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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Data Acquisition and Control Systems Laboratory

The Data Acquisition and Control Systems (DACS) Laboratory is a facility at Stennis Space Center that provides an off test-stand capability to develop data-acquisition and control systems for rocket-engine test stands. It is also used to train new employees in state-of-the-art systems, and provides a controlled environment for troubleshooting existing systems, as well as the ability to evaluate the application of new technologies and process improvements. With the SSC propulsion testing schedules, without the DACS Laboratory, it would have been necessary to perform most of the development work on actual test systems, thereby subjecting both the rocket-engine testing and development programs to substantial interference in the form of delays, restrictions on modifications of equipment, and potentially compromising software configuration control. The DACS Laboratory contains a versatile assortment of computer hardware and software, digital and analog electronic control and data-acquisition equipment, and standard electronic bench test equipment and tools. Recently completed Control System development and software verification projects include support to the joint NASA/Air Force Integrated Powerhead Demonstration (IPD) LOX & LH2 PreBurner and Turbopump ground testing programs. In other recent activities, the DACS Laboratory equipment and expertise have supported the off-stand operation of high-pressure control valves to correct valve leak problems prior to installation on the test stand. Future plans include expanding the Laboratory's capabilities to provide cryogenic control valve characterization prior to installation, thereby reducing test stand activation time.

Holland, Randy↗
NASA NTRS2015

Operating in "Strange New Worlds" and Measuring Success - Test and Evaluation in Complex Environments

Software tools are being developed by the Autonomy Incubator at NASA's Langley Research Center that will provide an integrated and scalable capability to support research and non-research flight operations across several flight domains, including urban and mixed indoor-outdoor operations. These tools incorporate a full range of data products to support mission planning, approval, flight operations, and post-flight review. The system can support a number of different operational scenarios that can incorporate live and archived data streams for UAS operators, airspace regulators, and other important stakeholders. Example use cases are described that illustrate how the tools will benefit a variety of users in nominal and off-nominal operational scenarios. An overview is presented for the current state of the toolset, including a summary of current demonstrations that have been completed. Details of the final, fully operational capability are also presented, including the interfaces that will be supported to ensure compliance with existing and future airspace operations environments.

Qualls, Garry↗
NASA NTRS2019

Demisability of GFRP and CFRP Components of Reentering Orbital Debris: Phase I Test Results

Observations of surviving reentry debris on the ground and research performed by Hyperschall Technologie Göttingen (HTG) [1] indicated that significantly more glass fiber-reinforced polymer (GFRP) and carbon fiber-reinforced polymer (CFRP) components survive reentry than current models predict. NASA’s Orbital Debris Program Office conducted a series of tests to evaluate the accuracy of material demise models for reentering orbital debris used in NASA’s Object Reentry Survival Analysis Tool (ORSAT) and Debris Assessment Software (DAS). Testing is planned in a multi-phase series to allow for quick quantification of results as well as refinement of methods resulting from lessons learned during early phases. The Phase 1 tests discussed here validated ORSAT models for homogeneous metals, provided an efficient quantification of composite material demisability properties like mass loss rate and overall time to demise, and identified potential failure modes, which are currently not well understood. Phase 2 tests will be used to further understand mass loss rates and modes of both thermal and mechanical failure in composite materials. The authors exposed 95 samples of aluminum, CFRP, Kevlar fiber-reinforced polymer, GFRP, and sheets of G10 fiberglass to conditions approximating the reentry environment using an inductively coupled plasma (ICP) torch facility. The cylindrical CFRP samples were exposed to the atmospheric pressure plasma, at both the end and the midpoint, to investigate the difference in demisability between parts with exposed edges, like panels, and parts with no edges, such as carbonoverwrapped pressure vessels (COPVs). In a non-oxidative environment, no composite materials demised within the 5-minute test time. In the oxidative, elevated heat flux environment, CFRP samples demised between 210 s and 270 s. For the first 100 s of insertion time, most of the mass loss was due to pyrolysis of resin, creating an approximately bi-linear mass-loss rate curve with time. In a non-oxidative environment, carbon filaments were observed to unravel from some of the CFRP end-burned samples; however, this effect did not seem to affect the overall time to demise for the samples significantly. These results indicate that both GFRP and CFRP components survive reentry with significantly more remaining mass than current models predict.

