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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 NTRS2000

BOREAS RSS-7 LAI, Gap Fraction, and FPAR Data

The BOREAS RSS-7 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of Leaf Area Index (LAI) from remotely sensed images. Ground measurements of LAI and Fraction of Photosynthetically Active Radiation (FPAR) absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-Aug-1993 to 19-Sep-1994. The measurements were intensive at the NSA and SSA tower sites, but were made just once or twice at auxiliary sites. The final processed LAI and FPAR data set is contained in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884).

Hall, Forrest G.↗
NASA NTRS2012

LEGION: Lightweight Expandable Group of Independently Operating Nodes

LEGION is a lightweight C-language software library that enables distributed asynchronous data processing with a loosely coupled set of compute nodes. Loosely coupled means that a node can offer itself in service to a larger task at any time and can withdraw itself from service at any time, provided it is not actively engaged in an assignment. The main program, i.e., the one attempting to solve the larger task, does not need to know up front which nodes will be available, how many nodes will be available, or at what times the nodes will be available, which is normally the case in a "volunteer computing" framework. The LEGION software accomplishes its goals by providing message-based, inter-process communication similar to MPI (message passing interface), but without the tight coupling requirements. The software is lightweight and easy to install as it is written in standard C with no exotic library dependencies. LEGION has been demonstrated in a challenging planetary science application in which a machine learning system is used in closed-loop fashion to efficiently explore the input parameter space of a complex numerical simulation. The machine learning system decides which jobs to run through the simulator; then, through LEGION calls, the system farms those jobs out to a collection of compute nodes, retrieves the job results as they become available, and updates a predictive model of how the simulator maps inputs to outputs. The machine learning system decides which new set of jobs would be most informative to run given the results so far; this basic loop is repeated until sufficient insight into the physical system modeled by the simulator is obtained.

Burl, Michael C.↗
NASA NTRSDate not supplied

Feature Selection in High-Dimensional Space with Applications to Gene Expression Data

Recent years have seen rapid growth in high-dimensional datasets. Most existing machine learning (ML) algorithms fail in high-dimensional settings where many features could be redundant. A critical process of feature selection is thus applied in such a setting that helps in identifying the most relevant features while removing redundant ones. With the increase in high dimensionality, one is also faced with problems of efficiency and interpretation in performing such selection methods. Therefore, this paper proposes a “novel” feature selection framework that uses an ensemble of interpretable ML algorithms to perform feature selection and the ranking of final features. Finally, this framework is applied to a gene expression dataset obtained through collaboration with the National Aeronautics and Space Administration (NASA)’s Biological and Physical Sciences (BPS) team and helps identify important and relevant genes contributing to specific target attributes through classification tasks.

Nishan Pantha↗
DOE OSTI2025

Compactly‐Supported Nonstationary Kernels for Computing Exact Gaussian Processes on Big Data

The Gaussian process (GP) is a widely used method for analyzing large-scale data sets, including spatio-temporal measurements of nonlinear processes that are now commonplace in the environmental sciences. Traditional implementations of GPs involve stationary kernels (also termed covariance functions) that limit their flexibility, and exact methods for inference that prevent application to data sets with more than about 10,000 points. Modern approaches to address stationarity assumptions generally fail to accommodate large data sets, while all attempts to address scalability focus on approximating the Gaussian likelihood, which can involve subjectivity and lead to inaccuracies. In this work, we explicitly derive an alternative kernel that can discover and encode both sparsity and nonstationarity. We embed the kernel within a fully Bayesian GP model and leverage high-performance computing resources to enable the analysis of massive data sets. We demonstrate the favorable performance of our novel kernel relative to existing exact and approximate GP methods across a variety of synthetic data examples. Furthermore, we conduct space–time prediction based on more than 1 million measurements of daily maximum temperature and verify that our results outperform state-of-the-art methods in the Earth sciences. More broadly, having access to exact GPs that use ultra-scalable, sparsity-discovering, nonstationary kernels allows GP methods to truly compete with a wide variety of machine learning methods.

