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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 1,189 records · Page 66
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NASA NTRS2014

NASA's Optical Measurement Program 2014

The Optical Measurements Group (OMG) within the NASA Orbital Debris Program Office (ODPO) addresses U.S. National Space Policy goals by monitoring and characterizing debris. Since 2001, the OMG has used the Michigan Orbital Debris Survey Telescope (MODEST) at Cerro Tololo Inter-American Observatory (CTIO) in Chile for general orbital debris survey. The 0.6-m Schmidt MODEST provides calibrated astronomical data of GEO targets, both catalogued and uncatalogued debris, with excellent image quality. The data are utilized by the ODPO modeling group and are included in the Orbital Debris Engineering Model (ORDEM) v. 3.0. MODEST and the CTIO/SMARTS (Small and Moderate Aperture Research Telescope System) 0.9 m both acquire filter photometric data, as well as synchronously observing targets in selected optical filters. This information provides data used in material composition studies as well as longer orbital arc data on the same target, without time delay or bias from a rotating, tumbling, or spinning target. NASA, in collaboration with the University of Michigan, began using the twin 6.5-m Magellan telescopes at Las Campanas Observatory in Chile for deep imaging (Baade) and spectroscopic data (Clay) in 2011. Through the data acquired on Baade, debris have been detected that are ~3 magnitudes fainter than detections with MODEST, while the data from Clay provide better resolved information used in material characterization analyses via selected bandpasses. To better characterize and model optical data, the Optical Measurements Center (OMC) at NASA/JSC has been in operation since 2005, resulting in a database of comparison laboratory data. The OMC is designed to emulate illumination conditions in space using equipment and techniques that parallel telescopic observations and source-target-sensor orientations. Lastly, the OMG is building the Meter Class Autonomous Telescope (MCAT) at Ascension Island. The 1.3-m telescope is designed to observe GEO and LEO targets, using a modified Ritchey-Chrétien configuration on a double horseshoe equatorial mount to allow tracking objects at LEO rates through the dome's keyhole at zenith. Through the data collection techniques employed at these unique facilities, NASA's ODPO has developed a multi-faceted approach to characterize the orbital debris risk to satellites in various altitudes and provide material characterization of debris via photometric and spectroscopic measurements. Ultimately, the data are used in conjunction with in-situ and radar measurements to provide accurate data for models of our space environment and service spacecraft risk assessment.

Cowardin, H.↗
NASA NTRS2019

Biological CubeSats: What Have We Learned so Far and What Is Next?

Since Apollo 17 in 1972, NASA has sent no humans or other biological organisms outside of Earth's protective magnetosphere. Recently, NASA has set its sights on human exploration in deep space, with an ambitous plan to put astronauts back on the Moon by 2024 and to eventually land human missions on Mars. Such missions will require significant countermeasures, likely both technological and biomedical, to protect biology from chronic radiation exposure. CubeSats can inform these countermeasures by querying relevant space environments with model organisms.NASA has launched five biological CubeSat missions into low-Earth orbit (LEO). GeneSat-1 was launched in 2006 to study gene expression and increase our knowledge of how spaceflight affects microbes. Similar life-support technologies were then used in PharmaSat and O/OREOS, which launched in 2009 and 2010, respectively. PharmaSat contained optical systems to examine how yeast cells responded to an antifungal treatment. One of O/OREOS payloads, SESLO (Space Environment Survivability of Living Organisms), housed dormant microorganisms, which were rehydrated on orbit to track alterations to growth and metabolism induced by microgravity and radiation. In 2014, NASA launched SporeSat to study the mechanisms of plant cell gravity sensing using lab-on-a-chip devices. Most recently, in 2017, NASA launched EcAMSat (E. coli AntiMicrobial Satellite), which investigated the effects of microgravity on antibiotic resistance of a pathogenic bacterium. Each one of these missions increased our understanding of the biological effects of spaceflight in LEO, while refining technologies and imparting valuable lessons to the next generation of CubeSats.CubeSats housing translational biological models are therefore ideal for defining the hazards of deep space travel, as they can provide critical data over relevant durations. BioSentinel, a next-generation deep-space CubeSat, is planned to launch as a secondary payload on Artemis 1 in 2020. BioSentinel will study the DNA damage response to deep space radiation in yeast.

