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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Improved Instrument for Detecting Water and Ice in Soil

An instrument measures electrical properties of relatively dry soils to determine their liquid water and/or ice contents. Designed as a prototype of instruments for measuring the liquid-water and ice contents of lunar and planetary soils, the apparatus could also be utilized for similar purposes in research and agriculture involving terrestrial desert soils and sands, and perhaps for measuring ice buildup on aircraft surfaces. This instrument is an improved version of the apparatus described in Measuring Low Concentrations of Liquid Water and Ice in Soil (NPO-41822), NASA Tech Briefs, Vol. 33, No. 2 (February 2009), page 22. The designs of both versions are based on the fact that the electrical behavior of a typical soil sample is well approximated by a network of resistors and capacitors in which resistances decrease and capacitances increase (and the magnitude and phase angle of impedance changes accordingly) with increasing water content. The previous version included an impedance spectrometer and a jar into which a sample of soil was placed. Four stainless-steel screws at the bottom of the jar were used as electrodes of a fourpoint impedance probe connected to the spectrometer. The present instrument does not include a sample jar and can be operated without acquiring or handling samples. Its impedance probe consists of a compact assembly of electrodes housed near the tip of a cylinder. The electrodes protrude slightly from the cylinder (see Figure 1). In preparation for measurements, the cylinder is simply pushed into the ground to bring the soil into contact with the electrodes.

Buehler, Martin↗
DOE OSTI2026

Mo 4 FeGa 17.25– x Ge x : Complementary Point Substitutions, Buffering Frameworks, and Merging of the 18-n and Octet Bonding Schemes

We present the discovery of Mo 4 FeGa 17.25–x Ge x (x ∼ 2.1(4), based on determination of Ge content), a complex gallide featuring a variety of point substitution phenomena. Its crystal structure is derived from the Ti 2 Ni type, in which a diamond network of face-sharing octahedra is interpenetrated by a second diamond network of vertex-sharing stella quadrangula. However, while in the ideal Ti 2 Ni type these two frameworks run uninterrupted through the crystal, Mo 4 FeGa 17.25–x Ge x shows three variations. First, the inner tetrahedron of every other stella quadrangula is replaced with a main group atom, creating tetrahedra reminiscent of the Zintl phase NaTl. Next, a selection of Ga 6 octahedra are filled with Fe atoms, in a manner analogous to the stuffed AuCu 3 -type phases. Finally, the refined crystal structure shows that in each unit cell a single Ga2 atom in the octahedral network is substituted with a dumbbell of Ga/Ge atoms. Electronic structure calculations on ordered models of Mo 4 FeGa 17.25–x Ge x reveal a narrow band near the Fermi energy, which is explained in terms of the 18-n and octet bonding schemes using reversed approximation Molecular Orbital analysis. A DFT-chemical pressure analysis connects the tetrahedron/atom and atom/dumbbell substitutions to the relief of atomic packing tensions and highlights soft atomic motions within the octahedral framework and driving forces for atom/dumbbell substitution. The combination of soft vibrational modes, disorder, and a narrow band gap could make this phase of interest for potential thermoelectric properties.

Chemical structure↗
DOE OSTI2021

Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning

Here, we present the first reported quantification of trace elements in plutonium via a portable laser-induced breakdown spectroscopy (LIBS) device and demonstrate the use of chemometric analysis to enhance the handheld device's sensitivity and precision. Quantification of trace elements such as iron and nickel in plutonium metal via LIBS is a challenging problem due to the complex nature of the plutonium optical emission spectra. While rapid analysis of plutonium alloys has been demonstrated using portable LIBS devices, such as the SciAps Z300, their detection limits for trace elements are severely constrained by their achievable pulse power and length, light collection optics, and detectors. In this paper, analytical methods are evaluated as a means to circumvent the detection constraints. Three chemometric methods often used in analytical spectroscopy are evaluated; principal component regression, partial least-squares regression, and artificial neural networks. These models are evaluated based on goodness-of-fit metrics, root mean-squared error, and their achievable limits of detection (LoDs). Partial least squares proved superior for determining content of iron and nickel in plutonium metal, yielding LoDs of 15 and 20 ppm, respectively. These results of identifying the undesirable trace elements in plutonium components are critical for applications such as fabricating radioisotope thermoelectric generators or nuclear fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Systematic and stochastic influences on the performance of the MinION nanopore sequencer across a range of nucleotide bias