Greene, Benton R.↗
NASA NTRS1989

Multi-version software reliability through fault-avoidance and fault-tolerance

A number of experimental and theoretical issues associated with the practical use of multi-version software to provide run-time tolerance to software faults were investigated. A specialized tool was developed and evaluated for measuring testing coverage for a variety of metrics. The tool was used to collect information on the relationships between software faults and coverage provided by the testing process as measured by different metrics (including data flow metrics). Considerable correlation was found between coverage provided by some higher metrics and the elimination of faults in the code. Back-to-back testing was continued as an efficient mechanism for removal of un-correlated faults, and common-cause faults of variable span. Software reliability estimation methods was also continued based on non-random sampling, and the relationship between software reliability and code coverage provided through testing. New fault tolerance models were formulated. Simulation studies of the Acceptance Voting and Multi-stage Voting algorithms were finished and it was found that these two schemes for software fault tolerance are superior in many respects to some commonly used schemes. Particularly encouraging are the safety properties of the Acceptance testing scheme.

Vouk, Mladen A.↗
NASA NTRS1988

Applications of square-root information filtering and smoothing in spacecraft orbit determination

The JPL (Jet Propulsion Laboratory) Orbit Determination Software System is a set of computer programs developed for the primary purpose of determining the flight path of deep-space mission spacecraft in NASA's Planetary Program and highly elliptical orbiting spacecraft in Earth orbit. The filtering processes available within the JPL Orbit Determination Software are discussed, and several examples are presented. In particular, solutions obtained by the Square Root Information Filter (SRIF) using Bierman's Estimation Subroutine Library (ESL) are discussed and compared with the solutions obtained by the singular value decomposition (SVD) technique. It is concluded that the SRIF filtering and smoothing algorithms are efficient and numerically stable for well-conditioned systems. The use of Bierman's ESL simplifies the task of maintaining the orbit determination software by providing efficient, tested filtering tools. For solving a large well-conditioned system (rank higher than 120), SRIF is approximately four times faster than SVD; however, for solving an ill-conditioned system, SVD is recommended.

Wang, Tseng-Chan↗
NASA NTRS2001

Predicting Human Error in Air Traffic Control Decision Support Tools and Free Flight Concepts

The document is a set of briefing slides summarizing the work the Advanced Air Transportation Technologies (AATT) Project is doing on predicting air traffic controller and airline pilot human error when using new decision support software tools and when involved in testing new air traffic control concepts. Previous work in this area is reviewed as well as research being done jointly with the FAA. Plans for error prediction work in the AATT Project are discussed. The audience is human factors researchers and aviation psychologists from government and industry.

Mogford, Richard↗
NASA NTRS2010

Spinoff 2010

Topics covered include: Burnishing Techniques Strengthen Hip Implants; Signal Processing Methods Monitor Cranial Pressure; Ultraviolet-Blocking Lenses Protect, Enhance Vision; Hyperspectral Systems Increase Imaging Capabilities; Programs Model the Future of Air Traffic Management; Tail Rotor Airfoils Stabilize Helicopters, Reduce Noise; Personal Aircraft Point to the Future of Transportation; Ducted Fan Designs Lead to Potential New Vehicles; Winglets Save Billions of Dollars in Fuel Costs; Sensor Systems Collect Critical Aerodynamics Data; Coatings Extend Life of Engines and Infrastructure; Radiometers Optimize Local Weather Prediction; Energy-Efficient Systems Eliminate Icing Danger for UAVs; Rocket-Powered Parachutes Rescue Entire Planes; Technologies Advance UAVs for Science, Military; Inflatable Antennas Support Emergency Communication; Smart Sensors Assess Structural Health; Hand-Held Devices Detect Explosives and Chemical Agents; Terahertz Tools Advance Imaging for Security, Industry; LED Systems Target Plant Growth; Aerogels Insulate Against Extreme Temperatures; Image Sensors Enhance Camera Technologies; Lightweight Material Patches Allow for Quick Repairs; Nanomaterials Transform Hairstyling Tools; Do-It-Yourself Additives Recharge Auto Air Conditioning; Systems Analyze Water Quality in Real Time; Compact Radiometers Expand Climate Knowledge; Energy Servers Deliver Clean, Affordable Power; Solutions Remediate Contaminated Groundwater; Bacteria Provide Cleanup of Oil Spills, Wastewater; Reflective Coatings Protect People and Animals; Innovative Techniques Simplify Vibration Analysis; Modeling Tools Predict Flow in Fluid Dynamics; Verification Tools Secure Online Shopping, Banking; Toolsets Maintain Health of Complex Systems; Framework Resources Multiply Computing Power; Tools Automate Spacecraft Testing, Operation; GPS Software Packages Deliver Positioning Solutions; Solid-State Recorders Enhance Scientific Data Collection; Computer Models Simulate Fine Particle Dispersion; Composite Sandwich Technologies Lighten Components; Cameras Reveal Elements in the Short Wave Infrared; Deformable Mirrors Correct Optical Distortions; Stitching Techniques Advance Optics Manufacturing; Compact, Robust Chips Integrate Optical Functions; Fuel Cell Stations Automate Processes, Catalyst Testing; Onboard Systems Record Unique Videos of Space Missions; Space Research Results Purify Semiconductor Materials; and Toolkits Control Motion of Complex Robotics.

