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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Sensors are the key information source for fault detection and diagnostics (FDD) in buildings. However, sensors are often not properly designed, installed, calibrated, located, and maintained, which negatively impacts FDD performance. Several sensor-related FDD topics have been widely studied, covering a wide range of fault types and applications. However, it is difficult to get a clear picture of the technical development of sensor-related topics in FDD. A systematic review of sensor topics is needed to summarize the existing research in a logical way, draw conclusions on the current development, and predict the future development of sensors in building FDD. To address this gap, we conducted a comprehensive literature review of more than 100 FDD-sensor-related papers. In this article, we subdivide the FDD tasks into building-level, system-level, and component-level FDD, and review sensor-related topics in each category. Our major conclusions are: (a) current data-driven FDD research focuses more on FDD algorithms than sensors, (b) sensor “hardware” research topics are less studied than sensor “software” topics, (c) very few papers focus on sensor engineering as an integral aspect of FDD development, and (d) some important sensor topics, such as sensor cost-effectiveness and sensor schema/layout/location, are not well studied. Finally, we discuss the need for a systematic framework of FDD sensors and models to integrate sensor design/selection, sensor data analysis/mining, feature selection, physics-based or data-driven algorithm development, sensor fault detection, sensor calibration, and sensor maintenance. Finally, expert interviews are conducted to validate the above findings and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Magnetic surface domain imaging of uncapped epitaxial FeRh(001) thin films across the temperature-induced metamagnetic transition

The surface magnetic domain structure of uncapped epitaxial FeRh/MgO(001) thin films was imaged by in-situ scanning electron microscopy with polarization analysis (SEMPA) at various temperatures between 122 and 450 K. This temperature range covers the temperature-driven antiferromagnetic-to-ferromagnetic phase transition in the body of the films that was observed in-situ by means of the more depth-sensitive magneto-optical Kerr effect. The SEMPA images confirm that the interfacial ferromagnetism coexisting with the antiferromagnetic phase inside the film is an intrinsic property of the FeRh(001) surface. Furthermore, the SEMPA data display a reduction of the in-plane magnetization occuring well above the phase transition temperature which, thus, is not related to the volume expansion at the phase transition. This observation is interpreted as a spin reorientation of the surface magnetization for which we propose a possible mechanism based on temperature-dependent tetragonal distortion due to different thermal expansion coefficients of MgO and FeRh.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Reduced Erosion Augments Soil Carbon Storage Under Cover Crops

ABSTRACT Cover crops, a promising strategy to increase soil organic carbon (SOC) storage in croplands and mitigate climate change, have typically been shown to benefit soil carbon (C) storage from increased plant C inputs. However, input‐driven C benefits may be augmented by the reduction of C outputs induced by cover crops, a process that has been tested by individual studies but has not yet been synthesized. Here we quantified the impact of cover crops on organic C loss via soil erosion (SOC erosion) and revealed the geographical variability at the global scale. We analyzed the field data from 152 paired control and cover crop treatments from 57 published studies worldwide using meta‐analysis and machine learning. The meta‐analysis results showed that cover crops widely reduced SOC erosion by an average of 68% on an annual basis, while they increased SOC stock by 14% (0–15 cm). The absolute SOC erosion reduction ranged from 0 to 18.0 Mg C −1 ha −1 year −1 and showed no correlation with the SOC stock change that varied from −8.07 to 22.6 Mg C −1 ha −1 year −1 at 0–15 cm depth, indicating the latter more likely related to plant C inputs. The magnitude of SOC erosion reduction was dominantly determined by topographic slope. The global map generated by machine learning showed the relative effectiveness of SOC erosion reduction mainly occurred in temperate regions, including central Europe, central‐east China, and Southern South America. Our results highlight that cover crop‐induced erosion reduction can augment SOC stock to provide additive C benefits, especially in sloping and temperate croplands, for mitigating climate change.

