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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

New observations of interstellar abundances and depletions of boron, vanadium, chromium, and cobalt

New observations of interstellar lines of boron, vanadium, chromium, and cobalt in the spectra of Zeta Oph and Xi Per have been obtained with the Copernicus satellite. Chromium has been detected for the first time toward a reddened star, and cobalt has been seen for the first time in any interstellar line of sight. New limits have been obtained for boron and vanadium. These new data, along with limits on scandium and other species from the literature, have been compared with models for the depletion process. No fully conclusive test of depletion models is yet possible, but the new data on boron appear to favor the hypothesis that the depletions are dominated by accretion of gas-phase particles onto grains, rather than being due to grain condensation under pressure equilibrium. The impact of these new data on the study of grain surface properties is described.

Snow, T. P., Jr.↗
NASA NTRS1998

Evaluation of risk from space radiation with high-energy heavy ion beams

The most challenging radiation in space consists of fully ionized atomic elements with high energy for which only the few lowest energy ions can be stopped in shielding materials. The health risk from exposure to these ions and their secondary radiations generated in shield materials is poorly understood since there are few human data and a systematic study in relevant animal model systems has not been made. The accuracy of risk prediction is described as the major limiting factor in the management of space radiation risk. The expected impact of systematic studies is examined using the limited available biological data and models. Given the limitations of current predictions, models must be developed that are able to incorporate the required fundamental scientific data into accurate risk estimates. The important radiation components that can be provided for laboratory testing are identified. The use of ground-based accelerator beams to simulate space radiation is explained and quantitative scientific constraints on such facilities are derived. Three facilities, one each in the United States, in Germany and in Japan, currently have the partial capability to satisfy these constraints. A facility has been proposed using the Brookhaven National Laboratory Booster Synchrotron in the United States; in conjuction with other on-site accelerators, it will be able to provide the full range of heavy ion beams and energies required.

NASA Discipline Radiation Health↗
NASA NTRS1989

Tank Pressure Control Experiment on the Space Shuttle

The tank pressure control experiment is a demonstration of NASA intent to develop new technology for low-gravity management of the cryogenic fluids that will be required for future space systems. The experiment will use freon as the test fluid to measure the effects of jet-induced fluid mixing on storage tank pressure and will produce data on low-gravity mixing processes critical to the design of on-orbit cryogenic storage and resupply systems. Basic data on fluid motion and thermodynamics in low gravity is limited, but such data is critical to the development of space transfer vehicles and spacecraft resupply facilities. An in-space experiment is needed to obtain reliable data on fluid mixing and pressure control because none of the available microgravity test facilities provide a low enough gravity level for a sufficient duration to duplicate in-space flow patterns and thermal processes. Normal gravity tests do not represent the fluid behavior properly; drop-tower tests are limited in length of time available; aircraft low-gravity tests cannot provide the steady near-zero gravity level and long duration needed to study the subtle processes expected in space.

Source record↗
DOE OSTI2023

BEYONDPLANCK XIII. Intensity foreground sampling, degeneracies, and priors

We present the intensity foreground algorithms and model employed within the BEYONDPLANCK analysis framework. The BEYONDPLANCK analysis is aimed at integrating component separation and instrumental parameter sampling within a global framework, leading to complete end-to-end error propagation in the Planck Low Frequency Instrument (LFI) data analysis. Given the scope of the BEYONDPLANCK analysis, a limited set of data is included in the component separation process, leading to foreground parameter degeneracies. In order to properly constrain the Galactic foreground parameters, we improve upon the previous Commander component separation implementation by adding a suite of algorithmic techniques. These algorithms are designed to improve the stability and computational efficiency for weakly constrained posterior distributions. These are: (1) joint foreground spectral parameter and amplitude sampling, building on ideas from MIRAMARE; (2) component-based monopole determination; (3) joint spectral parameter and monopole sampling; and (4) application of informative spatial priors for component amplitude maps. We find that the only spectral parameter with a significant signal-to-noise ratio using the current BEYONDPLANCK data set is the peak frequency of the anomalous microwave emission component, for which we find $ν$ p = 25.3 ± 0.5 GHz; all others must be constrained through external priors. Future works will be aimed at integrating many more data sets into this analysis, both map and time-ordered based, thereby gradually eliminating the currently observed degeneracies in a controlled manner with respect to both instrumental systematic effects and astrophysical degeneracies. When this happens, the simple LFI-oriented data model employed in the current work will need to be generalized to account for both a richer astrophysical model and additional instrumental effects. This work will be organized within the Open Science-based COSMOGLOBE community effort.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Third data release of the Hyper Suprime-Cam Subaru Strategic Program

