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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Remote Attitude Measurement Sensor (RAMS)

Remote attitude measurement sensor (RAMS) offers a low-cost, low-risk, proven design concept that is based on mature, demonstrated space sensor technology. The electronic design concepts and interpolation algorithms were tested and proven in space hardware like th Retroreflector Field Tracker and various star trackers. The RAMS concept is versatile and has broad applicability to both ground testing and spacecraft needs. It is ideal for use as a precision laboratory sensor for structural dynamics testing. It requires very little set-up or preparation time and the output data is immediately usable without integration or extensive analysis efforts. For on-orbit use, RAMS rivals any other type of dynamic structural sensor (accelerometer, lidar, photogrammetric techniques, etc.) for overall performance, reliability, suitability, and cost. Widespread acceptance and extensive usage of RAMS will occur only after some interested agency, such as OAST, adopts the RAMS concept and provides the funding support necessary for further development and implementation of RAMS for a specific program.

Davis, H. W.↗
DOE OSTI2015

THE ARROWHEAD MINI-SUPERCLUSTER OF GALAXIES

Superclusters of galaxies can be defined kinematically from local evaluations of the velocity shear tensor. The location where the smallest eigenvalue of the shear is positive and maximal defines the center of a basin of attraction. Velocity and density fields are reconstructed with Wiener Filter techniques. Local velocities due to the density field in a restricted region can be separated from external tidal flows, permitting the identification of boundaries separating inward flows toward a basin of attraction and outward flows. This methodology was used to define the Laniakea Supercluster that includes the Milky Way. Large adjacent structures include Perseus–Pisces, Coma, Hercules, and Shapley but current kinematic data are insufficient to capture their full domains. However, there is a small region trapped between Laniakea, Perseus–Pisces, and Coma that is close enough to be reliably characterized and that satisfies the kinematic definition of a supercluster. Because of its shape, it is given the name the Arrowhead Supercluster. This entity does not contain any major clusters. A characteristic dimension is ∼25 Mpc and the contained mass is only ∼10{sup 15} M{sub ⊙}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Pushing the limits of NAND technology scaling with ferroelectrics

Artificial intelligence (AI) continues to drive transformative advancements across various industries. The data-intensive nature of AI training (and inferencing) has resulted in the generation of unprecedented volumes of data with machine-generated content surpassing human-generated data by more than 100-fold in 2025. Efficiently managing this data influx necessitates advanced digital storage technologies. However, traditional NAND flash memory, which is critical for supporting data flows in AI systems—alongside high-bandwidth memory, for AI training—faces fundamental scaling limitations as it approaches the 1000-layer milestone, encompassing more than 40 trillion transistors. This article delves into the potential of hafnia-based ferroelectric materials as a breakthrough solution to these challenges. Recent advancements indicate that the intrinsic limitations of ferroelectric field-effect transistors (FEFETs) can be mitigated through material and device-level engineering. These advancements enable FEFETs to meet the stringent density, reliability, and scalability requirements of future three-dimensional NAND technology. The role of ferroelectrics in addressing NAND scaling challenges and expanding storage capabilities presents a promising avenue for meeting the storage demands of the AI-driven era.

3D NAND↗
DOE OSTI2024

Quantification of the Crack Evolution Process by Extracting Relevant Signal Components from Wave Propagation and Diffusive Transport Front Measurements

Wave propagation and diffusive transport phenomena in a geological rock sample undergoing crack evolution process are expected to interact with the mechanical discontinuities in the medium. The measurements of the signals associated with these phenomena can be used to assess and monitor the crack-driven micromechanical alterations in the rock. Different wave/diffusion phenomena, such as sonic propagation, pressure diffusion, and acoustic emission (AE), are sensitive to different elements of the mechanical discontinuities generated during the evolution of the crack clusters from initiation to coalescence. Sonic propagation, AE, and pressure diffusion monitoring have the potential to map the crack evolution because the transmitter-receiver arrays can be designed, arranged and tuned to (1) achieve maximum recovery of the scattered waveforms and travel times, (2) capture the later arrivals and multiple reflections, and (3) illuminate large rock volume. However, the structural/topological complexities of the mechanical discontinuities, complex distribution of the stress fields, complex mechanical alterations in media, and fluid redistribution in the crack system pose serious challenges for the detection and modeling of the crack evolution process (from here on, we will use the term ‘crack evolution process’ to mean that the crack evolution occurred under shallow crustal conditions). For purposes of accurately accounting such complexities and heterogeneities in the absence of reliable physical laws, simulation methods, and signal processing techniques, my early-career research proposal will develop and apply novel data-driven machine learning methods to: (1) extract signal components relevant to the various phases of crack evolution and (2) generate a 2D visual map of the crack evolution process.