Source record↗
NASA NTRS2022

Adaptive IV&V for Increasingly Complex Software Systems

As NASA software systems continue to innovate, becoming more complex and nondeterministic, the need for the NASA IV&V Program to become systemically adaptive to ensure mission success is paramount. To ensure adaptability within resource constraints, IV&V has developed an agile, risk-based approach to identify, characterize, scope, focus, and prioritize mission assurance activities. This risk based adaptive framework has been applied to trends such as increased reliance on data driven algorithms for safety and mission critical software behavior, use of MBSE in system design, and application of agile principles to embedded software development. The framework is enabled by continuous innovation of new approaches such as software only test beds, assurance design tools, and initiatives that augment IV&V assurance methods with artificial intelligence and machine learning techniques. This presentation will highlight the trends the NASA IV&V Program is seeing, the innovative steps it is taking to address those challenges, and how it is postured to address evolving risk and constantly changing and new technologies.

Wesley W Deadrick↗
NASA NTRS1984

An interactive program for software reliability modeling

With the tremendous growth in computer software, the demand has arisen for producing cost effective reliable software. Over the last 10 years an area of research has developed which attempts to address this problem by estimating a program's current reliability by modeling either the times between error detections or the error counts in past testing periods. A new tool for interactive software reliability analysis using the computer is described. This computer program allows the user to perform a complete reliability analysis using any of eight well-known models appearing in the literature. Some of the capabilities of the program are illustrated by means of an analysis of a set of simulated error data.

Farr, W. H.↗
NASA NTRS1981

A high order approach to flight software development and testing

The use of a software development facility is discussed as a means of producing a reliable and maintainable ECS software system, and as a means of providing efficient use of the ECS hardware test facility. Principles applied to software design are given, including modularity, abstraction, hiding, and uniformity. The general objectives of each phase of the software life cycle are also given, including testing, maintenance, code development, and requirement specifications. Software development facility tools are summarized, and tool deficiencies recognized in the code development and testing phases are considered. Due to limited lab resources, the functional simulation capabilities may be indispensable in the testing phase.

Steinbacher, J.↗
NASA NTRS2011

Hazard Detection Software for Lunar Landing

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project is developing a system for safe and precise manned lunar landing that involves novel sensors, but also specific algorithms. ALHAT has selected imaging LIDAR (light detection and ranging) as the sensing modality for onboard hazard detection because imaging LIDARs can rapidly generate direct measurements of the lunar surface elevation from high altitude. Then, starting with the LIDAR-based Hazard Detection and Avoidance (HDA) algorithm developed for Mars Landing, JPL has developed a mature set of HDA software for the manned lunar landing problem. Landing hazards exist everywhere on the Moon, and many of the more desirable landing sites are near the most hazardous terrain, so HDA is needed to autonomously and safely land payloads over much of the lunar surface. The HDA requirements used in the ALHAT project are to detect hazards that are 0.3 m tall or higher and slopes that are 5 or greater. Steep slopes, rocks, cliffs, and gullies are all hazards for landing and, by computing the local slope and roughness in an elevation map, all of these hazards can be detected. The algorithm in this innovation is used to measure slope and roughness hazards. In addition to detecting these hazards, the HDA capability also is able to find a safe landing site free of these hazards for a lunar lander with diameter .15 m over most of the lunar surface. This software includes an implementation of the HDA algorithm, software for generating simulated lunar terrain maps for testing, hazard detection performance analysis tools, and associated documentation. The HDA software has been deployed to Langley Research Center and integrated into the POST II Monte Carlo simulation environment. The high-fidelity Monte Carlo simulations determine the required ground spacing between LIDAR samples (ground sample distances) and the noise on the LIDAR range measurement. This simulation has also been used to determine the effect of viewing on hazard detection performance. The software has also been deployed to Johnson Space Center and integrated into the ALHAT real-time Hardware-in-the-Loop testbed.

