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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Deep Space Atomic Clock Technology Demonstration Mission Onboard Navigation Analog Experiment

The timing and frequency stability provided by the Deep Space Atomic Clock (DSAC) is on par with the Deep Space Network’s ground clocks, and will enable one-way radiometric measurements with accuracy equivalent to current two-way tracking data. A demonstration unit of the clock will be launched into low Earth orbit in late 2016/early 2017 for the purpose of validating DSAC’s performance in the space environment. GPS data collected throughout the mission will be utilized not only for precise clock estimation, but also as a proxy for deep space tracking data. Through careful processing of GPS Doppler data and limited modeling fidelity representative of onboard capabilities, onboard orbit solutions can be compared to higher-fidelity ground solutions, demonstrating DSAC’s viability as an onboard navigation instrument in conditions typical for a low altitude Mars orbiter.

Seubert, Jill↗
DOE OSTI2023

DUNE Data Management: Network Visualization Monitoring Software

Fermilab’s fagship Deep Underground Neutrino Experiment (DUNE) seeks to better understand the nature of neutrinos within the context of Leptogenesis, neutrino oscillations, multi-messenger Astronomy, and other scientifc phenomena. The experiment will send a beam of neutrinos from the Fermilab site in Illinois to the Sanford Underground Neutrino Facility (SURF) in South Dakota, generating petabytes of scientifc data. Given the high volume of data expected when measurements begin at the end of the decade, DUNE computing and the data management group must carefully monitor data transfers across the 15 remote storage sites and, more generally, the 36 global DUNE computing sites. This report will describe both the frontend and backend data monitoring software designed to analyze and visualize these data transfers. Specifc emphasis will be placed on the software’s setup, usage, and methods for future implementations. The full software code can be found under the DUNE/data-mgmt-testing GitHub repository.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Strangeness in the proton from $W+$ charm production and SIDIS data

We perform a global QCD analysis of unpolarized parton distribution functions (PDFs) in the proton, including new 𝑊+⁢ charm production data from 𝑝⁢𝑝 collisions at the LHC and semi-inclusive pion and kaon production data in lepton-nucleon deep-inelastic scattering, both of which have been suggested for constraining the strange quark PDF. Compared with a baseline global fit that does not include these datasets, the new analysis reduces the uncertainty on the strange quark distribution over the range 0.01 < 𝑥 < 0.3, and provides a consistent description of processes sensitive to strangeness in the proton. Including the new datasets, the ratio of strange to nonstrange sea quark distributions is $R_s = (s + \bar{s})/(\bar{u} +\bar{d})$ $=$ {$0.7⁢2^{+0.52}_{−0.34}, 0.4⁢6^{+0.30}_{−0.20}, 0.3⁢2^{+0.23}_{−0.15}$} for 𝑥 ={$0.01, 0.04, 0.1$} at 𝑄 2 $=$ 4 GeV 2 . The data place more stringent constraints on the strange asymmetry $(s - \bar{s})$, which is found to be consistent with zero in this range.

Anderson, Trey [College of William and Mary, Willi↗
DOE OSTI2024

Predicting Li-ion Battery Performance for Impurity-doped NMC Cathodes Using Deep Learning

With the electric vehicle (EV) market expansion and the energy sector's shift towards electrification, the demand for battery metals, including lithium (Li), cobalt (Co), and nickel (Ni), is set to surpass supply. A critical knowledge gap exists in the purity standards for battery precursors and the impact of impurities on battery performance. Addressing this, our study employs a Deep Machine Learning (DL) based multi-objective optimization approach to interpret the relationship between metal impurities in domestic battery resources and their effects on battery performance. We analyze experimental data from Li-ion batteries with NMC (Nickel-Manganese-Cobalt oxide) cathodes over 1000 cycles, representing approximately ~6-8 months of operation, to establish a baseline of performance without impurities. Leveraging this data, we develop a Physics-Informed Deep Learning (PIDL) framework to extend our findings to cases that include metal impurities (e.g., Fe, Cu, Al) ranging from (0.001 - 0.01) %, respectively. By incorporating physics-based features, our PIDL model can accurately estimate the performance of NMC cathodes doped with various metal impurities to provide rapid design decisions. This research paves the way for informed decisions in Li-ion battery material design and optimization, ensuring the sustainable growth of the EV market and the broader energy sector.