Huang, Wenjuan [Department of Ecology, Evolution, ↗
DOE OSTI2017

Optimizing the query performance of block index through data analysis and I/O modeling

Indexing technique has become an efficient tool to enable scientists to directly access the most relevant data records. But, the time and space requirements of building and storing indexes are expensive in the traditional approaches, such as R-tree and bitmaps. Recently, we started to address this issue by using the idea of "block index", and our previous work has shown promising results from comparing it against other well-known solutions, including ADIOS, SciDB, and FastBit. In this work, we further improve the technique from both theoretical and implementation perspectives. Driven by an extensive effort in characterizing scientific datasets and modeling I/O systems, we presented a theoretical model to analyze its query performance with respect to a given block size configuration. We also introduced three optimization techniques to achieve a 2.3x query time reduction comparing to the original implementation.

Wu, Tzuhsien↗
DOE OSTI2020

Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product Report

Objective analysis (OA) is a method of mapping unstructured data to a structured grid. In the context of scanning radar data, OA is used to interpolate data in antenna coordinates (range, azimuth, and elevation) onto a regularly spaced Cartesian grid (Trapp and Doswell 2000). The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product (VAP) uses the Python ARM Radar Toolkit (Py-ART), a data model-driven interactive architecture for working with weather radar data, to map the data to a Cartesian grid (Helmus and Collis 2016). MMCG, with Py-ART built in, has the ability to take radar data in antenna coordinates and map the gates to a Cartesian grid using inverse distance weight functions such as Cressman (square) and Barnes (exponential), but also can filter the data during the interpolation. MMCG also allows arbitrary formulations for the radius of influence, which are matched to particular radar scanning strategies. This creates a complex parameter space for optimizing the retention of storm structure detail while minimizing artifacts. MMCG takes data processed with ARM’s Corrected Precipitation Radar Moments in Antenna Coordinates (CMAC) VAP and maps it to a Cartesian grid as the output product. A variety of fields that have been mapped to the Cartesian grid are then saved to plots to complement each grid file.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Technical Requirements Analysis and Control Systems (TRACS) Initial Operating Capability (IOC) documentation

The Technical Requirements Analysis and Control Systems (TRACS) software package is described. TRACS offers supplemental tools for the analysis, control, and interchange of project requirements. This package provides the fundamental capability to analyze and control requirements, serves a focal point for project requirements, and integrates a system that supports efficient and consistent operations. TRACS uses relational data base technology (ORACLE) in a stand alone or in a distributed environment that can be used to coordinate the activities required to support a project through its entire life cycle. TRACS uses a set of keyword and mouse driven screens (HyperCard) which imposes adherence through a controlled user interface. The user interface provides an interactive capability to interrogate the data base and to display or print project requirement information. TRACS has a limited report capability, but can be extended with PostScript conventions.

Hammond, Dana P.↗
NASA NTRS2011

Investigation of Seasonal Landscape Freeze/Thaw Cycles in Relation to Cloud Structure in the High Northern Latitudes

The seasonal freezing and thawing of Earth's cryosphere (the portion of Earth's surface permanently or seasonally frozen) has an immense impact on Earth's climate as well as on its water, carbon and energy cycles. During the spring, snowmelt and the transition between frozen and non-frozen states lowers Earth's surface albedo. This change in albedo causes more solar radiation to be absorbed by the land surface, raising surface soil and air temperatures as much as 5 C within a few days. The transition of ice into liquid water not only raises the surface humidity, but also greatly affects the energy exchange between the land surface and the atmosphere as the phase change creates a latent energy dominated system. There is strong evidence to suggest that the thawing of the cryosphere during spring and refreezing during autumn is correlated to local atmospheric conditions such as cloud structure and frequency. Understanding the influence of land surface freeze/thaw cycles on atmospheric structure can help improve our understanding of links between seasonal land surface state and weather and climate, providing insight into associated changes in Earth's water, carbon, and energy cycles that are driven by climate change.Information on both the freeze/thaw states of Earth's land surface and cloud characteristics is derived from data sets collected by NOAA's Special Sensor Microwave/Imager (SSM/I), the Advanced Microwave Scanning Radiometer on NASA's Earth Observing System(AMSR-E), NASA's CloudSat, and NASA's SeaWinds-on-QuickSCAT Earth remote sensing satellite instruments. These instruments take advantage of the microwave spectrum to collect an ensemble of atmospheric and land surface data. Our analysis uses data from radars (active instruments which transmit a microwave signal toward Earth and measure the resultant backscatter) and radiometers (passive devices which measure Earth's natural microwave emission) to accurately characterize salient details on Earth's surface and atmospheric states. By comparing the cloud measurements and the surface freeze-thaw data sets, a correlation between the two phenomena can be developed.