58 GEOSCIENCES↗
DOE OSTI2023

Assessment of Potential Pennycress Availability and Suitable Sites for Sustainable Aviation Fuel Refineries in Ohio

Pennycress grain has a relatively high oil content (25–36%) and it is considered a desirable feedstock to produce sustainable aviation fuel (SAF). Pennycress crop can be integrated into the corn–soybean rotation as a winter cover crop in the midwestern U.S. to provide both ecosystem services and economic benefits for the farmers, while serving as a promising feedstock for SAF production. For pennycress-based SAF biorefineries to be established at the commercial scale, a sustainable design of the supply system is required to provide reliable information on feedstock availability and optimal facility locations. The objectives of this research were to assess the pennycress production potential in Ohio, and to identify the best locations to establish the SAF biorefineries. To estimate the pennycress production potential in Ohio, a geographic information system (GIS)-based model was developed using the spatially explicit six-year historical data on areas that were planted in the corn–soybean rotation for the period of 2013 through 2018, pennycress yield estimates from field-based experiments reported in the literature, and the soil productivity index for the region of study. Optimal SAF biorefinery locations were identified using a GIS-based location-allocation model. Annual land potentially available for pennycress production in Ohio was estimated to be ~0.6 million ha, which could produce ~1.1 million metric tons of pennycress grain as feedstock to produce ~210 million liters of SAF, depending on the pennycress yield level, oil content, and conversion efficiencies. In addition, the optimum locations for 12 biorefineries, each at an annual capacity of 18.9 million liters of SAF, were identified, and the average transportation distance was estimated to be 35 and 58 km for maximizing attendance and coverage conditions, respectively. The outcomes of this research would help minimize the risks associated with feedstock supply and cost variabilities for pennycress-based SAF production in the region.

Mousavi-Avval, Seyed Hashem↗
NASA NTRS1984

Growth and reflectance characteristics of winter wheat canopies

A valuable input to crop growth and yield models would be estimates of current crop condition. If multispectral reflectance indicates crop condition, then remote sensing may provide an additional tool for crop assessment. The effects of nitrogen fertilization on the spectral reflectance and agronomic characteristics of winter wheat (Triticum aestivum L.) were determined through field experiments. Spectral reflectance was measured during the 1979 and 1980 growing seasons with a spectroradiometer. Agronomic data included total leaf N concentration, leaf chlorophyll concentration, stage of development, leaf area index (LAI), plant moisture, and fresh and dry phytomass. Nitrogen deficiency caused increased visible, reduced near infrared, and increased middle infrared reflectance. These changes were related to lower levels of chlorophyll and reduced leaf area in the N-deficient plots. Green LAI, an important descriptor of wheat canopies, could be reliably estimated with multispectral data. The potential of remote sensing in distinguishing stressed from healthy crops is demonstrated. Evidence suggests multispectral imagery may be useful for monitoring crop condition.

Hinzman, L. D.↗
NASA NTRS1992

Unsteady two- and three-dimensional Navier-Stokes simulations of multistage turbomachinery flows

The flow-field within an axial flow turbomachine, such as a turbine or compressor, is extremely complex because of three-dimensional features such as hub-corner stall, tip-leakage flows, and airfoil wakes. These flow features interact with each other and with rotor and stator airfoils inducing time-varying forces on the airfoils. These complicated rotor-stator interactions must be understood in order to design turbomachines that are light and compact as well as reliable and efficient. Two codes, STAGE-2 and STAGE-3, have been developed to compute these unsteady rotor-stator interaction flows in multistage turbomachines. An implicit, thin-layer Euler/Navier-Stokes zonal algorithm is used to compute the unsteady flow-field within both turbine and compressor configurations. Results include surface pressures and wake profiles for two-dimensional turbine and compressor configurations and surface pressures for a three-dimensional single-stage turbine configuration. The results compare well with experimental data and other unsteady computations.