25 ENERGY STORAGE↗
DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A Type–Curve Approach for Evaluating Aquifer Properties by Interpreting Shallow Strain Measured During Well Tests

Strains occur at shallow depths in response to pressure changes during well tests in an underlying aquifer, and recent developments in instrumentation have made it feasible to measure essentially the full strain tensor. Simulations using poroelastic analyses indicate that shallow normal strains are approximately proportional to the logarithm of time when a well is injecting into or pumping from a deep aquifer or reservoir. The drawdown is also a linear function of log time, as shown by the classic Cooper-Jacob type-curve analysis. The time when the semilog straight line intercepts the zero-strain axis is similar to the time determined from the Cooper-Jacob pressure analysis, and it can be used to estimate hydraulic diffusivity, suggesting that horizontal strain data can be used directly to estimate aquifer properties. This approach was validated using measurements from shallow (30-m deep) borehole strainmeters during an injection test at a 530-m-deep sandstone aquifer/reservoir in Oklahoma. The results show intercept times for the shallow normal strain data are essentially the same as for deep pressure data from an equivalent radial distance. The slopes of the semilog plots of the pressure and the strain increase at the same time, suggesting that they both respond to a lateral aquifer boundary. Significantly, though, strain was measured at shallow depths while the pressure data were measured at 530-m depth. This suggests that strain data from shallow depths could be an effective way to improve the characterization of an underlying aquifer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

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

This paper presents the second data release of the Hyper Suprime-Cam Subaru Strategic Program, a wide-field optical imaging survey using the 8.2 m Subaru Telescope. We report the release includes data from 174 nights of observation through 2018 January. The Wide layer data cover about 300 deg$^2$ in all five broad-band filters ($grizy$) to the nominal survey exposure (10 min in $gr$ and 20 min in $izy$). Partially observed areas are also included in the release; about 1100 deg$^2$ is observed in at least one filter and one exposure. The median seeing in the i-band is ${0_{.}^{\prime \prime }6}$, demonstrating the superb image quality of the survey. The Deep (26 deg$^2$) and UltraDeep (4 deg$^2$) data are jointly processed and the UltraDeep-COSMOS field reaches an unprecedented depth of $i\sim 28$ at $5 \, \sigma$ for point sources. In addition to the broad-band data, narrow-band data are also available in the Deep and UltraDeep fields. This release includes a major update to the processing pipeline, including improved sky subtraction, PSF modeling, object detection, and artifact rejection. The overall data quality has been improved, but this release is not without problems; there is a persistent deblender problem as well as new issues with masks around bright stars. The user is encouraged to review the issue list before utilizing the data for scientific explorations. All the image products as well as catalog products are available for download. The catalogs are also loaded into a database, which provides an easy interface for users to retrieve data for objects of interest. In addition to these main data products, detailed galaxy shape measurements withheld from Public Data Release 1 (PDR1) are now available to the community. The shape catalog is drawn from the S16A internal release, which has a larger area than PDR1 (160 deg$^2$). All products are available at the data release site, https://hsc-release.mtk.nao.ac.jp/.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Anomalous electroweak physics unraveled via evidential deep learning

The ever-growing ecosystem of beyond standard model (BSM) calculations and parametrizations has motivated the development of systematic methods for making quantitative cross-comparisons over the wide range of possible models, especially with controllable uncertainties. In this setting, the language of uncertainty quantification (UQ) furnishes useful metrics for assessing statistical overlaps and discrepancies among BSM and related models. In this study, we leverage recent machine learning (ML) developments in evidential deep learning (EDL) for UQ to separate data (aleatoric) and knowledge (epistemic) uncertainties in a model-discrimination setting. We construct several potentially BSM-motivated scenarios for the anomalous electroweak interaction (AEWI) of neutrinos with nucleons in deep inelastic scattering ( v DIS). These scenarios are then quantitatively mapped, as a demonstration, alongside Monte Carlo replicas of the CT18 PDFs used to calculate the $\varDelta \chi ^{2}$ statistic for a typical multi-GeV v DIS experiment, CDHSW. Our framework effectively highlights areas of model agreement and provides a classification of out-of-distribution (OOD) samples. By offering the opportunity to quantitatively understand model overlaps, the approach presented in this work can help facilitate efficient BSM model exploration and exclusion for future New Physics searches.

AI↗
NASA NTRS2008

Cassini Information Management System in Distributed Operations Collaboration and Cassini Science Planning