clouds↗
DOE OSTI2019

Data-driven material models for atomistic simulation

The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and find accurate parametrizations for potentials using traditional approaches. Machine learning has emerged as an effective alternative approach to develop accurate and robust interatomic potentials. Beginning with a very general model form, the potential is learned directly from a database of electronic structure calculations and therefore can be viewed as a multiscale link between quantum and classical atomistic simulations. Risk of inaccurate extrapolation exists outside the narrow range of time and length scales where the two methods can be directly compared. Here, we use the spectral neighbor analysis potential (SNAP) and show how a fit can be produced with minimal interpolation errors which is also robust in extrapolating beyond training. To demonstrate the approach, we have developed a tungsten-beryllium potential suitable for the full range of binary compositions. Subsequently, large-scale molecular dynamics simulations were performed of high energy Be atom implantation onto the (001) surface of solid tungsten. The machine learned W-Be potential generates a population of implantation structures consistent with quantum calculations of defect formation energies. A very shallow ( < 2 nm ) average Be implantation depth is predicted which may explain ITER diverter degradation in the presence of beryllium.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predicting fatigue from heart rate signatures using functional logistic regression

Physical fatigue can have adverse effects on humans in extreme environments. Therefore, being able to predict fatigue using easy to measure metrics such as heart rate (HR) signatures has potential to have an impact in real-life scenarios. We apply a functional logistic regression model that uses HR signatures to predict physical fatigue, where physical fatigue is defined in a data-driven manner. Data were collected using commercially available wearable devices on 47 participants hiking the 20.7-mile Grand Canyon rim-to-rim trail in a single day. Fitted model provides good predictions and interpretable parameters for real-life application.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Improving Empirical Magnetic Field Models by Fitting to In Situ Data Using an Optimized Parameter Approach

A method for comparing and optimizing the accuracy of empirical magnetic field models using in situ magnetic field measurements is presented in this paper. The optimization method minimizes a cost function—τ—that explicitly includes both a magnitude and an angular term. A time span of 21 days, including periods of mild and intense geomagnetic activity, was used for this analysis. A comparison between five magnetic field models (T96, T01S, T02, TS04, and TS07) widely used by the community demonstrated that the T02 model was, on average, the most accurate when driven by the standard model input parameters. The optimization procedure, performed in all models except TS07, generally improved the results when compared to unoptimized versions of the models. Additionally, using more satellites in the optimization procedure produces more accurate results. This procedure reduces the number of large errors in the model, that is, it reduces the number of outliers in the error distribution. The TS04 model shows the most accurate results after the optimization in terms of both the magnitude and direction, when using at least six satellites in the fitting. It gave a smaller error than its unoptimized counterpart 57.3% of the time and outperformed the best unoptimized model (T02) 56.2% of the time. Its median percentage error in |B| was reduced from 4.54% to 3.84%. Finally, the difference among the models analyzed, when compared in terms of the median of the error distributions, is not very large. However, the unoptimized models can have very large errors, which are much reduced after the optimization.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Intermittency Model for Coupled CFD-Stability Transition Analysis in Hypersonic Flow