Santa Maria, Sergio R.↗
NASA NTRS2022

Deep Space Atomic Clock Technology Demonstration Mission Results

The Deep Space Atomic Clock (DSAC), a NASA Technology Demonstration Mission, was launched into low-Earth orbit on June 25, 2019 as a hosted payload aboard General Atomics’ Orbital Test Bed (OTB) spacecraft. The DSAC mission has been conducting a two-year demonstration of a mercury ion atomic clock to characterize its space-based performance and to validate its utility for deep space navigation and radio science. Analysis of the collected data using JPL’s GIPSY-OASIS software has shown DSAC’s AD at one-day to be near 310-15; much better than required AD of 210-14. Such low spacecraft clock errors will enable one-way radiometric tracking data with precision equivalent to or better than current-day two way tracking data, allowing a shift to a more efficient and flexible one-way deep space navigation architecture. To verify this, an analog deep space navigation experiment was performed using JPL’s operational navigation software (Monte). The experiment recovered orbit solutions with reduced data sets and geometric variations that are more representative of deep space missions, and showed that orbit determination using DSAC derived data is on par with more traditional two-way datatypes. As a technology demonstrator, DSAC’s development focus has been on maturing the mercury ion trap clock technology rather than achieving the smallest size, weight, and power (SWaP). Over the course of DSAC’s development the project has identified numerous improvements that could be made to significantly reduce SWaP for DSAC’s next version. Indeed, DSAC-2 was recently selected by NASA for further demonstration on the VERITAS mission to Venus. This work will review the DSAC technology, mission, and results from its two-year mission.

Wang, Rabi↗
DOE OSTI2017

A New Look to Nuclear Data

Databases of evaluated nuclear data form a cornerstone on which we build academic nuclear structure physics, reaction physics, astrophysics, and many applied nuclear technologies. In basic research, nuclear data are essential for selecting, designing and conducting experiments, and for the development and testing of theoretical models to understand the fundamental properties of atomic nuclei. Likewise, the applied fields of nuclear power, homeland security, stockpile stewardship and nuclear medicine, all have deep roots requiring evaluated nuclear data. Each of these fields requires rapid and easy access to up-to-date, comprehensive and reliable databases. The DOE-funded US Nuclear Data Program is a specific and coordinated effort tasked to compile, evaluate and disseminate nuclear structure and reaction data such that it can be used by the world-wide nuclear physics community.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

G2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms Using Multi-Omics Data

Multi-omics data offers rich insights into complex traits across organisms, yet integrating and analyzing these datasets for phenotype prediction and marker discovery remains challenging. Researchers need accessible tools that combine deep learning, hyperparameter optimization, visualization, and downstream analysis in a unified web platform. To address this, we developed G2PDeep-v2, a web-based platform powered by deep learning for phenotype prediction and marker discovery from multi-omics data across a wide range of organisms, including humans and plants. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Characterization of Offshore Storage Resource Potential in the Central Planning Area of the Gulf of Mexico

This report contains a brief overview of the several studies that provide the basis for this work and details the reservoir characterization efforts that have been completed to date. Briefly, geological characterization data suggests that the deep-water reservoirs are the result of the interplay between turbidite depositional systems and salt tectonics. Project Partners have estimated the CO 2 storage capacity associated with saline reservoirs and that associated with CO 2 -Enhanced Oil Recovery (EOR) in the state waters of Louisiana. In addition, Project Partners have conducted a detailed analysis of CO 2 storage potential associated with CO 2 -EOR in the central planning area of the GOM. This work estimates that 3,140 million metric tonnes of CO 2 can be stored in optimal reservoirs of the shallow- and deep-water central GOM. A reduced order modeling and machine learning approach is introduced that will be used to evaluate CO 2 storage capacity and plume dynamics for down-selected, reservoir-specific, saline characterization. Last, a brief discussion establishes how the data presented in this report will support future work.

02 PETROLEUM↗
DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗
NASA NTRSDate not supplied

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process and down stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Prognostics↗
DOE OSTI2016

Time Dependent Data Mining in RAVEN

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The goal of this type of analyses is to understand the response of such systems in particular with respect their probabilistic behavior, to understand their predictability and drivers or lack of thereof. Data mining capabilities are the cornerstones to perform such deep learning of system responses. For this reason static data mining capabilities were added last fiscal year (FY 15). In real applications, when dealing with complex multi-scale, multi-physics systems it seems natural that, during transients, the relevance of the different scales, and physics, would evolve over time. For these reasons the data mining capabilities have been extended allowing their application over time. In this writing it is reported a description of the new RAVEN capabilities implemented with several simple analytical tests to explain their application and highlight the proper implementation. The report concludes with the application of those newly implemented capabilities to the analysis of a simulation performed with the Bison code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data and Code for Understanding Generative AI Content with Embedding Models

This repository contains code for the experiments in the paper "Understanding Generative AI Content with Embedding Models". Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representations based on domain expertise, deep neural networks (DNNs) now offer a radically different approach. DNNs implicitly engineer features by transforming their input data into hidden feature vectors called embeddings. For embedding vectors produced by foundation models -- which are trained to be useful across many contexts -- we demonstrate that simple and well-studied dimensionality-reduction techniques such as Principal Component Analysis uncover inherent heterogeneity in input data concordant with human-understandable explanations. Of the many applications for this framework, we find empirical evidence that there is intrinsic separability between real samples and those generated by artificial intelligence (AI).

