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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

NASA Tech Briefs, May 2011

Topics covered include: 1) Method to Estimate the Dissolved Air Content in Hydraulic Fluid; 2) Method for Measuring Collimator-Pointing Sensitivity to Temperature Changes; 3) High-Temperature Thermometer Using Cr-Doped GdAlO3 Broadband Luminescence; 4)Metrology Arrangement for Measuring the Positions of Mirrors of a Submillimeter Telescope; 5) On-Wafer S-Parameter Measurements in the 325-508-GHz Band; 6) Reconfigurable Microwave Phase Delay Element for Frequency Reference and Phase-Shifter Applications; 7) High-Speed Isolation Board for Flight Hardware Testing; 8) High-Throughput, Adaptive FFT Architecture for FPGA-Based Spaceborne Data Processors; 9) 3D Orbit Visualization for Earth-Observing Missions; 10) MaROS: Web Visualization of Mars Orbiting and Landed Assets; 11) RAPID: Collaborative Commanding and Monitoring of Lunar Assets; 12) Image Segmentation, Registration, Compression, and Matching; 13) Image Calibration; 14) Rapid ISS Power Availability Simulator; 15) A Method of Strengthening Composite/Metal Joints; 16) Pre-Finishing of SiC for Optical Applications; 17) Optimization of Indium Bump Morphology for Improved Flip Chip Devices; 18) Measuring Moisture Levels in Graphite Epoxy Composite Sandwich Structures; 19) Marshall Convergent Spray Formulation Improvement for High Temperatures; 20) Real-Time Deposition Monitor for Ultrathin Conductive Films; 21) Optimized Li-Ion Electrolytes Containing Triphenyl Phosphate as a Flame-Retardant Additive; 22) Radiation-Resistant Hybrid Lotus Effect for Achieving Photoelectrocatalytic Self-Cleaning Anticontamination Coatings; 23) Improved, Low-Stress Economical Submerged Pipeline; 24) Optical Fiber Array Assemblies for Space Flight on the Lunar Reconnaissance Orbiter; 25) Local Leak Detection and Health Monitoring of Pressurized Tanks; 26) Dielectric Covered Planar Antennas at Submillimeter Wavelengths for Terahertz Imaging; 27) Automated Cryocooler Monitor and Control System; 28) Broadband Achromatic Phase Shifter for a Nulling Interferometer; 29) Super Dwarf Wheat for Growth in Confined Spaces; 30) Fine Guidance Sensing for Coronagraphic Observatories; 31) Single-Antenna Temperature- and Humidity-Sounding Microwave Receiver; 32) Multi-Wavelength, Multi-Beam, and Polarization-Sensitive Laser Transmitter for Surface Mapping; 33) Optical Communications Link to Airborne Transceiver; 34) Ascent Heating Thermal Analysis on Spacecraft Adaptor Fairings; 35) Entanglement in Self-Supervised Dynamics; 36) Prioritized LT Codes; 37) Fast Image Texture Classification Using Decision Trees; 38) Constraint Embedding Technique for Multibody System Dynamics; 39) Improved Systematic Pointing Error Model for the DSN Antennas; 40) Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks; 41) More-Accurate Model of Flows in Rocket Injectors; 42) In-Orbit Instrument-Pointing Calibration Using the Moon as a Target; 43) Reliability of Ceramic Column Grid Array Interconnect Packages Under Extreme Temperatures; 44) Six Degrees-of-Freedom Ascent Control for Small-Body Touch and Go; and 45) Optical-Path-Difference Linear Mechanism for the Panchromatic Fourier Transform Spectrometer.