Gaussian processes↗
NASA NTRS1983

Massively parallel processor computer

An apparatus for processing multidimensional data with strong spatial characteristics, such as raw image data, characterized by a large number of parallel data streams in an ordered array is described. It comprises a large number (e.g., 16,384 in a 128 x 128 array) of parallel processing elements operating simultaneously and independently on single bit slices of a corresponding array of incoming data streams under control of a single set of instructions. Each of the processing elements comprises a bidirectional data bus in communication with a register for storing single bit slices together with a random access memory unit and associated circuitry, including a binary counter/shift register device, for performing logical and arithmetical computations on the bit slices, and an I/O unit for interfacing the bidirectional data bus with the data stream source. The massively parallel processor architecture enables very high speed processing of large amounts of ordered parallel data, including spatial translation by shifting or sliding of bits vertically or horizontally to neighboring processing elements.

Fung, L. W.↗
NASA NTRS1993

The EUVE bright source list

Initial results for bright extreme ultraviolet sources discovered during the EUVE all-sky and deep ecliptic surveys have been published as a Bright Source List (BSL) and released to the astronomical community with a recent NASA research announcement (NRA 93-OSS-02, Appendix F). This paper describes the data processing software, the EUVE survey data set, and the production of the BSL at the Center for EUV Astrophysics. The contents, format, and selection criteria for sources, the data processing strategy, some problems encountered, and a summary of the BSL results are presented.

Stroozas, B.↗
NASA NTRS2015

Addressing Human System Risks to Future Space Exploration

NASA is contemplating future human exploration missions to destinations beyond low Earth orbit, including the Moon, deep-space asteroids, and Mars. While we have learned much about protecting crew health and performance during orbital space flight over the past half-century, the challenges of these future missions far exceed those within our current experience base. To ensure success in these missions, we have developed a Human System Risk Board (HSRB) to identify, quantify, and develop mitigation plans for the extraordinary risks associated with each potential mission scenario. The HSRB comprises research, technology, and operations experts in medicine, physiology, psychology, human factors, radiation, toxicology, microbiology, pharmacology, and food sciences. Methods: Owing to the wide range of potential mission characteristics, we first identified the hazards to human health and performance common to all exploration missions: altered gravity, isolation/confinement, increased radiation, distance from Earth, and hostile/closed environment. Each hazard leads to a set of risks to crew health and/or performance. For example the radiation hazard leads to risks of acute radiation syndrome, central nervous system dysfunction, soft tissue degeneration, and carcinogenesis. Some of these risks (e.g., acute radiation syndrome) could affect crew health or performance during the mission, while others (e.g., carcinogenesis) would more likely affect the crewmember well after the mission ends. We next defined a set of design reference missions (DRM) that would span the range of exploration missions currently under consideration. In addition to standard (6-month) and long-duration (1-year) missions in low Earth orbit (LEO), these DRM include deep space sortie missions of 1 month duration, lunar orbital and landing missions of 1 year duration, deep space journey and asteroid landing missions of 1 year duration, and Mars orbital and landing missions of 3 years duration. We then assessed the likelihood and consequences of each risk against each DRM, using three levels of likelihood (Low: less than or equal to 0.1%; Medium: 0.1%–1.0%; High: greater than or equal to 1.0%) and four levels of consequence ranging from Very Low (temporary or insignificant) to High (death, loss of mission, or significant reduction to length or quality of life). Quantitative evidence from clinical, operational, and research sources were used whenever available. Qualitative evidence was used when quantitative evidence was unavailable. Expert opinion was used whenever insufficient evidence was available. Results: A set of 30 risks emerged that will require further mitigation efforts before being accepted by the Agency. The likelihood by consequence risk assessment process provided a means of prioritizing among the risks identified. For each of the high priority risks, a plan was developed to perform research, technology, or standards development thought necessary to provide suitable reduction of likelihood or consequence to allow agency acceptance. Conclusion: The HSRB process has successfully identified a complete set of risks to human space travelers on planned exploration missions based on the best evidence available today. Risk mitigation plans have been established for the highest priority risks. Each risk will be reassessed annually to track the progress of our risk mitigation efforts.

