS) is software that establishes a distributed data-processing framework for automated generation of instrument data products from a spacecraft mission. Each mission may set up a set of MATIS servers for processing its data products. MATIS embodies lessons learned in experience with prior instrument- data-product-generation software. MATIS is an event-driven workflow manager that interprets project-specific, user-defined rules for managing processes. It executes programs in response to specific events under specific conditions according to the rules. Because requirements of different missions are too diverse to be satisfied by one program, MATIS accommodates plug-in programs. MATIS is flexible in that users can control such processing parameters as how many pipelines to run and on which computing machines to run them. MATIS has a fail-safe capability. At each step, MATIS captures and retains pertinent information needed to complete the step and start the next step. In the event of a restart, this information is retrieved so that processing can be resumed appropriately. At this writing, it is planned to develop a graphical user interface (GUI) for monitoring and controlling a product generation engine in MATIS. The GUI would enable users to schedule multiple processes and manage the data products produced in the processes. Although MATIS was initially designed for instrument data product generation,

Cheng, Cecilia S.↗

NASA NTRS2019

Quantifying Water Storage Change and Land Subsidence Induced by Reservoir Impoundment Using GRACE, Landsat, and GPS Data

The construction of hydropower dams is a common strategy to support a country's increasing need for electricity and river water management for industry and agriculture. Although the hydrological and geophysical impacts of water relocation are usually assessed prior to impoundment, their accuracy is generally limited due to the lack of in situ observations, especially in a remote area. This study presents a workflow to quantify the terrestrial water storage change (TWS) and land subsidence induced by a reservoir's water impoundment using multiple satellite observations (GRACE, Landsat), land surface models (CABLE, GLDAS, NCEP, ECMWF), and GPS data. The study site is the Bakun Dam, located in Sarawak, Malaysia, which is the largest hydropower dam in Southeast Asia. Commencing operation in late 2010, the dam induced a change of water mass and lake surface area that was clearly observed by GRACE and Landsat observations, respectively. During the 17-month impounding period (from August 2010 to December 2011), GRACE observed a dramatic increase of approximately 200 mm equivalent water height, while Landsat detected an increased lake extent of around 600 km2. In this paper, a forward model is developed to determine the increased water surface level corresponding to GRACE observations, estimated to be about 120 m. In contrast to GRACE, the TWS derived from land surface models cannot capture the increased TWS, due to the lack of reservoir routing algorithms in the models. In addition, the land subsidence was calculated using the disk load model constructed based on the GRACE-derived lake level and Landsat-derived lake extent; the result is validated with the GPS data from BIN1 station, located at the western coast of Borneo. The commencement stage of the Bakun Dam induces the large-scale land subsidence, which causes the GPS-BIN1 station to subside by ~9 mm, and move toward the Bakun Lake by ~4 mm. Computation of the surface displacements directly from GRACE spherical harmonic coefficient data fails to capture the subsidence feature, mainly due to the truncation error. Overall, this study demonstrates that evaluating GRACE in conjunction with Landsat, LSMs, and GPS data allows the exploitation of the gravity signal at a much smaller spatial scale than its intrinsic resolution. Benefiting from global coverage, the newly developed satellite-based algorithm is a valuable tool for assessing the impacts of reservoir operation on hydrological and geophysical changes from local to regional scales.

Natthachet Tangdamrongsub↗

NASA NTRS2013

Virtual Construction of Space Habitats: Connecting Building Information Models (BIM) and SysML

Current trends in design, construction and management of complex projects make use of Building Information Models (BIM) connecting different types of data to geometrical models. This information model allow different types of analysis beyond pure graphical representations. Space habitats, regardless their size, are also complex systems that require the synchronization of many types of information and disciplines beyond mass, volume, power or other basic volumetric parameters. For this, the state-of-the-art model based systems engineering languages and processes - for instance SysML - represent a solid way to tackle this problem from a programmatic point of view. Nevertheless integrating this with a powerful geometrical architectural design tool with BIM capabilities could represent a change in the workflow and paradigm of space habitats design applicable to other aerospace complex systems. This paper shows some general findings and overall conclusions based on the ongoing research to create a design protocol and method that practically connects a systems engineering approach with a BIM architectural and engineering design as a complete Model Based Engineering approach. Therefore, one hypothetical example is created and followed during the design process. In order to make it possible this research also tackles the application of IFC categories and parameters in the aerospace field starting with the application upon the space habitats design as way to understand the information flow between disciplines and tools. By building virtual space habitats we can potentially improve in the near future the way more complex designs are developed from very little detail from concept to manufacturing.