Source record↗
DOE OSTI2023

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2007

Predicting the Earth encounters of (99942) Apophis

Arecibo delay-Doppler measurements of (99942) Apophis in 2005 and 2006 resulted in a five standard-deviation trajectory correction to the optically predicted close approach distance to Earth in 2029. The radar measurements reduced the volume of the statistical uncertainty region entering the encounter to 7.3% of the pre-radar solution, but increased the trajectory uncertainty growth rate across the encounter by 800% due to the closer predicted approach to the Earth. A small estimated Earth impact probability remained for 2036. With standard-deviation plane-of-sky position uncertainties for 2007-2010 already less than 0.2 arcsec, the best near-term ground-based optical astrometry can only weakly affect the trajectory estimate. While the potential for impact in 2036 will likely be excluded in 2013 (if not 2011) using ground-based optical measurements, approximations within the Standard Dynamical Model (SDM) used to estimate and predict the trajectory from the current era are sufficient to obscure the difference between a predicted impact and a miss in 2036 by altering the dynamics leading into the 2029 encounter. Normal impact probability assessments based on the SDM become problematic without knowledge of the object's physical properties; impact could be excluded while the actual dynamics still permit it. Calibrated position uncertainty intervals are developed to compensate for this by characterizing the minimum and maximum effect of physical parameters on the trajectory. Uncertainty in accelerations related to solar radiation can cause between 82 and 4720 Earth-radii of trajectory change relative to the SDM by 2036. If an actionable hazard exists, alteration by 2-10% of Apophis' total absorption of solar radiation in 2018 could be sufficient to produce a six standard-deviation trajectory change by 2036 given physical characterization; even a 0.5% change could produce a trajectory shift of one Earth-radius by 2036 for all possible spin-poles and likely masses. Planetary ephemeris uncertainties are the next greatest source of systematic error, causing up to 23 Earth-radii of uncertainty. The SDM Earth point-mass assumption introduces an additional 2.9 Earth-radii of prediction error by 2036. Unmodeled asteroid perturbations produce as much as 2.3 Earth-radii of error. We find no future small-body encounters likely to yield an Apophis mass determination prior to 2029. However, asteroid (144898) 2004 VD17, itself having a statistical Earth impact in 2102, will probably encounter Apophis at 6.7 lunar distances in 2034, their uncertainty regions coming as close as 1.6 lunar distances near the center of both SDM probability distributions.

Orbit determination↗
NASA NTRS2017

Models of Sector Flows Under Local, Regional and Airport Weather Constraints

Recently, the ATM community has made important progress in collaborative trajectory management through the introduction of a new FAA traffic management initiative called a Collaborative Trajectory Options Program (CTOP). FAA can use CTOPs to manage air traffic under multiple constraints (manifested as flow constrained areas or FCAs) in the system, and it allows flight operators to indicate their preferences for routing and delay options. CTOPs also permits better management of the overall trajectory of flights by considering both routing and departure delay options simultaneously. However, adoption of CTOPs in airspace has been hampered by many factors that include challenges in how to identify constrained areas and how to set rates for the FCAs. Decision support tools providing assistance would be particularly helpful in effective use of CTOPs. Such DSTs tools would need models of demand and capacity in the presence of multiple constraints. This study examines different approaches to using historical data to create and validate models of maximum flows in sectors and other airspace regions in the presence of multiple constraints. A challenge in creating an empirical model of flows under multiple constraints is a lack of sufficient historical data that captures diverse situations involving combinations of multiple constraints especially those with severe weather. The approach taken here to deal with this is two-fold. First, we create a generalized sector model encompassing multiple sectors rather than individual sectors in order to increase the amount of data used for creating the model by an order of magnitude. Secondly, we decompose the problem so that the amount of data needed is reduced. This involves creating a baseline demand model plus a separate weather constrained flow reduction model and then composing these into a single integrated model. A nominal demand model is a flow model (gdem) in the presence of clear local weather. This defines the flow as a function of weather constraints in neighboring regions, airport constraints and weather in locations that can cause re-routes to the location of interest. A weather constrained flow reduction model (fwx-red) is a model of reduction in baseline counts as a function of local weather. Because the number of independent variables associated with each of the two decomposed models is smaller than that with a single model, need for amount of data is reduced. Finally, a composite model that combines these two can be represented as fwx-red (gdem(e), l) where e represents non-local constraints and l represents local weather. The approaches studied to developing these models are divided into three categories: (1) Point estimation models (2) Empirical models (3) Theoretical models. Errors in predictions of these different types of models have been estimated. In situations when there is abundant data, point estimation models tend to be very accurate. In contrast, empirical models do better than theoretical models when there is some data available. The biggest benefit of theoretical models is their general applicability in wider range situations once the degree of accuracy of these has been established.