Launched on October 15, 1997, the Cassini-Huygens spacecraft began its ambitious journey to the Saturnian system with a complex suite of 12 scientific instruments, and another 6 instruments aboard the European Space Agencies Huygens Probe. Over the next 6 1/2 years, Cassini would continue its relatively simplistic cruise phase operations, flying past Venus, Earth, and Jupiter. However, following Saturn Orbit Insertion (SOI), Cassini would become involved in a complex series of tasks that required detailed resource management, distributed operations collaboration, and a data base for capturing science objectives. Collectively, these needs were met through a web-based software tool designed to help with the Cassini uplink process and ultimately used to generate more robust sequences for spacecraft operations. In 2001, in conjunction with the Southwest Research Institute (SwRI) and later Venustar Software and Engineering Inc., the Cassini Information Management System (CIMS) was released which enabled the Cassini spacecraft and science planning teams to perform complex information management and team collaboration between scientists and engineers in 17 countries. Originally tailored to help manage the science planning uplink process, CIMS has been actively evolving since its inception to meet the changing and growing needs of the Cassini uplink team and effectively reduce mission risk through a series of resource management validation algorithms. These algorithms have been implemented in the web-based software tool to identify potential sequence conflicts early in the science planning process. CIMS mitigates these sequence conflicts through identification of timing incongruities, pointing inconsistencies, flight rule violations, data volume issues, and by assisting in Deep Space Network (DSN) coverage analysis. In preparation for extended mission operations, CIMS has also evolved further to assist in the planning and coordination of the dual playback redundancy of highvalue data from targets such as Titan and Enceladus. This paper will outline the critical role that CIMS has played for Cassini in the distributed ops paradigm throughout operations. This paper will also examine the evolution that CIMS has undergone in the face of new science discoveries and fluctuating operational needs. And finally, this paper will conclude with theoretical adaptation of CIMS for other projects and the potential savings in cost and risk reduction that could potentially be tapped into by future missions.

Equils, Douglas J.↗
DOE OSTI2022

Power quality disturbances diagnosis: A 2D densely connected convolutional network framework

The fast and accurate diagnosis of power quality disturbances (PQD) aids in avoiding shutdowns and unnecessary procedures, concerning electric energy distribution systems. As such, a number of techniques have been tested and applied in order to reach this objective. Majority of the techniques applied are two-step based. On the first step, power quality disturbances features are extracted. Second step, considering features extracted, disturbance classification is implemented. Recently, relevant literature has presented data-driven signal processing-based approaches, as deep convolutional neural networks (DCNN), which can implement both processing steps while providing automated recognition of patterns and outliers in data. However, not considered by state-of-art, power quality disturbances are evolving in nature, while all possible regularities might not be represented in the dataset. In this work a 2 Dimension Densely Connected Convolutional Network (2D-DenseNet) framework is presented. Further, a case study with synthetic disturbance events are analyzed. Easy-to-implement formulation, built on the 2D-DenseNet, without hard-to-design parameters, highlight potential aspects for real-life implementation.

42 ENGINEERING↗
NASA NTRS1994

Development of a Prototype Real-Time Automated Filter for Operational Deep Space Navigation

Operational deep space navigation has in the past, and is currently, performed using systems whose architecture was designed to accommodate tape data transfers and computing environments with a tiny fraction of the current capability. Additionally, this architecture requires constant human supervision and intervention. A prototype for a system which allows relatively automated processing of radio metric data received in near real-time from NASA's Deep Space Network (DSN) without any redesign of the existing operational flow has been developed. This system will allow for more rapid response as well as much reduced staffing to support mission navigation operations.

deep↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

The GREATS H β + [O iii] luminosity function and galaxy properties at z ∼ 8: walking the way of JWST

ABSTRACT The James Webb Space Telescope will allow to spectroscopically study an unprecedented number of galaxies deep into the reionization era, notably by detecting [O iii]λλ4959, 5007, and H β nebular emission lines. To efficiently prepare such observations, we photometrically select a large sample of galaxies at z ∼ 8 and study their rest-frame optical emission lines. Combining data from the GOODS Re-ionization Era wide-Area Treasury from Spitzer (GREATS) survey and from HST we perform spectral energy distribution (SED) fitting, using synthetic SEDs from a large grid of photoionization models. The deep Spitzer/IRAC data combined with our models exploring a large parameter space enables to constrain the [O iii] + H β fluxes and equivalent widths for our sample, as well as the average physical properties of z ∼ 8 galaxies, such as the ionizing photon production efficiency with $\log (\xi _\mathrm{ion}/\mathrm{erg}^{-1}\mathrm{Hz})\ge 25.77$. We find a relatively tight correlation between the [O iii] + H β and UV luminosity, which we use to derive for the first time the [O iii]λλ4959, 5007 + H β luminosity function (LF) at z ∼ 8. The z ∼ 8 [O iii] + H β LF is higher at all luminosities compared to lower redshift, as opposed to the UV LF, due to an increase of the [O iii] + H β luminosity at a given UV luminosity from z ∼ 3 to z ∼ 8. Finally, using the [O iii] + H β LF, we make predictions for JWST/NIRSpec number counts of z ∼ 8 galaxies. We find that the current wide-area extragalactic legacy fields are too shallow to use JWST at maximal efficiency for z ∼ 8 spectroscopy even at 1 h depth and JWST pre-imaging to ≳30 mag will be required.