This work presents the third data release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), a wide-field multi-band imaging survey with the Subaru 8.2 m telescope. HSC-SSP has three survey layers (Wide, Deep, and UltraDeep) with different area coverages and depths, designed to address a wide array of astrophysical questions. This third release from HSC-SSP includes data from 278 nights of observing time and covers about 670 deg 2 in all five broad-band filters (grizy) at the full depth (~26 mag at 5σ depending on filter) in the Wide layer. If we include partially observed areas, the release covers 1470 deg 2 . The Deep and UltraDeep layers have ~80% of the originally planned integration times, and are considered done, as we have slightly changed the observing strategy in order to compensate for various time losses. There are a number of updates in the image processing pipeline. Of particular importance is the change in the sky subtraction algorithm; we subtract the sky on small scales before the detection and measurement stages, which has significantly reduced the number of false detections. Thanks to this and other updates, the overall quality of the processed data has improved since the previous release. However, there are limitations in the data (for example, the pipeline is not optimized for crowded fields), and we encourage the user to check the quality assurance plots as well as a list of known issues before exploiting the data. The data release website is https://hsc-release.mtk.nao.ac.jp.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Exploring the Three-Dimensional Momentum Distribution of Longitudinally Polarized Quarks in the Proton

By analyzing experimental data on semi-inclusive deep inelastic scattering off longitudinally polarized targets, we extract the transverse momentum dependence of the quark helicity distribution, i.e., the difference between the three-dimensional motion of quarks with polarization parallel or antiparallel to the longitudinal polarization of the parent hadron. We perform the analysis at next-to-leading and next-to-next-to-leading perturbative accuracy. The quality of the fit is very good for both cases, reaching a χ 2 per number of data points equal to 1.11 and 1.09, respectively. Although the limited number of data points leads to significant uncertainties, the data are consistent with an interpretation in which the helicity distribution is narrower in transverse momentum than the unpolarized distribution. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Abstract. Quantifying the spatiotemporal dynamics in subsurface hydrological flows over a long time window usually employs a network of monitoring wells. However, such observations are often spatially sparse with potential temporal gaps due to poor quality or instrument failure. In this study, we explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. We use a well network that monitors the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer at the U.S. Department of Energy's Hanford site. This 10-year-long dataset contains hourly temperature, specific conductance, and groundwater table elevation measurements from 42 wells with gaps of various lengths. We employ a long short-term memory (LSTM) model to capture the temporal variations in the observed system behaviors needed for gap filling. The performance of the LSTM-based gap-filling method was evaluated against a traditional autoregressive integrated moving average (ARIMA) method in terms of error statistics and accuracy in capturing the temporal patterns of river corridor wells with various dynamics signatures. Our study demonstrates that the ARIMA models yield better average error statistics, although they tend to have larger errors during time windows with abrupt changes or high-frequency (daily and subdaily) variations. The LSTM-based models excel in capturing both high-frequency and low-frequency (monthly and seasonal) dynamics. However, the inclusion of high-frequency fluctuations may also lead to overly dynamic predictions in time windows that lack such fluctuations. The LSTM can take advantage of the spatial information from neighboring wells to improve the gap-filling accuracy, especially for long gaps in system states that vary at subdaily scales. While LSTM models require substantial training data and have limited extrapolation power beyond the conditions represented in the training data, they afford great flexibility to account for the spatial correlations, temporal correlations, and nonlinearity in data without a priori assumptions. Thus, LSTMs provide effective alternatives to fill in data gaps in spatially distributed time-series observations characterized by multiple dominant frequencies of variability, which are essential for advancing our understanding of dynamic complex systems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Noise characteristics of upper surface blown configurations. Experimental program and results

An experimental data base was developed from the model upper surface blowing (USB) propulsive lift system hardware. While the emphasis was on far field noise data, a considerable amount of relevant flow field data were also obtained. The data were derived from experiments in four different facilities resulting in: (1) small scale static flow field data; (2) small scale static noise data; (3) small scale simulated forward speed noise and load data; and (4) limited larger-scale static noise flow field and load data. All of the small scale tests used the same USB flap parts. Operational and geometrical variables covered in the test program included jet velocity, nozzle shape, nozzle area, nozzle impingement angle, nozzle vertical and horizontal location, flap length, flap deflection angle, and flap radius of curvature.