Paloski, W. H.↗
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗
DOE OSTI2023

Coherent Neutrino-Nucleus Scattering: A Tool to Search for New Physics

The Neutrino is a well-understood particle in the models of particle physics. Even so, it is electrically neutral, and has a very small mass. As such, it’s interaction probability is extremely small. Detecting neutrinos has always been a challenge for particle physics. Over 40 years ago, Daniel Freedman predicted a new type of interaction that neutrinos could undergo that would be much more frequent. This has since been called Coherent Elastic Neutrino Nucleus Scattering (CEvNS). While this process occurred more frequently than the traditionally used interaction process (called inverse beta decay) it was more challenging to detect. Interestingly, though, if you could detect the CEvNS process, you would be sensitive to many new studies of fundamental particle physics. A researcher may also be able to use this process to study neutrinos in more applied settings, for instance by more easily monitoring the neutrinos given off during the nuclear reaction processes in nuclear reactors. The aim of this project was to observe coherent neutrino scattering for the first time, and begin to use it as a tool both to study new types of physics models, and to study the feasibility of this concept for applied situations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2014

Sputtering of Lunar Regolith by Solar Wind Protons and Heavy Ions, and General Aspects of Potential Sputtering

New simulation results for the sputtering of lunar soil surface by solar-wind protons and heavy ions will be presented. Previous simulation results showed that the sputtering process has significant effects and plays an important role in changing the surface chemical composition, setting the erosion rate and the sputtering process timescale. In this new work and in light of recent data, we briefly present some theoretical models which have been developed to describe the sputtering process and compare their results with recent calculation to investigate and differentiate the roles and the contributions of potential (or electrodynamic) sputtering from the standard (or kinetic) sputtering.

Alnussirat, S. T.↗
NASA NTRS1996

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Knasel, T. Michael↗
DOE OSTI2020

Novel Membrane and Electrodeposition-Based Separation and Recovery of Rare Earth Elements from Coal Combustion Residues (Final Report)

This project developed a hydrometallurgical-based technology to extract and concentrate rare earth elements (REEs) from a representative group of coal combustion residuals (CCRs) from major coal sources in the United States. The approach for REE recovery comprises two general components: (1) Leaching of REEs from CCRs and (2) Separation of REEs from other major ions in the CCR leachate. The leaching step entails acid leaching with nitric or hydrochloric acid, and in some cases, pretreatment of CCRs with alkaline roasting prior to acid leaching. The follow-up REE recovery steps involve a series of membrane-based methods to separate and concentrate rare earth ions from other major ions in acid leachates of CCRs. These methods include liquid emulsion membranes, supported liquid membranes and electrodeposition with carbon nanotube electrochemical filters. The advantage of these separation approaches (relative to conventional approaches such as solvent extraction) is a reduction in the usage of solvents and other costly chemicals and a reduction of hazardous waste products that are challenges in conventional REE separation methods. In the testing of our process, we selected a representative set of CCR samples from our collection and performed a suite of characterization measurements (total REE content and major mineralogy). The major results of this work demonstrated that heated acid leaching could efficiently extract REEs from PRB-based coal fly ashes while the Appalachian and Illinois Basin CCRs required alkaline roasting with NaOH prior to acid leaching at room temperature. The supported liquid membrane (SLM) configuration and electrochemical deposition (ED) system yielded the most promising methods of REE recovery from the acid leachates of fly ash. Our most concentrated product to date comprised 1 wt.% (dry basis) REEs produced from an Appalachian-based fly ash leached and purified by SLM. The SLM separation process was more selective for heavy REEs over the light REEs. When SLM was combined with ED, preliminary results demonstrated that a final product of >2 wt.% REEs was possible.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1992

Data indexing techniques for the EUVE all-sky survey

This poster describes techniques developed for manipulating large full-sky data sets for the Extreme Ultraviolet Explorer project. The authors have adapted the quatrilateralized cubic sphere indexing algorithm to allow us to efficiently store and process several types of large data sets, such as full-sky maps of photon counts, exposure time, and count rates. A variation of this scheme is used to index sparser data such as individual photon events and viewing times for selected areas of the sky, which are eventually used to create EUVE source catalogs.

