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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 NTRS1982

Precision of spiral-bevel gears

The kinematic errors in spiral bevel gear trains caused by the generation of nonconjugate surfaces, by axial displacements of the gears during assembly, and by eccentricity of the assembled gears were determined. One mathematical model corresponds to the motion of the contact ellipse across the tooth surface, (geometry I) and the other along the tooth surface (geometry II). The following results were obtained: (1) kinematic errors induced by errors of manufacture may be minimized by applying special machine settings, the original error may be reduced by order of magnitude, the procedure is most effective for geometry 2 gears, (2) when trying to adjust the bearing contact pattern between the gear teeth for geometry 1 gears, it is more desirable to shim the gear axially; for geometry II gears, shim the pinion axially; (3) the kinematic accuracy of spiral bevel drives are most sensitive to eccentricities of the gear and less sensitive to eccentricities of the pinion. The precision of mounting accuracy and manufacture are most crucial for the gear, and less so for the pinion.

Litvin, F. L.↗
NASA NTRS1983

Precision of spiral-bevel gears

The kinematic errors in spiral bevel gear trains caused by the generation of nonconjugate surfaces, by axial displacements of the gears during assembly, and by eccentricity of the assembled gears were determined. One mathematical model corresponds to the motion of the contact ellipse across the tooth surface, (geometry I) and the other along the tooth surface (geometry II). The following results were obtained: (1) kinematic errors induced by errors of manufacture may be minimized by applying special machine settings, the original error may be reduced by order of magnitude, the procedure is most effective for geometry 2 gears, (2) when trying to adjust the bearing contact pattern between the gear teeth for geometry I gears, it is more desirable to shim the gear axially; for geometry II gears, shim the pinion axially; (3) the kinematic accuracy of spiral bevel drives are most sensitive to eccentricities of the gear and less sensitive to eccentricities of the pinion. The precision of mounting accuracy and manufacture are most crucial for the gear, and less so for the pinion. Previously announced in STAR as N82-30552

Litvin, F. L.↗
NASA NTRS2022

Local Scale (3-M) Soil Moisture Mapping Using SMAP and Planet Superdove

A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10s of kilometers. Hence a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine- learning (ML) and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing ab- solute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in-situ soil moisture measurements for the Australian Yanco region, while preserving the high sampling frequency (1-3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.

soil moisture↗
NASA NTRS2009

Progressive Classification Using Support Vector Machines

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.

Wagstaff, Kiri↗
NASA NTRSDate not supplied

New Tools for Automating Arcjet Sample Recession Tracking and Analysis

Arcjet Computer Vision (arcjetCV) has been significantly upgraded to enhance accuracy and performance in tracking material recession and shock-material standoff in test videos. These improvements include integrating new machine learning models, developing a specialized edge detection class, and incorporating a more comprehensive training dataset. These upgrades have refined the software’s ability to automate time-resolved recession tracking, making it more precise and reliable for analyzing complex physical processes. In parallel, a new tool called STARscan (Spatial Targeting and Alignment Rig for Scanning) is being developed to capture detailed 3D surface data before and after testing. By comparing these pre- and post-test scans with arcjetCV’s automated video analysis results, users can achieve a more comprehensive assessment of material recession. This method enables cross-validation of results, improving confidence in the analysis of tested materials. The expanded capabilities of arcjetCV have been successfully demonstrated on videos from various facilities, including the NASA Ames arcjets, UIUC’s PlasmatronX, and the VKI Plasmatron. It has been adopted as a new standard for in-situ recession tracking by the Mars Sample Return Project and Orion. ArcjetCV’s improved efficiency and accuracy are critical for reducing testing uncertainties and validating heatshield material performance under extreme conditions. The software’s user-friendly graphical interface ensures ease of use, enabling seamless processing and precise analysis of arcjet videos, providing deeper insights into material behavior in hypersonic environments. ArcjetCV is now available on both PyPI and Conda, allowing easy installation via "pip install arcjetCV" or through the Conda package manager, ensuring broad accessibility and streamlined deployment for users across various platforms.