Gundy-Burlet, K. L.↗
NASA NTRS2006

A Scheme for finding the Front Boundary of an Interplanetary Magnetic Cloud

We developed a scheme for finding the front boundary of an interplanetary magnetic cloud (MC) based on criteria that depend on the possible existence of any one or all of six specific solar wind features. The features that the program looks for, within +/- 2 hours of a preliminarily determined time for the front boundary, estimated either by visual inspection or by an automatic MC identification scheme, are: (1) a sufficiently large directional discontinuity in the interplanetary magnetic field (IMF), (2) existence of a magnetic hole, (3) a significant proton plasma beta drop, (4) a significant proton temperature drop, (5) a marked increase in the IMF's intensity, and (6) a significant decrease in a normalized root-mean-square deviation (RMS)of the magnetic field - where the scheme was tested using 5, 10, 15, and 20 minute averages of the relevant physical quantities, in order to find the optimum average (and RMS) to use. Other criteria, besides these six, were examined and dismissed as not reliable, e.g., plasma speed. The scheme was developed specifically for aiding in forecasting the strength and timing of a geomagnetic storm due to the passage of an interplanetary MC in real-time, but can be used in post ground-data collection for imposition of consistency in choosing a MC's front boundary. The scheme has been extensively tested, first using 80 bona fide MCs over about 9 years of WIND data, and also for 121 MC-like structures as defined by a program that automatically identifies such structures over the same period. Optimum limits for various parameters in the scheme were found by statistical studies of the WIND MCs. The resulting limits can be user-adjusted for other data sets, if desired. Final testing of the 80 MCs showed that for 50 percent of the events the boundary estimates occurred within +/-10 minutes of visually determined times, 80 percent occurred within +/-30 minutes, and 91 percent occur within +/-60 minutes, and three or more individual boundary tests were passed for 88 percent of the total MCs. The scheme and its testing will be described.

Lepping, Ronald P.↗
DOE OSTI2015

Mean PB To Failure - Initial results from a long-term study of disk storage patterns at the RACF

The RACF (RHIC-ATLAS Computing Facility) has operated a large, multi-purpose dedicated computing facility since the mid-l 990's, serving a worldwide, geographically diverse scientific community that is a major contributor to various HEPN projects. A central component of the RACF is the Linux-based worker node cluster that is used for both computing and data storage purposes. It currently has nearly 50,000 computing cores and over 23 PB of storage capacity distributed over 12,000+ (non-SSD) disk drives. The majority of the 12,000+ disk drives provide a cost-effective solution for dCache/XRootD-managed storage, and a key concern is the reliability of this solution over the lifetime of the hardware, particularly as the number of disk drives and the storage capacity of individual drives grow. We report initial results of a long-term study to measure lifetime PB read/written to disk drives in the worker node cluster. We discuss the historical disk drive mortality rate, disk drive manufacturers' published MPTF (Mean PB to Failure) data and how they are correlated to our results. The results help the RACF understand the productivity and reliability of its storage solutions and have implications for other highly-available storage systems (NFS, GPFS, CVMFS, etc) with large I/0 requirements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Bayes goes fast: Uncertainty quantification for a covariant energy density functional emulated by the reduced basis method

A covariant energy density functional is calibrated using a principled Bayesian statistical framework informed by experimental binding energies and charge radii of several magic and semi-magic nuclei. The Bayesian sampling required for the calibration is enabled by the emulation of the high-fidelity model through the implementation of a reduced basis method (RBM)—a set of dimensionality reduction techniques that can speed up demanding calculations involving partial differential equations by several orders of magnitude. The RBM emulator we build—using only 100 evaluations of the high-fidelity model—is able to accurately reproduce the model calculations in tens of milliseconds on a personal computer, an increase in speed of nearly a factor of 3,300 when compared to the original solver. Besides the analysis of the posterior distribution of parameters, we present model calculations for masses and radii with properly estimated uncertainties. We also analyze the model correlation between the slope of the symmetry energy L and the neutron skin of 48 Ca and 208 Pb. The straightforward implementation and outstanding performance of the RBM makes it an ideal tool for assisting the nuclear theory community in providing reliable estimates with properly quantified uncertainties of physical observables. Such uncertainty quantification tools will become essential given the expected abundance of data from the recently inaugurated and future experimental and observational facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1983