Huertas, Andres↗
NASA NTRSDate not supplied

Segmentation of Lightweight Ablator Micro-Tomography Using Deep Learning

Ablative thermal protection systems (TPS) are essential for high speed entry of planetary atmospheres, such as those of Earth and Mars. Upon entry the kinetic energy of the spacecraft is converted into thermal energy, leading to high heat fluxes at the wall of the craft. Because of this extreme heating, a robust ablative TPS material must be selected. A common material selection today is phenolic-impregnated carbon ablator (PICA), which is a low-density carbon material known for producing dust that is not suitable to a cleanroom environment. To mitigate dust created by a PICA heatshield, a silicone-based spray called NuSil is applied to the surface of the TPS, creating PICA-NuSil (PICA-N). PICA-N has been observed to have a different material response from regular PICA during high enthalpy flow testing, producing surface temperatures up to 200K less than those seen for PICA [1]. To better understand this phenomenon, it is critical that robust methods of PICA-N material characterization are developed. The purpose of this project is to investigate Object Research Systems’ (ORS) Dragonfly deep learning tools as a means of accurately segmenting and characterizing PICA-N. Systematic testing of this software has shown that Dragonfly deep learning tools have strong potential for accurate segmentation/ characterization of PICA-N and other TPS materials.

Micro-tomography↗
NASA NTRS1988

Validation of highly reliable, real-time knowledge-based systems

Knowledge-based systems have the potential to greatly increase the capabilities of future aircraft and spacecraft and to significantly reduce support manpower needed for the space station and other space missions. However, a credible validation methodology must be developed before knowledge-based systems can be used for life- or mission-critical applications. Experience with conventional software has shown that the use of good software engineering techniques and static analysis tools can greatly reduce the time needed for testing and simulation of a system. Since exhaustive testing is infeasible, reliability must be built into the software during the design and implementation phases. Unfortunately, many of the software engineering techniques and tools used for conventional software are of little use in the development of knowledge-based systems. Therefore, research at Langley is focused on developing a set of guidelines, methods, and prototype validation tools for building highly reliable, knowledge-based systems. The use of a comprehensive methodology for building highly reliable, knowledge-based systems should significantly decrease the time needed for testing and simulation. A proven record of delivering reliable systems at the beginning of the highly visible testing and simulation phases is crucial to the acceptance of knowledge-based systems in critical applications.

Johnson, Sally C.↗
DOE OSTI2024

pnnl-predictive-phenomics/csc052-gem

Genome-Scale Metabolic Model Continuous Validation with Memote for CarbStor Community Member Bacillus These repositories contain the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity.

Torres, Victor E.↗
DOE OSTI2024

pnnl-predictive-phenomics/csc031-gem

Genome-Scale Metabolic Model of CarbStor Community member Microbacterium (csc031) Continuous Validation with Memote These repositories contain the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity

McNaughton, Andrew [@PNNL]↗
DOE OSTI2024

pnnl-predictive-phenomics/csc009-gem

Genome-Scale Metabolic Model of CarbStore Community member Curtobacterium (csc009) Continuous Validation with Memote These repositories contain the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity

Lin, Tesia↗
DOE OSTI2024

pnnl-predictive-phenomics/csc040-gem

Genome-Scale Metabolic Model Continuous Validation with Memote for CarbStor Community Member Rhodococcus These repositories contain the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity.

McNaughton, Andrew [@PNNL]↗
DOE OSTI2024

pnnl-predictive-phenomics/csc043-gem

Genome-Scale Metabolic Model Continuous Validation with Memote for CarbStor Community Member Paenibacillus These repositories contain the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity.

Zucker, Jeremy [Pacific Northwest National Laborat↗