Kulkarni, Deepak↗
NASA NTRS2017

Models of Sector Aircraft Counts in the Presence of Local, Regional and Airport Constraints

Recently, the ATM community has made important progress in collaborative trajectory management through the introduction of a new FAA traffic management initiative called a Collaborative Trajectory Options Program (CTOP). FAA can use CTOPs to manage air traffic under multiple constraints (manifested as flow constrained areas or FCAs) in the system, and it allows flight operators to indicate their preferences for routing and delay options. CTOPs also permits better management of the overall trajectory of flights by considering both routing and departure delay options simultaneously. However, adoption of CTOPs in airspace has been hampered by many factors that include challenges in how to identify constrained areas and how to set rates for the FCAs. Decision support tools providing assistance would be particularly helpful in effective use of CTOPs. Such DSTs tools would need models of demand and capacity in the presence of multiple constraints. This study examines different approaches to using historical data to create and validate models of maximum flows in sectors and other airspace regions in the presence of multiple constraints. A challenge in creating an empirical model of flows under multiple constraints is a lack of sufficient historical data that captures diverse situations involving combinations of multiple constraints especially those with severe weather. The approach taken here to deal with this is two-fold. First, we create a generalized sector model encompassing multiple sectors rather than individual sectors in order to increase the amount of data used for creating the model by an order of magnitude. Secondly, we decompose the problem so that the amount of data needed is reduced. This involves creating a baseline demand model plus a separate weather constrained flow reduction model and then composing these into a single integrated model. A nominal demand model is a flow model (gdem) in the presence of clear local weather. This defines the flow as a function of weather constraints in neighboring regions, airport constraints and weather in locations that can cause re-routes to the location of interest. A weather constrained flow reduction model (fwx-red) is a model of reduction in baseline counts as a function of local weather. Because the number of independent variables associated with each of the two decomposed models is smaller than that with a single model, need for amount of data is reduced. Finally, a composite model that combines these two can be represented as fwx-red (gdem(e), l) where e represents non-local constraints and l represents local weather. The approaches studied to developing these models are divided into three categories: (1) Point estimation models (2) Empirical models (3) Theoretical models. Errors in predictions of these different types of models have been estimated. In situations when there is abundant data, point estimation models tend to be very accurate. In contrast, empirical models do better than theoretical models when there is some data available. The biggest benefit of theoretical models is their general applicability in wider range situations once the degree of accuracy of these has been established.

Kulkarni, Deepak↗
NASA NTRSDate not supplied

NASA's Moon to Mars Autonomous Habitat Status

NASA is developing a strategy for sending humans to the Mars vicinity, known broadly as the Moon to Mars (M2M) Campaign. A critical part of this campaign is the development of in-space and surface habitation systems capable of substantially extending human presence beyond Low Earth Orbit (LEO). Mars missions feature an in-space transit habitat capable of supporting crews of four on ~850-1200-day missions, including transit to and from Mars and time in Mars orbit. Surface and transit habitats are complex elements which must keep crewmembers healthy and productive in deep-space environments with limited resources, long rescue times in contingency situations, and communication delays; all within constrained mass, volume, and power budgets. These habitats provide crew both living and workspace as well as most of the resources needed to support crew life. For deep space habitats, automation needs to be employed due to latency and for significant amounts of time when the habitats are uncrewed. Automation of systems is possible in space applications, but there are limitations. Outside of the Earth’s (or any) magnetosphere, radiation environments are harsh to both the physical hardware and the software components. Radiation (charged particles and ionizing electromagnetic waves) degrades and damages the hardware and causes single event upsets (SEUs) in software. If the hardware is damaged, data can be lost, or control actions not made. For software, SEUs cause algorithms to result in different solutions, or incorrect commands to be sent out. This means that algorithms and hardware used for deep space systems are different than what is used on Earth. Radiation-tolerant hardware is generations behind the current state-of-the-art hardware. Recent NASA missions, such as James Webb Space Telescope, continue to rely on older technologies such as the RAD750 processor, and the most advanced processors are still single core and less than 1.5 GHz. There have been attempts to use higher performance processors, but these often take multiple mitigation steps to handle the radiation environments, which limits the processing power and/or throughput. Current techniques for radiation mitigation have been redundancies, voting, physical separation of hardware, encasing materials, under-clocking hardware, and more. Some radiation mitigation techniques do provide benefits such as having a redundant system to improve the probability that a system will be available when needed. Autonomous software systems will have fewer interactions with humans on deep space missions and therefore need to be able to handle more off-nominal conditions. Microgravity also complicates the autonomous aspects of the mission because autonomous systems are usually built from known deterministic states, but microgravity causes physical objects to shift and move changing the location an autonomous system placed the object. Not only does the software need to be reliable and deterministic, losing resources due to a software error is not only costly but detrimental to reputation. The combination of having lower performance hardware and having to be able to verify and deterministically run software and an ever-changing environment makes deep space autonomous systems more complicated. Multiple gaps have been identified including verification of autonomous software algorithms (including artificial intelligence and machine learning), higher performance processors (graphics and general purpose), high speed networks (onboard and transmissions), memory, power distribution, data security, and variations from these. These gaps need to be closed for more advanced systems to be deployed and reduce the size, weight, and power impacts on the habitats.

