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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 NTRS1999

Integrated Aeroservoelastic Optimization: Status and Direction

The interactions of lightweight flexible airframe structures, steady and unsteady aerodynamics, and wide-bandwidth active controls on modern airplanes lead to considerable multidisciplinary design challenges. More than 25 years of mathematical and numerical methods' development, numerous basic research studies, simulations and wind-tunnel tests of simple models, wind-tunnel tests of complex models of real airplanes, as well as flight tests of actively controlled airplanes, have all contributed to the accumulation of a substantial body of knowledge in the area of aeroservoelasticity. A number of analysis codes, with the capabilities to model real airplane systems under the assumptions of linearity, have been developed. Many tests have been conducted, and results were correlated with analytical predictions. A selective sample of references covering aeroservoelastic testing programs from the 1960s to the early 1980s, as well as more recent wind-tunnel test programs of real or realistic configurations, are included in the References section of this paper. An examination of references 20-29 will reveal that in the course of development (or later modification), of almost every modern airplane with a high authority active control system, there arose a need to face aeroservoelastic problems and aeroservoelastic design challenges.

Livne, Eli↗
NASA NTRS2010

The Influence of Friction Stir Weld Tool Form and Welding Parameters on Weld Structure and Properties: Nugget Bulge in Self-Reacting Friction Stir Welds

Although friction stir welding (FSW) was patented in 1991, process development has been based upon trial and error and the literature still exhibits little understanding of the mechanisms determining weld structure and properties. New concepts emerging from a better understanding of these mechanisms enhance the ability of FSW engineers to think about the FSW process in new ways, inevitably leading to advances in the technology. A kinematic approach in which the FSW flow process is decomposed into several simple flow components has been found to explain the basic structural features of FSW welds and to relate them to tool geometry and process parameters. Using this modelling approach, this study reports on a correlation between the features of the weld nugget, process parameters, weld tool geometry, and weld strength. This correlation presents a way to select process parameters for a given tool geometry so as to optimize weld strength. It also provides clues that may ultimately explain why the weld strength varies within the sample population.

Schneider, Judy↗
NASA NTRS2017

Remote Sensing of Multiple Cloud Layer Heights Using Multi-Angular Measurements

Cloud top height (CTH) affects the radiative properties of clouds. Improved CTH observations will allow for improved parameterizations in large-scale models and accurate information on CTH is also important when studying variations in freezing point and cloud microphysics. NASAs airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. For the determination of CTH, a set of consecutive nadir reflectances is selected and the cross-correlations between this set and co-located sets at other viewing angles are calculated for a range of assumed cloud top heights, yielding a correlation profile. Under the assumption that cloud reflectances are isotropic, local peaks in the correlation profile indicate cloud layers. This technique can be applied to every RSP footprint and we demonstrate that detection of multiple peaks in the correlation profile allow retrieval of heights of multiple cloud layers within single RSP footprints. This paper provides an in-depth description of the architecture and performance of the RSPs CTH retrieval technique using data obtained during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC(exp. 4)RS) campaign. RSP retrieved cloud heights are evaluated using collocated data from the Cloud Physics Lidar (CPL). The method's accuracy associated with the magnitude of correlation, optical thickness, cloud thickness and cloud height are explored. The technique is applied to measurements at a wavelength of 670 nm and 1880 nm and their combination. The 1880-nm band is virtually insensitive to the lower troposphere due to strong water vapor absorption.