Accurate prediction of aerothermodynamic loads on hypersonic vehicles requires precise modeling of surface quantities across the boundary layer transition zone. The peak heating loads and total heat transfer are determined by parameters such as the transition zone length and the magnitude of potential overshoots in heat flux and skin friction beyond their respective values in fully turbulent flows. While previous studies on CFD integrated transition modeling using linear stability correlations have shown promise in modeling these features, they did not develop an intermittency model for hypersonic flows. This paper presents a data-driven approach to develop a model of this type by correlating experimental transition data with the relevant flow parameters. The resulting model demonstrates significant improvements over previous low-speed models in terms of predicting heat transfer distributions during the transition process associated with first and second mode instabilities in axisymmetric high-speed flows. Separate correlations for flight and ground test conditions are developed, and the potential to combine these correlations is discussed.

Transition↗
DOE OSTI2016

Influence of Rotational Distortions on Li + - and Na + -Intercalation in Anti-NASICON Fe 2 (MoO 4 ) 3

Anti-NASICON Fe 2 (MoO 4 ) 3 (P2 1 /c) shows significant structural and electrochemical differences in the intercalation of Li + and Na + ions. To understand the origin of this behavior, we have used a combination of in situ X-ray and high-resolution neutron diffraction, total scattering, electrochemical measurements, density functional theory calculations, and symmetry-mode analysis. We find that for Li + -intercalation, which proceeds via a two-phase monoclinic-to-orthorhombic (Pbcn) phase transition, the host lattice undergoes a concerted rotation of rigid polyhedral subunits driven by strong interactions with the Li + ions, leading to an ordered lithium arrangement. Na + -intercalation, which proceeds via a two-stage solid solution insertion into the monoclinic structure, similarly produces rotations of the lattice polyhedral subunits. Furthermore, using a combination of total neutron scattering data and density functional theory calculations, we find that while these rotational distortions upon Na + -intercalation are fundamentally the same as for Li + -intercalation, they result in a far less coherent final structure, with this difference attributed to the substantial difference between the ionic radii of the two alkali metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Data-Driven Kinetic Reaction Networks for Separation Chemistry

Understanding complex, multistep chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. Here, in this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid–liquid extraction mechanism of uranium using N,N-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models that are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO 2 (NO 3 ) 2 (DEHiBA) 2 , agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at a low computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

HIGH ENERGY SPECTRAL EVOLUTION OF V404 CYGNI DURING THE 2015 JUNE OUTBURST AS OBSERVED BY INTEGRAL

The black hole binary GS 2023+338 exhibited an unprecedently bright outburst in 2015 June. On 2015 June 17, the high energy instruments on board INTEGRAL detected an extremely variable emission during both bright and low luminosity phases, with dramatic variations of the hardness ratio on timescales of approximately seconds. The analysis of the IBIS and SPI data reveals the presence of hard spectra in the brightest phases, compatible with thermal Comptonization with a temperature of kT{sub e} ∼ 40 keV. The seed photon’s temperature is best fit by kT{sub 0} ∼ 7 keV, which is too high to be compatible with blackbody emission from the disk. This result is consistent with the seed photons being provided by a different source, which we hypothesize to be a synchrotron driven component in the jet. During the brightest phase of flares, the hardness shows a complex pattern of correlation with flux, with the maximum energy released in the range of 40–100 keV. The hard-X-ray variability for E > 50 keV is correlated with flux variations in the softer band, showing that the overall source variability cannot originate entirely from absorption, but at least part of it is due to the central accreting source.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: measurement of the growth rate of structure from the small-scale clustering of the luminous red galaxy sample