Vargas, Max [Pacific Northwest National Laboratory↗
NASA NTRS1980

Voyager-Jupiter radio science data papers

The reduction and interpretation of the radio science data from the Voyager 1 and 2 encounters of the planet Jupiter and its satellites resulted in the preparation of several papers for publication in the special Voyager-Jupiter issue of the Journal of Geophysical Research. The radio science and tracking systems of the Deep Space Network provide the data which makes this research possible. This article lists submitted papers by title, with their authors and with abstracts of their contents.

Levy, G. S.↗
NASA NTRS1981

Performance of the real-time array signal combiner during the Voyager mission

During the Voyager Mission each Deep Space Station used its 64 meter antenna together with a 34 meter antenna to form an array. Data were selected from the Saturn encounter period from November 2 to November 13, 1980, to analyze the signal combiner performance. A statistical analysis of the residual gain data from all of the arrayed Deep Space Stations indicates that the Real Time Combiner is operating within the designed accuracy range of 0.2 + or - 0.05 dB around the theoretical gain. A decrease in the residual gain value appears to correlate with an increase in antenna elevation, which may be directly related to the changing antenna gain with elevation. Overall, the combined signal to noise ratio is improved by an average of 0.62 + or - 0.15 dB over the 64 meter signal to noise ratio alone.

Bartok, C. D.↗
NASA NTRS1989

Software package for performing experiments about the convolutionally encoded Voyager 1 link

A software package enabling engineers to conduct experiments to determine the actual performance of long constraint-length convolutional codes over the Voyager 1 communication link directly from the Jet Propulsion Laboratory (JPL) has been developed. Using this software, engineers are able to enter test data from the Laboratory in Pasadena, California. The software encodes the data and then sends the encoded data to a personal computer (PC) at the Goldstone Deep Space Complex (GDSC) over telephone lines. The encoded data are sent to the transmitter by the PC at GDSC. The received data, after being echoed back by Voyager 1, are first sent to the PC at GDSC, and then are sent back to the PC at the Laboratory over telephone lines for decoding and further analysis. All of these operations are fully integrated and are completely automatic. Engineers can control the entire software system from the Laboratory. The software encoder and the hardware decoder interface were developed for other applications, and have been modified appropriately for integration into the system so that their existence is transparent to the users. This software provides: (1) data entry facilities, (2) communication protocol for telephone links, (3) data displaying facilities, (4) integration with the software encoder and the hardware decoder, and (5) control functions.

Cheng, U.↗
NASA NTRSDate not supplied

Investigating Biological Responses to Deep Space Radiation for Missions Beyond Low Earth Orbit (LEO) using Yeast

To enable long-term spaceflight missions and establish habitation on the Moon and Mars, we require a comprehensive understanding of the effects of chronic deep space radiation exposure on humans. BioSentinel is NASA’s first biological CubeSat to venture beyond Low Earth Orbit (LEO). It utilizes Saccharomyces cerevisiae (budding yeast) as a model organism to study biological responses to deep space radiation. Yeast share significant genetic homology with humans, including basic cellular metabolism and DNA repair mechanisms. In addition, unlike human cell cultures, yeast can survive the duration and constraints of a deep space mission. BioSentinel measures biological responses using an optical system and alamarBlue oxidation-reduction (redox) dye. Two strains of yeast are studied - a wild-type and a rad51 mutant strain that is deficient in DNA repair. Changes in metabolism and growth are monitored throughout the nominal 6-month mission. Preliminary tests indicate a significant change in the alamarBlue response to low-dose ionizing radiation (IR). Additionally, rad51 cells have shown an IR dose-dependent decrease in glucose uptake and accumulation of oxidized NADH (NAD+). These biomolecules are involved in reactions responsible for basic cell processes, including growth and development, signaling, and respiration. The current study expanded upon previous data by exposing yeast to deep space-relevant radiation. Glucose and NADH/NAD+ assays were conducted on yeast subjected to varying dosages of high-energy Fe-56 and simulated galactic cosmic rays (GCRs). The resulting data was analyzed using Excel and GraphPad Prism. A particular focus was to identify biomolecules resulting from aerobic respiration, which requires the presence of oxygen, or anaerobic processes. As long-term spaceflight missions draw near, it is increasingly important to characterize biological processes affected by the conditions of deep space. Studying biomolecular damage caused by deep space radiation may enable the development of engineering controls or biomedical therapeutics that mitigate health complications for future astronauts.