Sinclair, Kenneth↗
NASA NTRSDate not supplied

Development of Computational Environmental Microbiome Workflows for the Laboratory and the International Space Station

Identification of microorganisms in the spaceflight environment is critical for crew health risk assessment on the International Space Station (ISS). Since 2017, nanopore sequencing technology has been used to support thein situ identification of microbial species during spaceflight. Beginning in 2018, a culture-independent, swab-to-sequencer method was implemented onboard the ISS to provide a more thorough insight of the ISS microbiome. Eliminating microbial culture enables identification of difficult-to-culture organisms, reduces risks associated with potentially pathogenic cultures, and could significantly reduce the time from sample-to-answer. However, this molecular-based approach generates large metagenomic datasets that require substantial computational resources for analysis. To process nanopore-generated sequencing data, the JSC Microbiology Laboratory established a bioinformatics workflow on Amazon EC2 under the security guidance of the NASA Science Managed Cloud Environment (SMCE).This resource allows for the development, testing, and accessing of computational tools for processing large and complex datasets. The work described here will address the downlinking of data from the ISS, the automated pipeline developed to identify targeted bacterial and fungal organisms, and the time from sampling onboard to microbial identification. The pipelines have been enhanced to address high and low biomass samples using optimization based on sample source (air, water, or surface) and type of collection (filter, colony, or swab).The resulting microbiome data can be assessed beyond microbial identifications to gain understanding toward population changes over time, potential selective environmental pressures, and evaluating correlations with a wide range of additional data sets. Metagenome analysis pipelines in development could allow for simultaneous identification of microbial species, gene function, and gene pathways present in the environment. Beyond the ground processing, the developed analysis pipeline is currently deployed onboard the ISS to allow for near real-time assessments of the ISS microbiome. This study serves as a critical foundation for exploration missions, where rapid microbiome analyses will be required.

G. Marie Sharp↗
NASA NTRS2024

Cosmological Constraints From the Tomography of DES-Y3 Galaxies With CMB Lensing From ACT DR4

We present a measurement of the cross-correlation between the MAGLIM galaxies selected from the Dark Energy Survey (DES) first three years of observations (Y3) and cosmic microwave background (CMB) lensing from the Atacama Cosmology Telescope (ACT) Data Release 4 (DR4), reconstructed over ∼ 436 deg 2 of the sky. Our galaxy sample, which covers ∼ 4143 deg 2 , is divided into six redshift bins spanning the redshift range of 0.20 8 ≡ σ 8 (Ω m /0.3) 0.5 = 0.75 +0.04 -0.05 . In addition, we constrain the linear growth of cosmic structure as a function of redshift. Our results are consistent with recent DES Y3 analyses and suggest a preference for a lower S 8 compared to results from measurements of CMB anisotropies by the Planck satellite, although at a mild level (< 2σ) of statistical significance.

cosmological parameters from CMBR↗
DOE OSTI2018

Introducing GEOPHIRES v2.0: Updated Geothermal Techno-Economic Simulation Tool: Preprint

This paper presents an updated version of the geothermal techno-economic simulation tool GEOPHIRES (GEOthermal Energy for Production of Heat and electricity (IR) Economically Simulated). GEOPHIRES combines reservoir, wellbore, surface plant and economic models to estimate the capital, and operation and maintenance costs, lifetime energy production, and overall levelized cost of energy of a geothermal plant. The available end-use options are electricity, direct-use heat and cogeneration. The main updates in the new version include conversion of the source code from FORTRAN to Python, the option to couple to an external reservoir simulator, updated cost correlations, and more flexibility in selecting the time step and number of injection and production wells. An overview of all the updates and two case-studies to illustrate the tool's new capabilities are provided in this paper.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Introducing GEOPHIRES v2.0: Updated Geothermal Techno-Economic Simulation Tool

This paper presents an updated version of the geothermal techno-economic simulation tool GEOPHIRES (GEOthermal energy for Production of Heat and electricity ('IR') Economically Simulated). GEOPHIRES combines engineering models of the reservoir, wellbores, and surface plant facilities of a geothermal plant with an economic model to estimate the capital and operation and maintenance costs, lifetime energy production, and overall levelized cost of energy. The available end-use options are electricity, direct-use heat, and cogeneration. The main updates in the new version include conversion of the source code from FORTRAN to Python, the option to import temperature data (e.g., measured or from stand-alone reservoir simulator), updated cost correlations, and more flexibility in selecting the time step and number of injection and production wells. In this paper, we provide an overview of all the updates and two case studies to illustrate the tool's new capabilities.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Advantage of multi-band solar-induced chlorophyll fluorescence to derive canopy photosynthesis in a temperate forest