The magnetic-field investigation on ISPM

The International Solar Polar Mission (ISPM) onboard instrumentation for the magnetic field experiment to establish, on the basis of in-situ observations, the heliolatitude dependence of the interplanetary magnetic field, is described. The prime output consists of vector measurements, made by two triaxial magnetometers, of the ambient magnetic field along the orbit of the spacecraft. The onboard data processor generates two data streams to be transmitted through the spacecraft telemetry. The low speed, analog data stream consists of averaged and despun vector measurements, digitized in the spacecraft analog to digital converter (ADC). The despinning algorithm used in the analog processor is described. The high speed, digital data stream consists of vector measurements digitized in the instrument ADCs, generating up to two vector samples per sec. Multiple data-path switching is used to increase system reliability and to allow cross calibration of the ADCs. On board facilities for inflight calibration are described.

Balogh, A.↗
NASA NTRSDate not supplied

Examining the Role of IMU Health Characterization and Monitoring in UAS Safety

Inertial measurement units (IMUs) can be vital for vehicle attitude and position estimation in unmanned aerial vehicles (UAVs). Degradation in these units could result in incorrect position estimates that are utilized in vehicle control, trajectory prediction, and other critical systems; however, the modes and effects of degradation in these IMUs are not well understood. In order to quantify the risk posed by degradation in these sensors, a study was conducted on the types of IMUs prevalent in the commercial market, their known failure modes, and the applicability to health monitoring methods for risk reduction and increased safety in an increasingly autonomous airspace. First, use cases and failure modes of the various types and reliability of commercially available IMUs were reviewed, and knowledge gaps and issues in the field were identified. Noting that inexpensive and lightweight MEMS (Microelectromechanical Systems) IMUs are some of the most commonly used but least reliable sensors in sUAS (small Unmanned Aerial Systems), five inexpensive MEMS IMUs were chosen for a performance evaluation study, selected from the Pixhawk autopilot systems, hobby sUAS, and prior NASA experimental studies. For each of these IMUs, a 10-minute bias test and a 12-hour drift test were performed for the accelerometers in a benchtop setting, using a BeagleBone Black for data collection. Using these results, the sensors’ performance is compared to their reported specifications, and the utility of implementing diagnostic methods for MEMS IMUs for research and commercial applications is evaluated. The paper concludes with a planned study for the evaluation of vibration-induced degradation for the selected sensors.

IMUs↗
NASA NTRSDate not supplied

The Additive Manufacturing Moment Measure - A Parallel Computation Technique for Determining Build Variance in the Laser Powder Bed Fusion Process

Additive manufacturing (AM) has unique process attributes that facilitate the creation of optimized, complex, and unique parts for aerospace applications. However, the multi-scale and complicated building process for AM parts can cause unexpected build conditions that result in microstructural variability within the deposited material. The need for richer datasets and computational modeling capabilities to improve AM process reliability is a consequence of this microstructural variability. A computational approach, referred to as the Additive Manufacturing Moment Measure (AM3), addresses this need by leveraging the way AM processes fuse material in precise, incremental steps. During the layering sequence, each previous step contributes to the condition of the current step at the center of the heat source. The precise steps can be selected to systematically create a time-space point field with co-located machine input and in-situ sensor data. The sequential nature of the AM process, coupled with nearest neighbor calculations, allows for a fully parallel computation for part-scale build profile analysis. The AM3 concept introduced here enables part-scale assessment directly from build files and in-situ process monitoring sensors alike. The AM moment measures were calculated for build point fields and compared with co-located in-situ and ex-situ nondestructive evaluation and optical microscopy observations. These comparisons permit a better understanding of how the sequential process actions can affect the quality of a laser powder bed fusion (LPBF) build. Details of the AM3technique will be discussed and compared to measured LPBF part characteristics. The AM3 results indicate a strong potential to advance the qualification process for aerospace applications.