space architecture↗

NASA NTRS2023

The SunPy Project: An Interoperable Ecosystem for Solar Data Analysis

The SunPy Project is a community of scientists and software developers creating an ecosystem of Python packages for solar physics. The project includes the sunpy core package as well as a set of affiliated packages. The sunpy core package provides general purpose tools to access data from different providers, read image and time series data, and transform between commonly used coordinate systems. Affiliated packages perform more specialized tasks that do not fall within the more general scope of the sunpy core package. In this article, we give a high-level overview of the SunPy Project, how it is broader than the sunpy core package, and how the project curates and fosters the affiliated package system. We demonstrate how components of the SunPy ecosystem, including sunpy and several affiliated packages, work together to enable multi-instrument data analysis workflows. We also describe members of the SunPy Project and how the project interacts with the wider solar physics and scientific Python communities. Finally, we discuss the future direction and priorities of the SunPy Project.

Solar physics↗

NASA NTRS2016

GC31G-1182: Opennex, a Private-Public Partnership in Support of the National Climate Assessment

The NASA Earth Exchange (NEX) is a collaborative computing platform that has been developed with the objective of bringing scientists together with the software tools, massive global datasets, and supercomputing resources necessary to accelerate research in Earth systems science and global change. NEX is funded as an enabling tool for sustaining the national climate assessment. Over the past five years, researchers have used the NEX platform and produced a number of data sets highly relevant to the National Climate Assessment. These include high-resolution climate projections using different downscaling techniques and trends in historical climate from satellite data. To enable a broader community in exploiting the above datasets, the NEX team partnered with public cloud providers to create the OpenNEX platform. OpenNEX provides ready access to NEX data holdings on a number of public cloud platforms along with pertinent analysis tools and workflows in the form of Machine Images and Docker Containers, lectures and tutorials by experts. We will showcase some of the applications of OpenNEX data and tools by the community on Amazon Web Services, Google Cloud and the NEX Sandbox.

datasets↗

NASA NTRS2005

An XML Representation for Crew Procedures

NASA ensures safe operation of complex systems through the use of formally-documented procedures, which encode the operational knowledge of the system as derived from system experts. Crew members use procedure documentation on the ground for training purposes and on-board space shuttle and space station to guide their activities. Investigators at JSC are developing a new representation for procedures that is content-based (as opposed to display-based). Instead of specifying how a procedure should look on the printed page, the content-based representation will identify the components of a procedure and (more importantly) how the components are related (e.g., how the activities within a procedure are sequenced; what resources need to be available for each activity). This approach will allow different sets of rules to be created for displaying procedures on a computer screen, on a hand-held personal digital assistant (PDA), verbally, or on a printed page, and will also allow intelligent reasoning processes to automatically interpret and use procedure definitions. During his NASA fellowship, Dr. Simpson examined how various industries represent procedures (also called business processes or workflows), in areas such as manufacturing, accounting, shipping, or customer service. A useful method for designing and evaluating workflow representation languages is by determining their ability to encode various workflow patterns, which depict abstract relationships between the components of a procedure removed from the context of a specific procedure or industry. Investigators have used this type of analysis to evaluate how well-suited existing workflow representation languages are for various industries based on the workflow patterns that commonly arise across industry-specific procedures. Based on this type of analysis, it is already clear that existing workflow representations capture discrete flow of control (i.e., when one activity should start and stop based on when other activities start and stop), but do not capture the flow of data, materials, resources or priorities. Existing workflow representation languages are also limited to representing sequences of discrete activities, and cannot encode procedures involving continuous flow of information or materials between activities.

Simpson, Richard C.↗

NASA NTRS2017

Application of Machine Learning to Rotorcraft Health Monitoring

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

machine learning↗

NASA NTRS2010

Anatomy of a Security Operations Center

Many agencies and corporations are either contemplating or in the process of building a cyber Security Operations Center (SOC). Those Agencies that have established SOCs are most likely working on major revisions or enhancements to existing capabilities. As principle developers of the NASA SOC; this Presenters' goals are to provide the GFIRST community with examples of some of the key building blocks of an Agency scale cyber Security Operations Center. This presentation viII include the inputs and outputs, the facilities or shell, as well as the internal components and the processes necessary to maintain the SOC's subsistence - in other words, the anatomy of a SOC. Details to be presented include the SOC architecture and its key components: Tier 1 Call Center, data entry, and incident triage; Tier 2 monitoring, incident handling and tracking; Tier 3 computer forensics, malware analysis, and reverse engineering; Incident Management System; Threat Management System; SOC Portal; Log Aggregation and Security Incident Management (SIM) systems; flow monitoring; IDS; etc. Specific processes and methodologies discussed include Incident States and associated Work Elements; the Incident Management Workflow Process; Cyber Threat Risk Assessment methodology; and Incident Taxonomy. The Evolution of the Cyber Security Operations Center viII be discussed; starting from reactive, to proactive, and finally to proactive. Finally, the resources necessary to establish an Agency scale SOC as well as the lessons learned in the process of standing up a SOC viII be presented.