When solar-induced chlorophyll fluorescence (SIF) is developed as a novel approach to quantify gross primary production (GPP), narrow SIF emission spectrum around the atmospheric oxygen absorption windows (˜ 761 nm and ˜687 nm) has been widely used to represent SIF and to derive GPP. As SIF is a continuous spectrum, deriving the full broadband SIF emission spectrum over 640–850 nm provides an opportunity to fully explore the potential of SIF in estimating GPP. Using high-frequency measurements of canopy carbon flux and SIF emissions at the atmospheric absorption bands, here we reconstruct the full SIF spectrum from SIF signals at the absorption bands, and then analyze correlations between the GPP and the selected single SIF bands and their combinations using both linear regression (LR) and Gaussian processes regression (GPR). Our results indicate that (1) the red SIF bands (640–700 nm) shows low correlation with GPP due to the strong (re)absorption of red SIF emissions by leaf chlorophyll; (2) the individual bands in near-infrared area (at 720 nm, 740 nm, and 761 nm) can determine about 60% and 62% of the variance in GPP with hourly scale by LR and GPR, respectively, and the combination of those SIF bands provide increased predictive power, explaining 66% and 76% variance in GPP with hourly scale by LR and GPR, respectively; (3) the solar radiation saturation, fraction of direct solar radiation, air temperature and leaf area index may have negative impacts on the SIF-GPP correlations when they are beyond optimal thresholds; and (4) the temporal aggregation of SIF-GPP (daily scale) enhances the correlations as compared with the hourly scale: the daily combination of SIF bands (at 687 nm, 720 nm, and 761 nm) can account for 80% and 93% of variance in daily daytime GPP by LR and GPR, respectively, suggesting that the combination of these three bands of SIF on the daily scale is the best proxy for GPP. Our results provide a new approach to analyze the SIF-GPP correlations using the ground-based full broadband SIF emission, suggesting that multi-bands SIF has a stronger capacity in predicting plant GPP than traditionally used signal band SIF data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Consistency-Enhanced Evolution for Variable Selection Can Identify Key Chemical Information from Spectroscopic Data

In the last few decades, spectroscopic techniques such as near-infrared (NIR) spectroscopy have gained wide applications in several industries, such as the pharmaceutical, agricultural, oil, and gas industries. As a result, various soft sensors have been developed to predict sample properties from spectroscopic readings. Because the spectroscopic readings at different wavelengths, especially at the adjacent wavelengths, are highly correlated, it has been shown that variable selection could significantly improve a soft sensor’s prediction performance while reducing the model complexity. To improve the prediction performance, most variable selection methods focus on identifying the variables (i.e., wavelengths or wavelength segments) that are strongly correlated with the dependent variable. Although many successful applications have been reported, these variable selection methods do have their limitations. Specifically, the selected wavelengths sometimes show little connection to the chemical bounds or functional groups presenting in the sample. In addition, the selected variables can be quite sensitive to the choice of the training samples. In this work, we address these limitations from a different perspective: if a variable selection algorithm can identify the truly relevant input variables, it should consistently identify the same subset of variables regardless of the choice of the training samples. Therefore, we propose a variable selection method that aims to improve the consistency of variable selection resulting from different training samples. Furthermore, the new algorithm is termed consistency-enhanced evolution for variable selection (CEEVS). To demonstrate the performance and robustness of CEEVS, we compare the proposed method with three representative variable selection methods using five published NIR data sets. These case studies clearly demonstrate that by improving the variable selection consistency, we can not only achieve improved prediction performance, but also identify key chemical information from spectroscopic data.

