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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.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Feeding People's Curiosity: Leveraging the Cloud for Automatic Dissemination of Mars Images

Smartphones and tablets have made wireless computing ubiquitous, and users expect instant, on-demand access to information. The Mars Science Laboratory (MSL) operations software suite, MSL InterfaCE (MSLICE), employs a different back-end image processing architecture compared to that of the Mars Exploration Rovers (MER) in order to better satisfy modern consumer-driven usage patterns and to offer greater server-side flexibility. Cloud services are a centerpiece of the server-side architecture that allows new image data to be delivered automatically to both scientists using MSLICE and the general public through the MSL website (http://mars.jpl.nasa.gov/msl/).

Cloud Computing↗
NASA NTRS2014

The Curiosity Mars Rover's Fault Protection Engine

The Curiosity Rover, currently operating on Mars, contains flight software onboard to autonomously handle aspects of system fault protection. Over 1000 monitors and 39 responses are present in the flight software. Orchestrating these behaviors is the flight software's fault protection engine. In this paper, we discuss the engine's design, responsibilities, and present some lessons learned for future missions.

flight software↗
NASA NTRS1996

Project Galileo at Jupiter

Galileo made a highly successful arrival at Jupiter on December 7, 1995. The Galileo Atmospheric Entry Probe transmitted the first-ever direct measurements of an outer planet to the Orbiter mothership for nearly one hour while decsending to a pressure depth of 23 bar-far beyond the 10 bar mission requirements...This paper will summarize: 1) the Probe mission results, both engineering and scientific, 2) the problems with the Orbiter tape recorder and its recovery, 3) the Orbiter engineering operations including the loading and performance of the new flight software, and 4) early science results from the arrival and first two orbits and Ganymede encounters. Overall, mission status and the forecast for the remainder of the Orbiter's two-year primary mission will also be provided.

Galileo↗
NASA NTRS2020

Integrating the Science Opportunity Analyzer with a Reusable Opportunity Search Framework

In our interactions with the Science Opportunity Analyzer (SOA) software, we recognized how its ability to search for geometric events in space is a need for robotic space missions in general. To satisfy this need, we propose the Tychonis framework, which is built upon the principles of: (1) separations of concerns, (2) extensibility, (3) reusability, and (4) independent verification and validation. Tychonis’ separation of concerns results in the availability of different constructs to model geometric events and search for them. These constructs can be extended by users as needed and reused across missions without changes. Given the low coupling between concerns, and the fact Tychonis can be augmented in isolation, its constructs can be validated independently from other pieces of software. This paper elaborates on these topics and presents an integration case study between Tychonis and SOA that relies on the concept of dynamic integration. Dynamic integration entails that augmentations of the framework are reflected automatically in the host application without any changes to the host application’s code. The SOA-Tychonis integration case study can be extrapolated to other tools that need to search for geometric events as the pattern repeats across implementations: (i) the manipulation of a user interface to model opportunities, (ii) the execution of algorithms to search for opportunities, and (iii) the presentation of search results to users. Overall, Tychonis’ is a story about how the application of proven software principles and good design choices can reduce risk and cost to space missions

Soria, Manel↗
NASA NTRS2022

Design of Materials with Alchemite

Machine learning models that establish the relationships between materials processing and properties can enable inverse design of materials through active learning. Alchemite is a commercial software that can perform inverse materials design on sparse data. Here we evaluate Alchemite’s performance on a dataset of shape memory alloys and a dataset of heat exchangers compared to baseline random forest models. Alchemite had higher accuracy when making predictions on sparse data and was more accurate or nearly as accurate as random forests on complete datasets while also quantifying uncertainty. The software was also used to suggest processing steps and design parameters to optimize properties and performance; however, physical validation of the suggested design parameters was beyond the scope of this work. Several useful design insights were gained about the impact of the design parameters on properties and performance including the importance of dopant choice and amount for shape memory alloys and the importance of height and weight on the thermal resistance of heat exchangers.

Machine learning↗
NASA NTRS2022

MLtool Python Code

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine Learning↗
NASA NTRS1988

Computer sciences

The Computer Science Program provides advanced concepts, techniques, system architectures, algorithms, and software for both space and aeronautics information sciences and computer systems. The overall goal is to provide the technical foundation within NASA for the advancement of computing technology in aerospace applications. The research program is improving the state of knowledge of fundamental aerospace computing principles and advancing computing technology in space applications such as software engineering and information extraction from data collected by scientific instruments in space. The program includes the development of special algorithms and techniques to exploit the computing power provided by high performance parallel processors and special purpose architectures. Research is being conducted in the fundamentals of data base logic and improvement techniques for producing reliable computing systems.