Kyra Keenan↗
DOE OSTI2018

Preserved Neoproterozoic Continental Collision in Southeastern North America: The Brunswick Suture Zone and Osceola Continental Margin Arc

A series of exotic terranes were accreted to the eastern margin of southeastern Laurentia beginning in the Siluro-Devonian (Acadian event), and to the northeastern Laurentian margin beginning earlier (late Taconian). Many of these terranes have unclear tectonostratigraphic relationships to each other and to their parental cratons, but their accretionary history is critical to understanding the evolution of the Appalachian orogen. Two of these, the Gondwanan Suwannee and Charleston terranes, accreted during the Alleghanian orogeny and now lie beneath the Atlantic Coastal Plain in southeastern North America. Reanalysis of deep seismic reflection and well data reveals a preserved Neoproterozoic continental collision zone and associated continental margin arc, the Osceola arc, related to their juxtaposition. The subduction zone and associated strain are recorded in the newly termed Brunswick suture zone (BSZ). The BSZ is readily identified on a series of eight deep seismic reflection transects across the Brunswick Magnetic Anomaly (BMA), which we interpret as the boundary between the Charleston and Suwannee terranes. While originally interpreted to be the Late Paleozoic Alleghanian suture, new age constraints provided by the overlapping Gondwanan Paleozoic Suwannee Basin strata require the BSZ to predate the Early to Middle Paleozoic passive margin sequence of the Suwannee Basin. These results provide new insights into the tectonostratigraphic evolution of the Charleston and Suwannee terranes, the controversy surrounding the age and origin of the dipping seismic reflectors, previously attributed to the suturing of the Suwannee terrane to Laurentia, and the relationship of this suture zone to the origin of the BMA.

58 GEOSCIENCES↗
NASA NTRS1987

The mass, gravity field, and ephemeris of Mercury

In the present gravity analysis of Mariner 10/Deep Space Network radio Doppler and range data for Mercury encounters in March 1974 and March 1975, a combined least-squares fit to the Doppler data has determined two second-degree gravity harmonics that are referred to a 2439-km equatorial radius. It is noted that the 1-sigma error limits on the gravity results encompass the possibility that harmonics other than J2 and C22 significantly differ from zero. The Deep Space Network radio range data obtained with Mariner 10 are primarily applicable to such improvements of Mercury's ephemeris as the more precise determination of perihelion precession.

Anderson, John D.↗
NASA NTRS1997

Applications of Data Assimilation to Analysis of the Ocean on Large Scales

It is commonplace to begin talks on this topic by noting that oceanographic data are too scarce and sparse to provide complete initial and boundary conditions for large-scale ocean models. Even considering the availability of remotely-sensed data such as radar altimetry from the TOPEX and ERS-1 satellites, a glance at a map of available subsurface data should convince most observers that this is still the case. Data are still too sparse for comprehensive treatment of interannual to interdecadal climate change through the use of models, since the new data sets have not been around for very long. In view of the dearth of data, we must note that the overall picture is changing rapidly. Recently, there have been a number of large scale ocean analysis and prediction efforts, some of which now run on an operational or at least quasi-operational basis, most notably the model based analyses of the tropical oceans. These programs are modeled on numerical weather prediction. Aside from the success of the global tide models, assimilation of data in the tropics, in support of prediction and analysis of seasonal to interannual climate change, is probably the area of large scale ocean modeling and data assimilation in which the most progress has been made. Climate change is a problem which is particularly suited to advanced data assimilation methods. Linear models are useful, and the linear theory can be exploited. For the most part, the data are sufficiently sparse that implementation of advanced methods is worthwhile. As an example of a large scale data assimilation experiment with a recent extensive data set, we present results of a tropical ocean experiment in which the Kalman filter was used to assimilate three years of altimetric data from Geosat into a coarsely resolved linearized long wave shallow water model. Since nonlinear processes dominate the local dynamic signal outside the tropics, subsurface dynamical quantities cannot be reliably inferred from surface height anomalies. Because of its potential for large scale synoptic coverage of the deep ocean, acoustic travel time data should be a natural complement to satellite altimetry. Satellite data give us vertical integrals associated with thermodynamic and dynamic processes.

Miller, Robert N.↗
NASA NTRS1988

Space Station-based deep-space optical communication experiments

A series of three experiments proposed for advanced optical deep-space communications is described. These proposed experiments would be carried out aboard the Space Station to test and evaluate the capability of optical instruments to conduct data communication and spacecraft navigation for deep-space missions. Techniques for effective data communication, precision spacecraft ranging, and accurate angular measurements will be developed and evaluated in a spaceborne environment.

Chen, Chien-Chung↗