ABSTRACT We measure the small-scale clustering of the Data Release 16 extended Baryon Oscillation Spectroscopic Survey Luminous Red Galaxy sample, corrected for fibre-collisions using Pairwise Inverse Probability weights, which give unbiased clustering measurements on all scales. We fit to the monopole and quadrupole moments and to the projected correlation function over the separation range $7-60\, h^{-1}{\rm Mpc}$ with a model based on the aemulus cosmological emulator to measure the growth rate of cosmic structure, parametrized by fσ8. We obtain a measurement of fσ8(z = 0.737) = 0.408 ± 0.038, which is 1.4σ lower than the value expected from 2018 Planck data for a flat ΛCDM model, and is more consistent with recent weak-lensing measurements. The level of precision achieved is 1.7 times better than more standard measurements made using only the large-scale modes of the same sample. We also fit to the data using the full range of scales $0.1\text{--}60\, h^{-1}{\rm Mpc}$ modelled by the aemulus cosmological emulator and find a 4.5σ tension in the amplitude of the halo velocity field with the Planck + ΛCDM model, driven by a mismatch on the non-linear scales. This may not be cosmological in origin, and could be due to a breakdown in the Halo Occupation Distribution model used in the emulator. Finally, we perform a robust analysis of possible sources of systematics, including the effects of redshift uncertainty and incompleteness due to target selection that were not included in previous analyses fitting to clustering measurements on small scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Structure of archaerhodopsin-2 at 1.8 Å resolution

The same hexagonal array of archaerhodpsin-2 trimers as observed in the native membrane is reconstituted in a three-dimensional crystal prepared by the membrane fusion method. In this crystal, a pair of conserved glutamate residues in the proton-release pathway is maintained by a low-barrier hydrogen bond. Archaerhodopsin-2 (aR2), the sole protein found in the claret membrane of Halorubrum sp. Aus-2, functions as a light-driven proton pump. In this study, structural analysis of aR2 was performed using a novel three-dimensional crystal prepared by the successive fusion of claret membranes. The crystal is made up of stacked membranes, in each of which aR2 trimers are arranged on a hexagonal lattice. This lattice structure resembles that found in the purple membrane of H. salinarum, except that lipid molecules trapped within the trimeric structure are not distributed with perfect threefold symmetry. Nonetheless, diffraction data at 1.8 Å resolution provide accurate structural information about functionally important residues. It is shown that two glutamates in the proton-release channel form a paired structure that is maintained by a low-barrier hydrogen bond. Although the structure of the proton-release pathway is highly conserved among proton-pumping archaeal rhodopsins, aR2 possesses the following peculiar structural features: (i) the motional freedom of the tryptophan residue that makes contact with the C13 methyl group of retinal is restricted, affecting the formation/decay kinetics of the L state, and (ii) the N-terminal polypeptide folds into an Ω-loop, which may play a role in organizing the higher-order structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Dynamic X-ray diffraction imaging of the ferroelectric response in bismuth ferrite

Abstract X-ray diffraction imaging is rapidly emerging as a powerful technique by which one can capture the local structure of crystalline materials at the nano- and meso-scale. Here, we present investigations of the dynamic structure of epitaxial monodomain BiFeO 3 thin-films using a novel full-field Bragg diffraction imaging modality. By taking advantage of the depth penetration of hard X-rays and their exquisite sensitivity to the atomic structure, we imaged in situ and in operando, the electric field-driven structural responses of buried BiFeO 3 epitaxial thin-films in micro-capacitor devices, with sub-100 nm lateral resolution. These imaging investigations were carried out at acquisition frame rates that reached up to 20 Hz and data transfer rates of 40 MB/s, while accessing diffraction contrast that is sensitive to the entire three-dimensional unit cell configuration. We mined these large datasets for material responses by employing matrix decomposition techniques, such as independent component analysis. We found that this statistical approach allows the extraction of the salient physical properties of the ferroelectric response of the material, such as coercive fields and transient spatiotemporal modulations in their piezoelectric response, and also facilitates their decoupling from extrinsic sources that are instrument specific.

36 MATERIALS SCIENCE↗