Smith, Paul H.↗
NASA NTRS2003

ESML for Earth Science Data Sets and Analysis

The primary objective of this research project was to transition ESML from design to application. The resulting schema and prototype software will foster community acceptance for the Define once, use anywhere concept central to ESML. Supporting goals include: 1) Refinement of the ESML schema and software libraries in cooperation with the user community; 2) Application of the ESML schema and software to a variety of Earth science data sets and analysis tools; 3) Development of supporting prototype software for enhanced ease of use; 4) Cooperation with standards bodies in order to assure ESML is aligned with related metadata standards as appropriate; and 5) Widespread publication of the ESML approach, schema, and software.

Graves, Sara↗
NASA NTRS2010

Kepler Science Operations Center Architecture

We give an overview of the operational concepts and architecture of the Kepler Science Data Pipeline. Designed, developed, operated, and maintained by the Science Operations Center (SOC) at NASA Ames Research Center, the Kepler Science Data Pipeline is central element of the Kepler Ground Data System. The SOC charter is to analyze stellar photometric data from the Kepler spacecraft and report results to the Kepler Science Office for further analysis. We describe how this is accomplished via the Kepler Science Data Pipeline, including the hardware infrastructure, scientific algorithms, and operational procedures. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center that hosts the computers required to perform data analysis. We discuss the high-performance, parallel computing software modules of the Kepler Science Data Pipeline that perform transit photometry, pixel-level calibration, systematic error-correction, attitude determination, stellar target management, and instrument characterization. We explain how data processing environments are divided to support operational processing and test needs. We explain the operational timelines for data processing and the data constructs that flow into the Kepler Science Data Pipeline.

Middour, Christopher↗
NASA NTRS2013

Memory-Efficient Onboard Rock Segmentation

Rockster-MER is an autonomous perception capability that was uploaded to the Mars Exploration Rover Opportunity in December 2009. This software provides the vision front end for a larger software system known as AEGIS (Autonomous Exploration for Gathering Increased Science), which was recently named 2011 NASA Software of the Year. As the first step in AEGIS, Rockster-MER analyzes an image captured by the rover, and detects and automatically identifies the boundary contours of rocks and regions of outcrop present in the scene. This initial segmentation step reduces the data volume from millions of pixels into hundreds (or fewer) of rock contours. Subsequent stages of AEGIS then prioritize the best rocks according to scientist- defined preferences and take high-resolution, follow-up observations. Rockster-MER has performed robustly from the outset on the Mars surface under challenging conditions. Rockster-MER is a specially adapted, embedded version of the original Rockster algorithm ("Rock Segmentation Through Edge Regrouping," (NPO- 44417) Software Tech Briefs, September 2008, p. 25). Although the new version performs the same basic task as the original code, the software has been (1) significantly upgraded to overcome the severe onboard re source limitations (CPU, memory, power, time) and (2) "bulletproofed" through code reviews and extensive testing and profiling to avoid the occurrence of faults. Because of the limited computational power of the RAD6000 flight processor on Opportunity (roughly two orders of magnitude slower than a modern workstation), the algorithm was heavily tuned to improve its speed. Several functional elements of the original algorithm were removed as a result of an extensive cost/benefit analysis conducted on a large set of archived rover images. The algorithm was also required to operate below a stringent 4MB high-water memory ceiling; hence, numerous tricks and strategies were introduced to reduce the memory footprint. Local filtering operations were re-coded to operate on horizontal data stripes across the image. Data types were reduced to smaller sizes where possible. Binary- valued intermediate results were squeezed into a more compact, one-bit-per-pixel representation through bit packing and bit manipulation macros. An estimated 16-fold reduction in memory footprint relative to the original Rockster algorithm was achieved. The resulting memory footprint is less than four times the base image size. Also, memory allocation calls were modified to draw from a static pool and consolidated to reduce memory management overhead and fragmentation. Rockster-MER has now been run onboard Opportunity numerous times as part of AEGIS with exceptional performance. Sample results are available on the AEGIS website at http://aegis.jpl.nasa.gov.

Burl, Michael C.↗
NASA NTRS2003

Technology for a NASA Space-Based Science Operations Grid

This viewgraph representation presents an overview of a proposal to develop a space-based operations grid in support of space-based science experiments. The development of such a grid would provide a dynamic, secure and scalable architecture based on standards and next-generation reusable software and would enable greater science collaboration and productivity through the use of shared resources and distributed computing. The authors propose developing this concept for use on payload experiments carried aboard the International Space Station. Topics covered include: grid definitions, portals, grid development and coordination, grid technology and potential uses of such a grid.