Ablation↗
NASA NTRS1981

Multibeam collimator uses prism stack

Optical instrument creates many divergent light beams for surveying and machine element alignment applications. Angles and refractive indices of stack of prisms are selected to divert incoming laser beam by small increments, different for each prism. Angles of emerging beams thus differ by small, precisely-controlled amounts. Instrument is nearly immune to vibration, changes in gravitational force, temperature variations, and mechanical distortion.

Minott, P. O.↗
NASA NTRS2018

Application of Machine-Learning Algorithms for On-Board Asteroid Shape Model Determination

The Application of Machine-learning Algorithms for On-board Asteroid Shape Model Determination project will develop an innovative system for spacecraft navigation to expand the capability of small spacecraft to meet the critical challenges associated with small-body exploration. Such challenges include accurate navigation in a microgravity environment and precision targeting of particular locations on an asteroid surface for sample collection. This on-board system will cut the computational "umbilical" back to Earth-currently necessary for the generation of a global shape model that requires thousands of images with sufficient resolution and adequate variation of incidence and emission angles, processed manually by a team of experts on Earth for several months. Small satellites have limited bandwidth and are unable to downlink the data volume required for this processing, restricting their ability to perform deep-space asteroid exploration.

Machine learning algorithms↗
NASA NTRS1977

Real time computer controlled weld skate

A real time, adaptive control, automatic welding system was developed. This system utilizes the general case geometrical relationships between a weldment and a weld skate to precisely maintain constant weld speed and torch angle along a contoured workplace. The system is compatible with the gas tungsten arc weld process or can be adapted to other weld processes. Heli-arc cutting and machine tool routing operations are possible applications.

Wall, W. A., Jr.↗
NASA NTRS2004

Triphasic Tooling with Small Oriented Diamond Tip for Turning and Smoothing Lightweight Mirrors

We are developing a new method for the growth of small diamond crystals at very high temperatures and pressures directly from a carbon melt. A prototype "Supercell" has been developed for this purpose. This system is capable of high rate crystal growth in relatively large working volumes. The resulting high quality diamond crystals will be incorporated into a triphasic diamond/titanium carbide/titanium composite tool, with an oriented diamond crystal at its tip. High pressure is needed to prevent degradation of diamond at high temperature, and to ensure the formation of a crack & composite structure. After grinding and polishing, the composite material will be joined to a steel holder, thus forming a diamond-tipped tool for turning and smoothing of a mirror surface. A properly oriented single-crystal diamond cuts and smoothes much better than a conventional polycrystalline diamond crystal. This is because the hardness depends on crystallographic orientation-the difference corresponds to 60-100 GPa on the Knoop scale. Our goal is to achieve surface roughness of about 1 nm, which will be accomplished by precision cutting and smoothing. The hardness of the functionally-graded diamond/titanium carbide/titanium composite tool varies from 100 GPa at its tip to 15 GPa at its base. Previous work has shown that the mass of machined material using an oriented-diamond tool is much larger than that for a standard diamond-metal composite tool.

Voronov, O. A.↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗
NASA NTRS1990

A new fabrication method for precision antenna reflectors for space flight and ground test

The use of higher frequencies on communications satellites has led to the requirement for increasingly precise antenna reflectors for use in space. Typical industry fabrication methods for space antenna reflectors employ successive molding techniques for reflector face sheets and a final fit-up to a master mold in order to achieve the required accuracies. However, new missions at much higher frequencies will require greater accuracies than may be achievable using these present methods. A new approach for the fabrication of ground-test antenna reflectors is to machine the reflective surface into a stainless steel and fiberglass composite structure. A 2.7-m diameter ground-test antenna reflector fabricated using this method has an accuracy of better than 0.013 mm (0.0005 in.) rms. A similar design concept for a solid surface reflector for use on spacecraft would involve fabrication in a similar manner but using space-qualified materials. This report describes the design, analysis, and fabrication of the 2.7-m-diameter precision antenna reflector for antenna ground test and the extension of this technology to precision space antenna reflectors.