Brown, W. H.↗
NASA NTRS1984

Ground vibration test of F-16 airplane with initial decoupler pylon

A ground vibration test was conducted on an F-16 airplane loaded on each wing with a 370-gal tank mounted on a standard pylon, a GBU-8 store mounted on a decoupler pylon, and an AIM-9J missile mounted on a wing-tip launcher. The decoupler pylon is a passive wing/store flutter-suppression device. The test was conducted prior to initial flight tests to determine the modal frequencies, mode shapes, and structural damping coefficients. The data presented include frequency response plots, force effect plots, and limited mode shape data.

Cazier, F. W., Jr.↗
NASA NTRS1996

Data Analysis and Theoretical Studies of the Upper Mesosphere and Lower Thermosphere

Three separate tasks were proposed under this award. The first involved extending our continuing study of electrodynamical feedback between the thermosphere/ionosphere and the magnetosphere. The second was a model-experiment comparison study of global dynamics and the third was a 'spectral energetics' analysis of tidal dissipation and energy exchange mechanisms. The Earth's mesosphere and lower-thermosphere/ionosphere (MLTI), between approximately 60 and 180 km altitude, is the most poorly understood region of the Earth's atmosphere, primarily because of its relative inaccessibility. This lack of knowledge has been widely recognized and has provided important scientific rationale for the upcoming NASA TIMED mission. While the data gathered during the TIMED era will revolutionize our understanding of the MLTI region, much work can be done prior to the mission, both to develop data-analysis and modeling techniques and to study the more limited relevant experimental data from previous missions. The grant reported on here continues and extends an existing successful program of scientific research into the energetics, dynamics and electrodynamics of the MLTI, using available theoretical and data analysis tools.

Burns, Alan↗
NASA NTRS2012

Revised Toxicological Assessment of ISS Air Quality: May 2012 - August 2012

A summary of the analytical results from 12 grab sample containers (GSCs) collected on ISS and returned aboard 30S is shown in Table 1. The average recoveries of the 3 surrogate standards from the GSCs were as follows: 12C-acetone, 115 +/-- 11%; fluorobenzene, 108 +/- 8%; and chlorobenzene, 102 +/- 16%. Shaded rows indicate data that re limited due to low sample pressures. For completeness, previously reported data from the US Lab collected in May 2012 are included here as well. The revised report provides results from one returned sample that was unlabeled and originally assumed to be unused. The sample was prepared and analyzed for the purpose of measuring the surrogate compounds. It was later determined, based on serial number, that this was the HTB3 first ingress sample.

Meyers, Valerie↗
NASA NTRS2015

3-D CFD Simulation and Validation of Oxygen-Rich Hydrocarbon Combustion in a Gas-Centered Swirl Coaxial Injector using a Flamelet-Based Approach

Injector design is a critical part of the development of a rocket Thrust Chamber Assembly (TCA). Proper detailed injector design can maximize propulsion efficiency while minimizing the potential for failures in the combustion chamber. Traditional design and analysis methods for hydrocarbon-fuel injector elements are based heavily on empirical data and models developed from heritage hardware tests. Using this limited set of data produces challenges when trying to design a new propulsion system where the operating conditions may greatly differ from heritage applications. Time-accurate, Three-Dimensional (3-D) Computational Fluid Dynamics (CFD) modeling of combusting flows inside of injectors has long been a goal of the fluid analysis group at Marshall Space Flight Center (MSFC) and the larger CFD modeling community. CFD simulation can provide insight into the design and function of an injector that cannot be obtained easily through testing or empirical comparisons to existing hardware. However, the traditional finite-rate chemistry modeling approach utilized to simulate combusting flows for complex fuels, such as Rocket Propellant-2 (RP-2), is prohibitively expensive and time consuming even with a large amount of computational resources. MSFC has been working, in partnership with Streamline Numerics, Inc., to develop a computationally efficient, flamelet-based approach for modeling complex combusting flow applications. In this work, a flamelet modeling approach is used to simulate time-accurate, 3-D, combusting flow inside a single Gas Centered Swirl Coaxial (GCSC) injector using the flow solver, Loci-STREAM. CFD simulations were performed for several different injector geometries. Results of the CFD analysis helped guide the design of the injector from an initial concept to a tested prototype. The results of the CFD analysis are compared to data gathered from several hot-fire, single element injector tests performed in the Air Force Research Lab EC-1 test facility located at Edwards Air Force Base.