Bradford, Robert N.↗
DOE OSTI2025

InverseBench: Inverse design benchmark suite that contains inverse problems from science and engineering (InverseBench) v0.0.1

A software package that contains three inverse design blackbox problems to investigate the efficiency and accuracy of inverse design machine learning models. The software contains highly accurate forward machine learning models that can be used to assess the inverse predictions. The package also contains separate test data for each problem. The inverse design problems that are in the package are: airfoil inverse design, scalar boundary reconstruction and photonic surfaces inverse design.

Grbcic, Luka [Lawrence Berkeley National Laborator↗
NASA NTRS1980

DSN system performance test software

The software support group of the Deep Space Network support section is continuing the development of system performance test (SPT) software for the Deep Space Network. During the past two and one-half years, test software was developed for a new system, Radio Science, and many new features were added to existing software. Plans are underway for implementation of test software for the very long baseline interferometry system and the network data processing area. A description of the elements of the SPT software is provided.

Leppla, F. B.↗
NASA NTRS2010

Data Validation in the Kepler Science Operations Center Pipeline

We present an overview of the Data Validation (DV) software component and its context within the Kepler Science Operations Center (SOC) pipeline and overall Kepler Science mission. The SOC pipeline performs a transiting planet search on the corrected light curves for over 150,000 targets across the focal plane array. We discuss the DV strategy for automated validation of Threshold Crossing Events (TCEs) generated in the transiting planet search. For each TCE, a transiting planet model is fitted to the target light curve. A multiple planet search is conducted by repeating the transiting planet search on the residual light curve after the model flux has been removed; if an additional detection occurs, a planet model is fitted to the new TCE. A suite of automated tests are performed after all planet candidates have been identified. We describe a centroid motion test to determine the significance of the motion of the target photocenter during transit and to estimate the coordinates of the transit source within the photometric aperture; a series of eclipsing binary discrimination tests on the parameters of the planet model fits to all transits and the sequences of odd and even transits; and a statistical bootstrap to assess the likelihood that the TCE would have been generated purely by chance given the target light curve with all transits removed. Keywords: photometry, data validation, Kepler, Earth-size planets

Wu, Hayley↗
NASA NTRS2012

Tools Ensure Reliability of Critical Software

In November 2006, after attempting to make a routine maneuver, NASA's Mars Global Surveyor (MGS) reported unexpected errors. The onboard software switched to backup resources, and a 2-day lapse in communication took place between the spacecraft and Earth. When a signal was finally received, it indicated that MGS had entered safe mode, a state of restricted activity in which the computer awaits instructions from Earth. After more than 9 years of successful operation gathering data and snapping pictures of Mars to characterize the planet's land and weather communication between MGS and Earth suddenly stopped. Months later, a report from NASA's internal review board found the spacecraft's battery failed due to an unfortunate sequence of events. Updates to the spacecraft's software, which had taken place months earlier, were written to the wrong memory address in the spacecraft's computer. In short, the mission ended because of a software defect. Over the last decade, spacecraft have become increasingly reliant on software to carry out mission operations. In fact, the next mission to Mars, the Mars Science Laboratory, will rely on more software than all earlier missions to Mars combined. According to Gerard Holzmann, manager at the Laboratory for Reliable Software (LaRS) at NASA's Jet Propulsion Laboratory (JPL), even the fault protection systems on a spacecraft are mostly software-based. For reasons like these, well-functioning software is critical for NASA. In the same year as the failure of MGS, Holzmann presented a new approach to critical software development to help reduce risk and provide consistency. He proposed The Power of 10: Rules for Developing Safety-Critical Code, which is a small set of rules that can easily be remembered, clearly relate to risk, and allow compliance to be verified. The reaction at JPL was positive, and developers in the private sector embraced Holzmann's ideas.

Source record↗
NASA NTRS2013

Advances in SPICE Support of Planetary Science

SPICE is the de facto international standard for determining the geometric conditions-parameters such as altitude, lighting angles, and LAT/LON coverage of an instrument footprint-pertaining to scientific observations acquired by instruments on board robotic spacecraft. This system, comprised of data and allied software, is used for planning science observations and for analyzing the data returned from those observations. Use of SPICE is not a NASA requirement but is recommended by NASA's Planetary Data System and by the International Planetary Data Alliance. Owing in part to its reliability, stability, portability and user support, the use of SPICE has spread to many national space agencies, including those of the U.S., Europe (ESA), Japan, Russia and India. SPICE has been in use since the Magellan mission to Venus and so has many well-known capabilities. But the NAIF Team responsible for implementing SPICE continues to add new features; this presentation describes a number of these.

computational capability↗