42 ENGINEERING↗
DOE OSTI2024

Modeling stochastic fluctuations in relativistic kinetic theory

Using the information current, we develop a Lorentz-covariant framework for modeling equilibrium fluctuations in relativistic kinetic theory in the grand-canonical ensemble. The resulting stochastic theory is proven to be causal and covariantly stable, and its predictions do not depend on the choice of spacetime foliation used to define the grand-canonical probabilities. As expected, in a box containing N > 5 particles, Boltzmann’s molecular chaos postulate is broken with (almost exact) probability N -1/2 , leading to a breakdown of the Boltzmann equation in small systems. Here, we also verify that, in ultrarelativistic gases, transient hydrodynamics already accounts for at least 80% of the equilibrium fluctuations of the stress-energy tensor at a given time. Finally, we compute the correlators at nonequal times for two selected collision kernels: that of a chemically active diluted solution, and that of ultrarelativistic scalar particles self-interacting via a quartic potential. For the former, we compute the density-density correlators analytically in real space, and dehydrodynamization of the stochastic theory is proven to occur whenever the mean free path diverges at high energy.

Astronomy & Astrophysics↗
DOE OSTI2020

Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Autonomous Selective Parts-Based Tracking

Object tracking from videos is still a challenging task due to various changes throughout a video sequence including occlusions, motion blur, scale and other deformation changes. In this paper, we propose a selective parts-based approach, using correlation filters, that makes choices based on a consensus of the parts and global tracking. Moreover, we further enhance our parts-based approach by introducing a segmentation-assisted parts initialization. In addition, we present a genetic algorithm-based method to autonomously select various parameters of the tracking algorithm, as opposed to the common practice of manually tuning those parameters. In contrast to existing part-based methods, the proposed method does not dilute accurate tracking by averaging results over multiple parts at every frame. Instead, we take a selective approach based on the relative weight of the responses across parts. Moreover, we only make location corrections when a part diverges, and rely on these location corrections to maintain an accurate appearance model. In the case of occlusions, which are among the main reasons for using a parts-based approach, our proposed approach consistently achieves the best performance. It is due to the ability to handle occlusion and not dilute decisions with incorrect parts, that our proposed approach enables state-of-the-art performance. The approach we propose was evaluated on videos from three different challenging benchmark datasets. Our approach has resulted in better overall precision and success rates for three different base tracking approaches.

42 ENGINEERING↗
DOE OSTI2024

Spin-dependent electron transfer in electrochemically transparent van der Waals heterostructures for oxygen evolution reaction

Spin selective catalysis is an emerging approach for improving the thermodynamics and kinetics of reactions. The role of electron spins has been scarcely studied in catalytic reactions. One exception is the oxygen evolution reaction (OER) where strongly correlated metals and oxides are used as catalysts. In OER, spin alignment facilitates the transition of singlet state of the reactant to the triplet state of O 2 . However, the influence of strong correlations on spin exchange mechanism and spin selective thermodynamics of most catalytic reactions remain unclear. Here we decouple the strongly correlated catalyst from the electrolyte to study spin exchange in two-dimensional (2D) magnetic iron germanium telluride (FGT) heterostructure. We demonstrate that transmission of spin and electrochemical information between the catalyst and the reactant can occur through quantum exchange interaction despite the catalyst of FGT being completely encapsulated by graphene or hexagonal boron nitride (hBN). The strong correlations in FGT that lead to enhanced spin exchange in OER are observed in graphene or hBN layers with thicknesses of up to 6 nm. We demonstrate that spin alignment in FGT leads to a lowering of thermodynamic barrier for adsorption of hydroxide ion and electron transfer to the catalyst. This results in up to fivefold enhancement in OER performance and improved kinetics. Our results provide clear evidence that transmission of both quantum mechanical and electrochemical information through quantum spin exchange interaction in FGT leads to an enhancement in catalytic performance.