Sharp, G. Richard↗
NASA NTRS1990

Removing Dross From Molten Solder

Automatic device helps to assure good solder connections. Machine wipes dross away from area on surface of molten solder in pot. Sweeps across surface of molten solder somewhat in manner of windshield wiper. Each cycle of operation triggered by pulse from external robot. Equipment used wherever precise, automated soldering must be done to military specifications.

Webb, Winston S.↗
NASA NTRS2005

Alignment Stage for a Cryogenic Dilatometer

A three-degree-of-freedom alignment stage has been designed and built for use in a cryogenic dilatometer that is used to measure thermal strains. The alignment stage enables precise adjustments of the positions and orientations of optical components to be used in the measurements and, once adjustments have been completed, keeps the components precisely aligned during cryogenic-dilatometer operations that can last as long as several days. The alignment stage includes a case, a circular tilt/tip platform, and a variety of flexural couplings between the case and the platform, all machined from a single block of the low-thermal-expansion iron/nickel alloy Invar, in order to minimize effects of temperature gradients and to obtain couplings that are free of stiction and friction. There are three sets of flexural couplings clocked at equal angles of 120 degrees around the platform, constituting a three-point kinematic support system.

Dudik, Matthew↗
NASA NTRS1998

Molecular Nanotechnology and Space Settlement

Atomically precise manipulation of matter is becoming increasingly common in laboratories around the world. As this control moves into aerospace systems, huge improvements in computers, high-strength materials, and other systems are expected. For example, studies suggest that it may be possible to build: 10(exp 18) MIPS computers, 10(exp 15) bytes/sq cm write once memory, $153-412/kg-of-cargo single- stage-to-orbit launch vehicles and active materials which sense their environment and react intelligently. All of NASA's enterprises should benefit significantly from molecular nanotechnology. Although the time may be measured in decades and the precise path to molecular nanotechnology is unclear, all paths (diamondoid, fullerene, self-assembly, biomolecular, etc.) will require very substantial computation. This talk will discuss fullerene nanotechnology and early work on hypothetical active materials consisting of large numbers of identical machines. The speaker will also discuss aerospace applications, particularly missions leading to widespread space settlement (e.g., small near-Earth - object retrieval). It is interesting to note that control of the tiny - individual atoms and molecules - may lead to colonization of the huge -first the solar system, then the galaxy.

Globus, Al↗
NASA NTRSDate not supplied

Center for Isotope Cosmochemistry and Geochronology at NASA Johnson Space Center

Ten years in the making the Center for Isotope Cosmochemistry and Geochronology (CICG) at NASA Johnson Space Center is designed to be a cooperative analytical facility where novel techniques are developed and performed. Our team works collaboratively to operate this integrated lab suite comprising sample preparation, metal-free clean chemistry, and mass spectrometers to measure a wide variety of elements and isotopes in astromaterials. Contamination control is enacted throughout by a positive pressure cascade of HEPA filtered air, weekly surface cleaning and particle counting. Astromaterials processing is performed with application-specific mortar and pestles, heavy liquid separation, a Frantz magnetic separator, as well as hand-picking using optical microscopes in laminar flow boxes. Microsampling is also carried out using a New Wave MicroMill. Elements for isotopic analysis are isolated from acid-digested samples using ion-exchange chromatography. Cross-contamination is minimized with dedicated laminar flow boxes and labware for different purification protocols. Instrumentation includes a ThermoFisher Element XR inductively coupled plasma mass spectrometer (ICP-MS) for quantification of elemental concentrations in dissolved sample solutions. For in situ analysis, a Teledyne Photon Machines Analyte Excite+ laser ablation system is coupled to the Element XR. The ThermoFisher Triton thermal ionization mass spectrometer (TIMS) is used to measure the isotopic composition of elements such as CA, K, Cr, Rb, Sr, Sm, Nd at high precision in processed samples. For a more diverse range of isotopic analyses, CICG houses a Nu Sapphire 1700 multi-collector inductively coupled plasma mass spectrometer (MC-ICP-MS), which is a large geometry instrument with a multi-pole collision cell for interference removal. An Applied Spectra Instruments J200 femto-second laser is planned for install this Fall and will be coupled to the Nu Sapphire 1700. NASA’s Internal Scientist Funding Model (ISFM)supports this facility. Advanced analytical geochemistry techniques are applied to targeted astromaterials to address important problems in planetary sciences, with a longer-term goal of preparing for samples collected and returned by space missions. CICG is open to collaborations on projects that utilize the facility’s unique capabilities and are within its research scope. We are particularly eager to support and share expertise with the next generation of planetary scientists.