Wang, John↗

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

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

Modern Scientific Data Governance Framework

Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies and stewardship best practices tend to be disconnected from operational data management practices and enforcement and mainly exist in well-meaning documents or reports. These governance policies are, at best, partially implemented and rarely monitored or audited. In addition, existing governance policies keep adding additional data management steps that require a human, ‘a data steward’, in the loop, and the cost of data management can no longer scale proportionately with the current and future increased data volume and complexity. The goal for developing an updated data governance framework is to modernize scientific data governance to the reality of Big data and align it with the current technology trends such as cloud computing and AI. The goals of this framework are two folds. One is to ensure thoroughness that the governance adequately covers the entire data life cycle. Two, provide a practical approach that offers a consistent and repeatable process for different projects. Three core principles ground this framework. First, focus on just enough governance and prevent data governance from becoming a roadblock toward the scientific process. Remove any unnecessary processes and steps. Second, automate data management steps where possible. Actively remove steps that require ‘human in the loop’ within the management process to be efficient and scale with increasing data. Third, all the processes should continually be optimized using quantified metrics to streamline the monitoring and auditing workflows.

Rahul Ramachandran↗

NASA NTRSDate not supplied

Material Property Estimation in Thin Battery Components Using Guided Wave Measurement, Experimental Dispersion Curve Extraction and Finite Element Modeling

At NASA, we are investigating nondestructive evaluation (NDE) and structural health monitoring (SHM) techniques to detect precursors of thermal runaway failure in lithium metal based lithium ion batteries (LIB). The approach is centered on computational simulation models to guide inspection and aid in interpretation of results. Since lithium metal LIBs have combinations of solid and fluid-filled porous materials, obtaining accurate material properties is both challenging and critical for successful simulation of battery inspection. To this end, we investigated a multi-verification approach for material characterization of thin battery components. First, a laser Doppler vibrometer (LDV) was used to measure guided wave fields in thin (microns-thick) battery components subject to broadband excitation. The time-space wavefield data was converted to frequency-wave number data to extract guided wave dispersion curves. A data visualization and post processing graphics user interface (GUI) was developed at NASA to aid the data exploration and analysis. Due to the thinness of the samples, low frequency-thickness-product plate wave approximations allowed for the calculation of closed-form solutions for material elastic property estimation. These approximations were then verified by calculating the Lamb wave solutions using the previously obtained material properties. Finally, the estimated elastic material properties were implemented in a COMSOL simulation model, and dispersion curves were extracted from simulation results. The dispersion curves and derived material properties were then compared to the analysis results from the experimental data. These comparisons informed on the accuracy of the measured material properties and helped demonstrate the accuracy of the finite element analysis (FEA) computational models. This assessment will prove vital when we start simulating more complex multi-layer components and poroelastic media. This paper gives a brief background of the problem space, outlines the workflow for data analysis and verification, shows results from the workflow, and gives an overview of future plans for simulation of lithium metal LIB inspection.

Peter Juarez↗

NASA NTRS2008

Automated Production of Movies on a Cluster of Computers

A method of accelerating and facilitating production of video and film motion-picture products, and software and generic designs of computer hardware to implement the method, are undergoing development. The method provides for automation of most of the tedious and repetitive tasks involved in editing and otherwise processing raw digitized imagery into final motion-picture products. The method was conceived to satisfy requirements, in industrial and scientific testing, for rapid processing of multiple streams of simultaneously captured raw video imagery into documentation in the form of edited video imagery and video derived data products for technical review and analysis. In the production of such video technical documentation, unlike in production of motion-picture products for entertainment, (1) it is often necessary to produce multiple video derived data products, (2) there are usually no second chances to repeat acquisition of raw imagery, (3) it is often desired to produce final products within minutes rather than hours, days, or months, and (4) consistency and quality, rather than aesthetics, are the primary criteria for judging the products. In the present method, the workflow has both serial and parallel aspects: processing can begin before all the raw imagery has been acquired, each video stream can be subjected to different stages of processing simultaneously on different computers that may be grouped into one or more cluster(s), and the final product may consist of multiple video streams. Results of processing on different computers are shared, so that workers can collaborate effectively.