Emerging sequencing technologies are allowing us to characterize environmental, clinical and laboratory samples with increasing speed and detail, including real-time analysis and interpretation of data. One example of this is being able to rapidly and accurately detect a wide range of pathogenic organisms, both in the clinic and the field. Genomes can have radically different GC content however, such that accurate sequence analysis can be challenging depending upon the technology used. Here, we have characterized the performance of the Oxford MinION nanopore sequencer for detection and evaluation of organisms with a range of genomic nucleotide bias. We have diagnosed the quality of base-calling across individual reads and discovered that the position within the read affects base-calling and quality scores. Finally, we have evaluated the performance of the current state-of-the-art neural network-based MinION basecaller, characterizing its behavior with respect to systemic errors as well as context- and sequence-specific errors. Overall, we present a detailed characterization the capabilities of the MinION in terms of generating high-accuracy sequence data from genomes with a wide range of nucleotide content. This study provides a framework for designing the appropriate experiments that are the likely to lead to accurate and rapid field-forward diagnostics.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2017

Estimating Surface Soil Moisture from SMAP Observations Using a Neural Network Technique

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m(exp. 3)m(exp. -3), 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m(exp. 3)m(exp. -3), 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Soil Moisture Remote Sensing↗
DOE OSTI2018

A kriging and entropy-based approach to raingauge network design

Highlights: • A kriging and entropy theory-based approach is developed for gauge network design. • The method considers both minimum estimating error andoptimum rainfall information. • The best combination is verified to be effectiveto achieve final goals. Hydrological data, such as precipitation, is fundamental for planning, designing, developing, and managing water resource projects as well as for hydrologic research. An optimal raingauge network leads to more accurate estimates of mean or point precipitation at any site over the watershed. Some studies in the past have suggested increasing gauge network density for reducing the estimation error. However, more stations mean more cost of installation and monitoring. This study proposes an approach on the basis of kriging and entropy theory to determine an optimal network design in the city of Shanghai, China. Unlike the past studies using kriging interpolation and entropy theory for network design, the approach developed in the current study not only used the kriging method as an interpolator to determine rainfall data at ungauged locations but also incorporated the minimum kriging standard error (KSE) and maximum net information (NI) content. The approach would thus lead to an optimal network and would enable the reduction of kriging standard error of precipitation estimates throughout the watershed and achieve an optimum rainfall information. This study also proposed an NI-KSE-based criterion which is dependent on a single-objective optimization. To evaluate the final optimal gauge network, areal average rainfall was estimated and its accuracy was compared with that obtained with the existing rain gauge network.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Insight into the Relationship Between Viscosity and Structure of CaO-SiO{sub 2}-MgO-Al{sub 2}O{sub 3} Molten Slags

This article elucidates the quantitative relationship between viscosity and structure in a basic slag system of CaO-SiO{sub 2}-MgO-Al{sub 2}O{sub 3} and focuses on the role of Al{sub 2}O{sub 3}. Slag viscosity was measured by the rotating cylinder method, and structural information was obtained using Fourier transformation infrared, Raman and magic angular spinning nuclear magnetic resonance (MAS-NMR) techniques. The results show that, as the Al{sub 2}O{sub 3} content increased, slag viscosity increased initially and decreased afterwards, directly indicating that Al{sub 2}O{sub 3} had an amphoteric effect on slag viscosity. The Raman spectra verified that with increasing Al{sub 2}O{sub 3} content, the concentrations of Q{sup 0}(Si) and Q{sup 2}(Si) decreased first and then increased, while that of Q{sup 1}(Si) kept increasing and that of Q{sup 3}(Si) increased first and then decreased. The {sup 27}Al MAS-NMR spectra proved that the mole ratios of AlO{sub 5} and AlO{sub 6} to AlO{sub 4} kept increasing with the increase of Al{sub 2}O{sub 3} content, and, overall, Al{sub 2}O{sub 3} changed from a network former to a network modifier. The relationship between the viscosity and structure of the molten slags was further analyzed quantitatively based on the modified (NBO/T), denoted as (NBO/T)′, and we found a fine linear correlation between the logarithm of viscosity and (NBO/T)′. Moreover, the variations of thermodynamic properties of this system also indirectly supported the present experimental results.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Determination of aerosol content in the atmosphere from ERTS-1 data

The author has identified the following significant results. Significant results have been obtained using the digital data from ERTS-1. A linear relationship, as predicted by theory, has been shown to exist between the MSS radiances over water surfaces and the aerosol content of the atmosphere measured with a ground-based Volz sun photometer. The relationship, based on seven data points, appears best for MSS 6. This relationship will enable the atmospheric aerosol content to be monitored on a global basis, once it has been verified with further measurements, and should provide the information with considerable cost-savings over a ground-based photometer network.