Justin I Simon↗
NASA NTRS2022

Design of a Low-Cost, Submersible, Digital Holographic Microscope for in Situ Microbial Imaging

The methodologies for studying marine microbiology typically consist of utilizing instrumentation within a laboratory. This typically requires extracting a sample from its place of origin prior to examination, which may be days after collection. Oftentimes, the solution is to bring the lab to the ocean, which may be costly and provide further limitations for a sterile and stable laboratory environment. Here we present a low-cost, submersible, digital holographic microscope (DHM) designed to image marine microorganisms (such as bacteria and plankton) in their natural underwater environment. Our instrument eliminates the need to transport samples and allows for instantaneous data collection of microbes in-situ. The DHM achieves sub-micron spatial resolution and is paired with artificial intelligence for the detection and tracking of specimens to reduce the overall collected data. This instrument also aims to reduce the cost of manufacturing and field expenses relative to marine microbiological research. “Off the shelf” components were selected in the design process of this instrument which allows us to achieve precise results without sacrificing data quality. The DHM itself costs under one thousand dollars and features a low-cost high-resolution camera, the Arducam MT9J001. Included in the design were five main subsystems: optical, mechanical, electrical, power, and machine learning. Our on board computer and artificial intelligence consist of a Raspberry Pi 4 (8 Gb) and Google Coral USB Tensorflow accelerator. Instrument testing has successfully proven our abilities of data acquisition for at least two hours in depths of at least forty meters below sea level. Additionally, our artificial intelligence system is currently capable of tracking up to ten areas of interest in a fraction of a second with over ninety percent confidence via the neural net driven by the tensor cores on the Google Coral. Furthermore, we have demonstrated the versatility of our instrument by mounting it on an ocean-going ROV, the BlueRov2 by BlueRobotics. Our tests on the BlueRov2 exemplified the cost-effective nature of a submersible and reusable instrument that can be implemented in moderate environments and on most vessels.

Wallace, James Kent↗
NASA NTRS2021

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤ 15 cGy of individual Galactic Cosmic Radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in Attentional Set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the Simple Discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the Compound Discrimination (CD) stage. On a subject-based level, implementing Machine Learning (ML) classifiers such as the Gaussian Naïve Bayes, Support Vector Machine, and Artificial Neural Networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreenperformance scores as multidimensional input features to predict each rodent’s susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Feis the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4Hein SD and 28Siin CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for morerobust predictionsof cognitive decrements due to space radiation exposure.

space radiation↗
NASA NTRS1984

Cooperative control - The interface challenge for men and automated machines

The research issues associated with the increasing autonomy and independence of machines and their evolving relationships to human beings are explored. The research, conducted by Langley Research Center (LaRC), will produce a new social work order in which the complementary attributes of robots and human beings, which include robots' greater strength and precision and humans' greater physical and intellectual dexterity, are necessary for systems of cooperation. Attention is given to the tools for performing the research, including the Intelligent Systems Research Laboratory (ISRL) and industrial manipulators, as well as to the research approaches taken by the Automation Technology Branch (ATB) of LaRC to achieve high automation levels. The ATB is focusing on artificial intelligence research through DAISIE, a system which tends to organize its environment into hierarchical controller/planner abstractions.

Hankins, W. W., III↗