Nail, Jasper↗

NASA NTRS2016

Climate Analytics as a Service

Exascale computing, big data, and cloud computing are driving the evolution of large-scale information systems toward a model of data-proximal analysis. In response, we are developing a concept of climate analytics as a service (CAaaS) that represents a convergence of data analytics and archive management. With this approach, high-performance compute-storage implemented as an analytic system is part of a dynamic archive comprising both static and computationally realized objects. It is a system whose capabilities are framed as behaviors over a static data collection, but where queries cause results to be created, not found and retrieved. Those results can be the product of a complex analysis, but, importantly, they also can be tailored responses to the simplest of requests. NASA's MERRA Analytic Service and associated Climate Data Services API provide a real-world example of climate analytics delivered as a service in this way. Our experiences reveal several advantages to this approach, not the least of which is orders-of-magnitude time reduction in the data assembly task common to many scientific workflows.

big data↗

NASA NTRSDate not supplied

Multi-Scale Thermo-Mechanical Modeling of Porous 3D Woven TPS Materials

This work summarizes the process to compute and analyze the thermal conductivity and mechanical properties of 3D woven TPS materials such as 3MDCP (3D Mid-Density Carbon-Phenolic). The PuMA [1,2] software, developed at NASA Ames, was used to characterize different 3MDCP samples from their constituents' data, averaging their thermal conductivity and elastic properties in the three main directions to obtain their effective orthotropic thermo-mechanical properties. This was performed in multiple steps: firstly, TPS samples were digitally reconstructed using micro computed tomography (µCT) and their constituents were segmented; then, the porous matrix phase was analyzed at the micro-scale and these results were used, along with the fibers’ constituents information, to model the tows at the meso-scale; finally, results from the constituents were homogenized and used to model the thermo-mechanical behavior of the unit cell at the macro-scale. Additionally, to gain a better understanding of the tows’ morphology and distribution of the carbon-phenolic blended fibers, microscopy images of the tows’ cross-section were segmented using deep learning techniques and analyzed with PuMA. This workflow can be applied to any TPS material to computationally obtain its thermo-mechanical properties, enabling more informed TPS design and manufacturing choices.

Multi-Scale↗

NASA NTRSDate not supplied

Multi-Scale Thermo-Mechanical Modeling of Porous 3D Woven TPS Materials

This work summarizes the process to compute and analyze the thermal conductivity and mechanical properties of 3D woven TPS materials such as 3MDCP (3D Mid-Density Carbon-Phenolic). The PuMA software, developed at NASA Ames, was used to characterize different 3MDCP samples from their constituents' data, averaging their thermal conductivity and elastic properties in the three main directions to obtain their effective orthotropic thermo-mechanical properties. This was performed in multiple steps: firstly, TPS samples were digitally reconstructed using micro computed tomography (µCT) and their constituents were segmented; then, the porous matrix phase was analyzed at the micro-scale and these results were used, along with the fibers’ constituents information, to model the tows at the meso-scale; finally, results from the constituents were homogenized and used to model the thermo-mechanical behavior of the unit cell at the macro-scale. Additionally, to gain a better understanding of the tows’ morphology and distribution of the carbon-phenolic blended fibers, microscopy images of the tows’ cross-section were segmented using deep learning techniques and analyzed with PuMA. This workflow can be applied to any TPS material to computationally obtain its thermo-mechanical properties, enabling more informed TPS design and manufacturing choices.

Multi-Scale↗

NASA NTRS2023

Simulation of a Single Rotor and a Quadrotor Hovering in Full and Partial Ground Effect

With the development of urban air mobility, the safety of multirotor vehicle operations in proximity to vertiports must be assessed. This paper examines high-fidelity blade-resolved OVERFLOW simulations and medium-fidelity vortex particle–mesh simulations that capture the aerodynamics of a rotorcraft in full and partial ground effect. Results obtained with each CFD method are compared to verify the consistency of the models introduced in the latter, which include an immersed lifting line for the blade aerodynamics and various boundary conditions to account for solid surfaces such as ground and walls. Satisfactory agreement is obtained for the blade loads and rotorwash velocity profiles in the case of a single rotor, except for small discrepancies at the blade tip. Finally, a quadrotor air taxi is simulated to quantify the load imbalance when the vehicle hovers above a rooftop edge. The combined use of high- and medium-fidelity methods is envisioned to accelerate the workflow when a large number of configurations need to be tested.

HEC↗