Griggs, M.↗
NASA NTRS1994

(abstract) Tropospheric Calibration for the Mars Observer Gravity Wave Experiment

In spring 1993, microwave radiometer-based tropospheric calibration was provided for the Mars Observer gravitational wave search. The Doppler shifted X-band radio signals propagating between Earth and the Mars Observer satellite were precisely measured to determine path length variations that might signal passage of gravitational waves. Experimental sensitivity was restricted by competing sources of variability in signal transit time. Principally, fluctuations in the solar wind and ionospheric plasma density combined with fluctions in tropospheric refractivity determined the detection limit. Troposphere-induced path delay fluctions are dominated by refractive changes caused by water vapor inhomogeneities blowing through the signal path. Since passive microwave remote sensing techniques are able to determine atmospheric propagation delays, radiometer-based tropospheric calibration was provided at the Deep Space Network Uranus tracking site (DSS-15). Two microwave water vapor radiometers (WVRs), a microwave temperature profiler (MTP), and a ground based meterological station were deployed to determine line-of-sight vapor content and vertical temperature profile concurrently with Mars Observer tracking measurements. This calibration system provided the capability to correct Mars Observer Doppler data for troposphere-induced path variations. We present preliminary analysis of the Doppler and WVR data sets illustrating the utility of WVRs to calibrate Doppler data. This takes an important step toward realizing the ambitious system required to support future Ka-band Cassini satellite gravity wave tropospheric calibration system.

atmospheric water vapor microwave remote sensing↗
DOE OSTI2014

Gene co-expression network analysis in Rhodobacter capsulatus and application to comparative expression analysis of Rhodobacter sphaeroides

The genus Rhodobacter contains purple nonsulfur bacteria found mostly in freshwater environments. Representative strains of two Rhodobacter species, R. capsulatus and R. sphaeroides, have had their genomes fully sequenced and both have been the subject of transcriptional profiling studies. Gene co-expression networks can be used to identify modules of genes with similar expression profiles. Functional analysis of gene modules can then associate co-expressed genes with biological pathways, and network statistics can determine the degree of module preservation in related networks. In this paper, we constructed an R. capsulatus gene co-expression network, performed functional analysis of identified gene modules, and investigated preservation of these modules in R. capsulatus proteomics data and in R. sphaeroides transcriptomics data. Results: The analysis identified 40 gene co-expression modules in R. capsulatus. Investigation of the module gene contents and expression profiles revealed patterns that were validated based on previous studies supporting the biological relevance of these modules. We identified two R. capsulatus gene modules preserved in the protein abundance data. We also identified several gene modules preserved between both Rhodobacter species, which indicate that these cellular processes are conserved between the species and are candidates for functional information transfer between species. Many gene modules were non-preserved, providing insight into processes that differentiate the two species. In addition, using Local Network Similarity (LNS), a recently proposed metric for expression divergence, we assessed the expression conservation of between-species pairs of orthologs, and within-species gene-protein expression profiles. Conclusions: Our analyses provide new sources of information for functional annotation in R. capsulatus because uncharacterized genes in modules are now connected with groups of genes that constitute a joint functional annotation. We identified R. capsulatus modules enriched with genes for ribosomal proteins, porphyrin and bacteriochlorophyll anabolism, and biosynthesis of secondary metabolites to be preserved in R. sphaeroides whereas modules related to RcGTA production and signalling showed lack of preservation in R. sphaeroides. In addition, we demonstrated that network statistics may also be applied within-species to identify congruence between mRNA expression and protein abundance data for which simple correlation measurements have previously had mixed results.

Comparative transcriptomics, Module preservation, ↗
DOE OSTI2017

Synthesis and analysis of separation networks for the recovery of intracellular chemicals generated from microbial-based conversions