Additive Manufacturing↗
NASA NTRS2010

NICMOS Spectroscopy of HD 189733b

Spectral features corresponding to methane and water opacity were reported based on transmission spectroscopy of HD 189733b with Hubble/NICMOS. Recently, these data, and a similar data set for XO-1b, have been reexamined in Gibson et al. 2010, who claim they cannot reliably reproduce prior results. We examine the methods used by the Gibson team and identify two specific issues that could act to increase the formal uncertainties and to create instability in the minimization process. This would also be consistent with the GPA10 finding that they could not identify a way to select among the several instrument models they constructed. In the case of XO-1b, the Gibson team significantly changed the way in which the instrument model is defined (both with respect to the three approaches they used for HD 189733b, and the approach used by previous authors); this change, which omits the effect of the spectrum position on the detector, makes direct intercomparison of results difficult. In the experience of our group, the position of the spectrum on the detector is an important element of the instrument model because of the significant residual structure in the NICMOS spectral flat field. The approach of changing instrument models significantly complicates understanding the data reduction process and interpreting the results. Our team favors establishing a consistent method of handling NICMOS instrument systematic errors and applying it uniformly to data sets.

infrared↗
DOE OSTI2020

Binary pseudorandom array test standard optimized for characterization of large field-of-view optical interferometers

Recently, a technique for calibrating the modulation transfer function (MTF) of a broad variety of metrology instrumentation has been demonstrated. This technique is based on test samples structured as one-dimensional binary pseudo-random (BPR) sequences and two-dimensional BPR arrays (BPRAs). The inherent power spectral density of BPR gratings (sequences) and arrays has a deterministic white-noise-like character that allows direct determination of the MTF with uniform sensitivity over the entire spatial frequency range and field-of-view of an instrument. As such, the BPR samples satisfy the characteristics of a test standard: functionality, ease of specification and fabrication, reproducibility, and low sensitivity to manufacturing error. Here we discuss our recent developments directed to the optimization of the sample design, fabrication, application, and data processing procedures, suitable for thorough characterization of large aperture optical interferometers. Compared with the previous coded-aperture based design, the improved, 'highly randomized' BPRA pattern of the new test standard provides better accuracy and reliability of instrument MTF and aberration characterization, and enables operation optimization of large aperture optical interferometers. We describe the pattern generation algorithm and tests to verify the compliance to desired BPRA topography. The data acquisition and analysis procedures for different applications of the technique are also discussed.

Yashchuk, Valeriy V.↗
NASA NTRS1999

The Role of Data Assimilation in the Study of Regional and Global Variability of the Hydrological Cycle

In the coming years, researchers will have at their disposal a host of new observations from advanced space-based sensors (e.g. the Tropical Rainfall Measuring Mission, EOS Terra and PM missions) providing, among other things, a more complete and accurate description of various components of the Earth's hydrological cycle. Also, increasingly more sophisticated and comprehensive geophysical models will provide researchers better tools for simulating the hydrological cycle, and for carrying out mechanistic studies of the role of moist processes in the climate system. In addition, new data sets generated with global four-dimensional data assimilation (4DDA) systems will provide comprehensive and complete information on both the state and forcing of the climate system. Ideally, the 4DDA systems optimally incorporate all relevant information from the observations together with a first guess from a state-of-the-art geophysical model to produce a "best" estimate of the climate state. Furthermore, to the extent that the assimilating models are realistic and are constrained by the observations, they should provide reliable estimates of the associated physical processes or climate forcing fields. While operational weather centers now have a considerable history of providing reliable estimates of the basic atmospheric state variables, the associated processes or diagnostic fields (which are less well constrained by the observations and sensitive to errors in the model's physical parameterizations) are still considered experimental and of uncertain quality. In this study we will examine the current generation of reanalysis products to assess the capabilities of 4DDA systems to represent components of the hydrological cycle. The focus is on the role of the model in providing consistent estimates of moist processes. We will also assess whether current observations provide sufficient constraints on these model- generated fields.

Schubert, Siegfried↗
NASA NTRS1997

A Comparison of Geoid Undulations for West Central Greenland

The accuracy of a new local gravity field model, GEOID94A, is examined at a site on the western Greenland ice sheet. The model, developed by the Danish National Survey and Cadastre, incorporates several new gravity data sets including an extensive amount of airborne gravity data. Model-derived geoid undulations were compared to independently determined undulations found by differencing the elevations from Global Positioning System controlled airborne laser altimetry and optical leveling surveys. Differences between the two sets of undulations were less than +/- 6 cm RMS. The comparison improved (+/- 5 cm RMS) when GEOID94A undulations were adjusted by local gravity observations also acquired at the site. Our comparisons demonstrate that GEOID94A adequately models the long to intermediate wavelengths of the gravity field. We conclude that GEOID94A constitutes a reliable reference model for studies of Greenland's gravity field.

Roman, Daniel R.↗