De Barros, S.↗
DOE OSTI2015

Probing Neutrino Properties with Long-Baseline Neutrino Beams

This final report on an Early Career Award grant began in April 15, 2010 and concluded on April 14, 2015. Alysia Marino's research is focussed on making precise measurements of neutrino properties using intense accelerator-generated neutrino beams. As a part of this grant, she is collaborating on the Tokai-to-Kamioka (T2K) long-baseline neutrino experiment, currently taking data in Japan, and on the Deep Underground Neutrino Experiment (DUNE) design effort for a future Long-Baseline Neutrino Facility (LBNF) in the US. She is also a member of the NA61/SHINE particle production experiment at CERN, but as that effort is supported by other funds, it will not be discussed further here. T2K was designed to search for the disappearance of muon neutrinos (ν μ ) and the appearance of electron neutrinos (ν e ), using a beam of muon neutrino beam that travels 295 km across Japan towards the Super-Kamiokande detector. In 2011 T2K first reported indications of ν e appearance, a previously unobserved mode of neutrino oscillations. In the past year, T2K has published a combined analysis of ν μ disappearance and ν e appearance, and began collecting taking data with a beam of anti-neutrinos, instead of neutrinos, to search for hints of violation of the CP symmetry of the universe. The proposed DUNE experiment has similar physics goals to T2K, but will be much more sensitive due to its more massive detectors and new higher-intensity neutrino beam. This effort will be very high-priority particle physics project in the US over the next decade.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).

Forestano, Roy T. (ORCID:0000000203552076)↗
NASA NTRS2016

Habitation Concepts for Human Missions Beyond Low-Earth-Orbit

The Advanced Concepts Office at the NASA Marshall Space Flight Center has been engaged for several years in a variety of study activities to help define various options for deep space habitation. This work includes study activities supporting asteroid, lunar and Mars mission activities for the Human spaceflight Architecture Team (HAT), the Deep Space Habitat (DSH) project, and the Exploration Augmentation Module (EAM) project through the NASA Advanced Exploration Systems (AES) Program. The missions under consideration required human habitation beyond low-Earth-orbit (LEO) including deep space habitation in the lunar vicinity to support asteroid retrieval missions, human and robotic lunar surface missions, deep space research facilities, Mars vehicle servicing, and Mars transit missions. Additional considerations included international interest and near term capabilities through the International Space Station (ISS) and Space Launch System (SLS) programs. A variety of habitat layouts have been considered, including those derived from the existing ISS systems, those that could be fabricated from SLS components, and other approaches. This paper presents an overview of several leading designs explored in late fiscal year (FY) 2015 for asteroid, lunar, and Mars mission habitats and identifies some of the known advantages and disadvantages inherent in each. Key findings indicate that module diameters larger than those used for ISS can offer lighter structures per unit volume, and sufficient volume to accommodate consumables for long-duration missions in deep space. The information provided with the findings includes mass and volume data that should be helpful to future exploration mission planning and deep space habitat design efforts.

Smitherman, David V.↗
NASA NTRS2010

Northrop Grumman TR202 LOX/LH2 Deep Throttling Engine Project Status

NASA's Propulsion and Cryogenic Advanced Development (PCAD) project is currently developing enabling propulsion technologies in support of the Exploration Initiative, with a particular focus on the needs of the Altair Project. To meet Altair requirements, several technical challenges need to be overcome, one of which is the ability for the lunar descent engine(s) to operate over a deep throttle range with cryogenic propellants. To address this need, PCAD has enlisted Northrop Grumman Aerospace Systems (NGAS) in a technology development effort associated with the TR202, a LOX/LH2 expander cycle engine driven by independent turbopump assemblies and featuring a variable area pintle injector similar to the injector used on the TR200 Apollo Lunar Module Descent Engine (LMDE). Since the Apollo missions, NGAS has continued to mature deep throttling pintle injector technology. The TR202 program has completed two phases of pintle injector testing. The first phase of testing used ablative thrust chambers and demonstrated igniter operation as well as stable performance at several power levels across the designed 10:1 throttle range. The second phase of testing was performed on a calorimeter chamber and demonstrated injector performance at various power levels (75%, 50%, 25%, 10%, and 7.5%) across the throttle range as well as chamber heat flux to show that the engine can close an expander cycle design across the throttle range. This paper provides an overview of the TR202 program. It describes the different phases of the program with the key milestones of each phase. It then shows when those milestones were met. Next, it describes how the test data was used to update the conceptual design and how the test data has created a database for deep throttling cryogenic pintle technology that is readily scaleable and can be used to again update the design once the Altair program's requirements are firm. The final section of the paper describes the path forward, which includes demonstrating continuously throttling with an actuator and pursuing a path towards integrated engine sea-level test-bed testing.

Gromski, J.↗