Background. Bioseparations can contribute to more than 70% in the total production cost of a bio-based chemical, and if the desired chemical is localized intracellularly, there can be additional challenges associated with its recovery. Based on the properties of the desired chemical and other components in the stream, there can be multiple feasible options for product recovery. These options are composed of several alternative technologies, performing similar tasks. The suitability of a technology for a particular chemical depends on (1) its performance parameters, such as separation efficiency; (2) cost or amount of added separating agent; (3) properties of the bioreactor effluent (e.g., biomass titer, product content); and (4) final product specifications. Our goal is to first synthesize alternative separation options and then analyze how technology selection affects the overall process economics. To achieve this, we propose an optimization-based framework that helps in identifying the critical technologies and parameters. Results. We study the separation networks for two representative classes of chemicals based on their properties. The separation network is divided into three stages: cell and product isolation (stage I), product concentration (II), and product purification and refining (III). Each stage exploits differences in specific product properties for achieving the desired product quality. The cost contribution analysis for the two cases (intracellular insoluble and intracellular soluble) reveals that stage I is the key cost contributor (>70% of the overall cost). Further analysis suggests that changes in input conditions and technology performance parameters lead to new designs primarily in stage I. Conclusions. The proposed framework provides significant insights for technology selection and assists in making informed decisions regarding technologies that should be used in combination for a given set of stream/product properties and final output specifications. Additionally, the parametric sensitivity provides an opportunity to make crucial design and selection decisions in a comprehensive and rational manner. This will prove valuable in the selection of chemicals to be produced using bioconversions (bioproducts) as well as in creating better bioseparation flow sheets for detailed economic assessment and process implementation on the commercial scale.

09 BIOMASS FUELS↗
DOE OSTI2023

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

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↗
DOE OSTI2022

It takes a village: using a crowdsourced approach to investigate organic matter composition in global rivers through the lens of ecological theory

Though community-based scientific approaches are becoming more common, many scientific efforts are conducted by small groups of researchers that together develop a concept, analyze data, and interpret results that ultimately translate into a publication. Here, we present a community effort that breaks these traditional boundaries of the publication process by engaging the scientific community from initial hypothesis generation to final publication. We leverage community-generated data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium to study organic matter composition through the lens of ecological theory. This community endeavor will use a suite of paired physical and chemical datasets collected from 97 river corridors across the globe. With our first step aimed at ideation, we engaged a community of scientists from 20 countries and 60 institutions, spanning disciplines and career stages by holding a virtual workshop (April 2021). In the workshop, participants generated content for questions, hypotheses, and proposed analyses based on the WHONDRS dataset. These ideation efforts resulted in several narratives investigating different questions led by different teams, which will be the basis for research articles in a Frontiers in Water collection. Currently, the community is collectively analyzing, interpreting, and synthesizing these data that will result in seven crowdsourced articles using a single, existing WHONDRS dataset. The use of a shared dataset across articles not only lowers barriers for broad participation by not requiring generation of new data, but also provides unique opportunities for emergent learning by connecting outcomes across studies. Here we will explain methods used to enable this community endeavor aimed to promote a greater diversity of thinking on river corridor biogeochemistry through community science.

Borton, Mikayla A.↗
NASA NTRSDate not supplied

A Process Model of Interpersonal Relationship Formation in Isolated and Confined Environments

Future space crews will face several challenges such as living and working in a confined environment, isolated from others. These circumstances increase the importance of interpersonal compatibility, teamwork, and team performance. The interpersonal compatibility of space crews has been and continues to be of interest to both NASA and the Institute of Biomedical Problems (IBMP), whose research informs operations for Roscosmos. In NASA-sponsored research, interpersonal compatibility has been examined in terms of how the combination of team members’ personality traits, values, and demographics shape team member relationships and team performance overtime. IBMP-sponsored research mostly has moved away from trait-based approaches toward an idiographic (in-depth, heavily descriptive) approach to researching crew interpersonal relations. This research uses software such as Personal Self-Perception and Attitudes (PSPA), network approaches to team member relations, and content analysis of interactions to assess interpersonal compatibility, and infer states and team dynamics. Our research program integrated these ideas. Our primary research aim was to develop and empirically test a process model of interpersonal relationship formation in isolated and confinement environments. We created a model, collected data from teams in an isolated and confined environment, and applied a novel analytical strategy that combines trait, state, and interaction data (i.e., relational events). We focused specifically on the formation of strained relationships in isolation and confinement—or with whom fellow crewmembers find it difficult to work.

S T Bell↗
DOE OSTI2024

Understanding Generative AI Content with Embedding Models

The construction of high-quality numerical features is critical to any quantitative data analysis. Feature engineering has been historically addressed by carefully hand-crafting data representations based on domain expertise. This work views the internal representations of modern deep neural networks (DNNs), called embeddings, as an implicit form of traditional feature engineering. For trained DNNs, we show that these embeddings can reveal interpretable, high-level concepts in unstructured sample data. We use these embeddings in natural language and computer vision tasks to uncover both inherent heterogeneity in the underlying data and human-understandable explanations for it. In particular, we find empirical evidence that there is inherent separability between real data and those generated from AI models.

Vargas, Max↗
DOE OSTI2014

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation and Completion of Episodic Information

Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach by which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.

Aimone, James Bradley↗