Scott B. Tashakkor↗
DOE OSTI2021

Deployment of salt sample extraction system at an engineering-scale electrorefiner

The goal of the salt sampling program at Argonne is to develop and deploy automated molten salt sampling approaches for interfacing relevant unit operations with salt analysis to improve the timeliness of sampling-based accountancy measurements. Two technologies under development in support of this goal are a vacuum sampling loop module and a high-throughput pneumatic sample generator module. Compared to traditional point sampling approaches (i.e., dip probes), the vacuum sampling loop facilitates the collection of a larger cross-section of the bulk salt in order to collect more representative samples. The vacuum sampling approach also eliminates the risk of dross contamination of samples and avoids the use of moving parts in the salt. The pneumatic sample generator module is used to facilitate high-throughput sample analysis to improve the measurement precision of any given analytical technique by averaging out random sampling and measurement errors. In FY21, two methods for integrating these two modules were tested including direct fluidic coupling and coupling using a solid salt transfer mechanism. Solid salt transfer was ultimately selected over fluidic coupling, primarily to enable the transport of samples over longer distances to support automated at-line integration with high-precision techniques (such as microcalorimetry) that cannot withstand the conditions near an electrorefining process. To facilitate rapid solid salt coupling, new mechanisms were developed for rapidly charging and discharging salt sample tubes at the vacuum sampling loop and pneumatic sample generator modules, respectively. While the charging mechanism will be deployed in FY22, the discharge mechanism was tested in FY21 and is described here. The solid salt tube transfer method was deployed at one of Argonne’s engineering-scale electrorefiners to implement at-line high-throughput pneumatic micro-sample generation capabilities. The approach was used to generate precise uranium- and lanthanide-bearing electrorefiner micro-samples with the specific dimensions requested by researchers at Los Alamos National Laboratory for use in testing their novel microcalorimeter x-ray techniques. The solid salt transfer mechanism proved not only to be an effective means of integrating the precision sample generator with vacuum sampling, but also improved the performance of the sampler generator. To discharge salt from the sample tubes at the sampler generator, tube segments were inserted directly into the sample generator’s Helmholtz chamber and pressure pulse actuations were used to generate precision molten salt samples directly from the tube segments. The direct insertion of sample tubes into the sample generator enabled rapid loading of the salt and prevented salt from contacting most of the interior surfaces of the sample generator, which eliminated cross-contamination between runs. The vacuum sampling-loop tube charging mechanism will support high-throughput tube sampling operations by employing a dynamic vacuum filling process to fill short charge tubes that are configured to be rapidly connected and disconnected from the loop. The dynamic vacuum sampling operation will be automated, and sample tube handling can be executed with simple overhead actuation. Because the modular sampling approach described here eliminates the need for new high-radiation sample handling capabilities, salt-wetted seals, salt-wetted moving parts, and heated transfer lines outside the electrorefiner, it will address most of the remaining technical challenges for the at-line deployment of high-precision analytical techniques which will enable significant reductions in the time delay for sampling-based high-precision accountancy measurements.

42 ENGINEERING↗