2D materials↗
DOE OSTI2026

Robustness of pairwise kinematic Sunyaev–Zel’dovich effect to optical-cluster-selection bias

The pairwise kinematic Sunyaev–Zel’dovich (kSZ) effect measures both the pairwise motion between galaxy groups and clusters and the amount of gas within them, providing a tracer for cosmic growth. To interpret the cosmological information in the kSZ measurements, it is crucial to understand the optical-cluster-selection bias on the kSZ observables. Line-of-sight structures that contribute to both the optical observable (e.g., richness) and the cosmological signal can induce a correlation between these two quantities at a fixed cluster mass. The selection bias arising from this correlation is a key systematic effect for cosmological analyses. For cosmological observables such as cluster abundance and weak lensing, controlling this selection bias may help explain the tension between the DES-Y1 results and the Planck constraints. In order to test for a kSZ effect equivalent of such a bias, we adopted an alternative mock richness based on galaxy counts within cylindrical volumes along the line of sight. We applied the cylindrical count method to hydrodynamical simulations across a wide range of galaxy-selection criteria, assigning richness consistent with DES-Y1 to the mock clusters. When comparing optically selected clusters to mass-selected halos, we find no significant bias on pairwise kSZ signals, pairwise velocities, or optical depth within our uncertainty limits of approximately 16, 10, and 8%, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1974

A Monte Carlo investigation of thrust imbalance of solid rocket motor pairs

A technique is described for theoretical, statistical evaluation of the thrust imbalance of pairs of solid-propellant rocket motors (SRMs) firing in parallel. Sets of the significant variables, determined as a part of the research, are selected using a random sampling technique and the imbalance calculated for a large number of motor pairs. The performance model is upgraded to include the effects of statistical variations in the ovality and alignment of the motor case and mandrel. Effects of cross-correlations of variables are minimized by selecting for the most part completely independent input variables, over forty in number. The imbalance is evaluated in terms of six time - varying parameters as well as eleven single valued ones which themselves are subject to statistical analysis. A sample study of the thrust imbalance of 50 pairs of 146 in. dia. SRMs of the type to be used on the space shuttle is presented. The FORTRAN IV computer program of the analysis and complete instructions for its use are included. Performance computation time for one pair of SRMs is approximately 35 seconds on the IBM 370/155 using the FORTRAN H compiler.

Sforzini, R. H.↗
DOE OSTI2017

Tuning the Selectivity of Single-Site Supported Metal Catalysts with Ionic Liquids

1,3-Dialkylimidazolium ionic liquid coatings act as electron donors, increasing the selectivity for partial hydrogenation of 1,3-butadiene catalyzed by iridium complexes supported on high-surface-area γ-Al 2 O 3 . High-energy-resolution fluorescence detection X-ray absorption near-edge structure (HERFD XANES) measurements quantify the electron donation and are correlated with the catalytic activity and selectivity. Furthermore, the results demonstrate broad opportunities to tune electronic environments and catalytic properties of atomically dispersed supported metal catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Caldera-structure relationships in Kenya - Observations from satellite data

Maps were constructed showing lineaments displayed on four Landsat MSS images, to study volcanic-tectonic relationships in the Kenya segment of the Gregory or eastern sector of the East African rift system. Many of the mapped lineaments correlated well with fractures shown on geological maps. Selected lineaments, transverse to the rift trend, could be extrapolated to intersect rift faults at locations correlative with the locations of Kenya's seven volcanic calderas. The volcano-genetic difference between these intersections vs. similar intersections having no obvious relationship to any type of volcanic vent is not yet understood.

Blodget, Herbert W.↗
DOE OSTI2017

Clustering of quasars in SDSS-IV eBOSS: study of potential systematics and bias determination

Here, we study the first year of the eBOSS quasar sample in the redshift range 0.9 Q = 2.45 ± 0.05 at $\bar{z}$=1.55, together with its evolution with redshift. We use this information to determine the minimum mass of the halo hosting the quasars and the characteristic halo mass, which we find to be both independent of redshift within statistical error. Using a recently-measured quasar-luminosity-function we also determine the quasar duty cycle. Lastly, the size of this first year sample is insufficient to detect any luminosity dependence to quasar clustering and this issue should be further studied with the final ~500,000 eBOSS quasar sample.

79 ASTRONOMY AND ASTROPHYSICS↗