Richardson, Brian↗
DOE OSTI2022

Do Neural Networks Trained with Topological Features Learn Different Internal Representations?

There is a growing body of work that leverages features extracted via topological data analysis to train machine learning models. While this field, sometimes known as topological machine learning (TML), has seen some notable successes, an understanding of how the process of learning from topological features differs from the process of learning from raw data is still limited. In this work, we begin to address one component of this larger issue by asking whether a model trained with topological features learns internal representations of data that are fundamentally different than those learned by a model trained with the original raw data. To quantify ``different'', we exploit two popular metrics that can be used to measure the similarity of the hidden representations of data within neural networks, neural stitching and centered kernel alignment. From these we draw a range of conclusions about how training with topological features does and does not change the representations that a model learns. Perhaps unsurprisingly, we find that structurally, the hidden representations of models trained and evaluated on topological features differ substantially compared to those trained and evaluated on the corresponding raw data. On the other hand, our experiments show that in some cases, these representations can be reconciled (at least to the degree required to solve the corresponding task) using a simple affine transformation. We conjecture that this means that neural networks trained on raw data may extract some limited topological features in the process of making predictions.

McGuire, Sarah L.↗
DOE OSTI2020

Mobile Sensing for Wind Field Estimation in Wind Farms: Preprint

This paper introduces a novel approach for estimating the wind field over an entire wind farm using a mobile sensor to collect limited amounts of data. The proposed method estimates the boundary conditions of a simplified turbine wake model by computing the model sensitivity matrix and using a recursive least-squares algorithm to recover the model parameters from the wind field measurements. To address the fact that it is not practical to take measurements across the entire wind farm, the proposed method classifies each area on the map based on its sensitivity to parameter variations. This classification is then used to generate a suitable path for a mobile sensor, which is charged with collecting data for the recursive least-squares algorithm. The proposed framework can successfully estimate the model boundary conditions using just the measurements collected along the path of the mobile sensor. This preliminary result paves the way for using real-time wind field estimates for the coordinated control of all the turbines within a wind farm.

estimation↗
DOE OSTI2024

Multi-fidelity Fourier neural operator for fast modeling of large-scale geological carbon storage

Deep learning-based surrogate models have been widely applied in geological carbon storage (GCS) problems to accelerate the prediction of reservoir pressure and CO2 plume migration. Large amounts of data from physics-based numerical simulators are required to train a model to accurately predict the complex physical behaviors associated with this process. In practice, the available training data are always limited in large-scale 3D problems due to the high computational cost. Therefore, we propose to use a multi-fidelity Fourier neural operator (FNO) to solve large-scale GCS problems with more affordable multi-fidelity training datasets. FNO has a desirable grid-invariant property, which simplifies the transfer learning procedure between datasets with different discretization. Here, we first test the model efficacy on a GCS reservoir model being discretized into 110 k grid cells. The multi-fidelity model can predict with accuracy comparable to a high-fidelity model trained with the same amount of high-fidelity data with 81% less data generation costs. We further test the generalizability of the multi-fidelity model on a same reservoir model with a finer discretization of 1 million grid cells. This case was made more challenging by employing high-fidelity and low-fidelity datasets generated by different geostatistical models and reservoir simulators. We observe that the multi-fidelity FNO model can predict pressure fields with reasonable accuracy even when the high-fidelity data are extremely limited. The findings of this study can help for better understanding of the transferability of multi-fidelity deep learning surrogate models.

58 GEOSCIENCES↗
NASA NTRS1985

Fundamental Algorithms of the Goddard Battery Model

The Goddard Space Flight Center (GSFC) is currently producing a computer model to predict Nickel Cadmium (NiCd) performance in a Low Earth Orbit (LEO) cycling regime. The model proper is currently still in development, but the inherent, fundamental algorithms (or methodologies) of the model are defined. At present, the model is closely dependent on empirical data and the data base currently used is of questionable accuracy. Even so, very good correlations have been determined between model predictions and actual cycling data. A more accurate and encompassing data base has been generated to serve dual functions: show the limitations of the current data base, and be inbred in the model properly for more accurate predictions. The fundamental algorithms of the model, and the present data base and its limitations, are described and a brief preliminary analysis of the new data base and its verification of the model's methodology are presented.

Jagielski, J. M.↗
DOE OSTI2026

Machine learning for seismic low-frequency extrapolation

The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.

58 GEOSCIENCES↗