Lewis, J.↗
NASA NTRS2005

Phase Equilibrium Investigations of Planetary Materials

This grant provided funds to carry out phase equilibrium studies on the processes of chemical differentiation of the moon and the meteorite parent bodies, during their early evolutionary history. Several experimental studies examined processes that led to the formation of lunar ultramafic glasses. Phase equilibrium studies were carried out on selected low-Ti and high-Ti lunar ultramafic glass compositions to provide constraints on the depth range, temperature and processes of melt generation and/or assimilation. A second set of experiments examined the role of sulfide melts in core formation processes in the earth and terrestrial planets. The major results of each paper are discussed, and copies of the papers are attached as Appendix I.

Grove, T. L.↗
NASA NTRS2015

Prioritized Contact Transport Stream

A detection process, contact recognition process, classification process, and identification process are applied to raw sensor data to produce an identified contact record set containing one or more identified contact records. A prioritization process is applied to the identified contact record set to assign a contact priority to each contact record in the identified contact record set. Data are removed from the contact records in the identified contact record set based on the contact priorities assigned to those contact records. A first contact stream is produced from the resulting contact records. The first contact stream is streamed in a contact transport stream. The contact transport stream may include and stream additional contact streams. The contact transport stream may be varied dynamically over time based on parameters such as available bandwidth, contact priority, presence/absence of contacts, system state, and configuration parameters.

Hunt, Walter Lee, Jr.↗
DOE OSTI2025

Forecasting constraints on the high-z IGM thermal state from the Lyman-α forest flux autocorrelation function

ABSTRACT The autocorrelation function of the Lyman-$\alpha$ (Ly $\alpha$) forest flux from high-z quasars probes the small-scale structure of the intergalactic medium (IGM). The thermal state of the IGM, determined by the physics of reionization, sets the small-scale power observed in the Ly $\alpha$ forest. To explore the sensitivity of the autocorrelation function to the IGM’s thermal state, we compute the autocorrelation function from a cosmological hydrodynamical simulation with an instantaneous reionization model and 135 post-processed thermal states. Using mock data sets of 20 quasars, we forecast constraints on $T_0$ and $\gamma$, which characterize the post-processed IGM thermal state, at $5.4 \le z \le 6$. While this model simplifies the IGM’s thermal state, it serves as a key first step in assessing future observational prospects. We also perform an inference test on mocks and re-weight out posterior distributions to guarantee that they exhibit statistically correct behaviour. At $z = 5.4$, we find that an idealized data set constrains $T_0$ to 59 per cent and $\gamma$ to 16 per cent at the 1$\sigma$ equivalent confidence level. To explore more realistic, non-instantaneous reionization scenarios, we analyse four models combining temperature and ultraviolet background (UVB) fluctuations at $z = 5.8$. We find that mock data generated from a model with both temperature and UVB fluctuations can rule out a model with only temperature fluctuations at the $> 1\sigma$ level 73.9 per cent of the time.

Wolfson, Molly↗
NASA NTRS1995

Sea ice motions in the Central Arctic pack ice as inferred from AVHRR imagery

Synoptic observations of ice motion in the Arctic Basin are currently limited to those acquired by drifting buoys and, more recently, radar data from ERS-1. Buoys are not uniformly distributed throughout the Arctic, and SAR coverage is currently limited regionally and temporally due to the data volume, swath width, processing requirements, and power needs of the SAR. Additional ice-motion observations that can map ice responses simultaneously over large portions of the Arctic on daily to weekly time intervals are thus needed to augment the SAR and buoys data and to provide an intermediate-scale measure of ice drift suitable for climatological analyses and ice modeling. Principal objectives of this project were to: (1) demonstrate whether sufficient ice features and ice motion existed within the consolidated ice pack to permit motion tracking using AVHRR imagery; (2) determine the limits imposed on AVHRR mapping by cloud cover; and (3) test the applicability of AVHRR-derived motions in studies of ice-atmosphere interactions. Each of these main objectives was addressed. We conclude that AVHRR data, particularly when blended with other available observations, provide a valuable data set for studying sea ice processes. In a follow-on project, we are now extending this work to cover larger areas and to address science questions in more detail.

Emery, William↗