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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Hawthorne Nevada Deep Direct-Use Feasibility Study - Temperature and Geochemistry Well Data

The objective of this project is to use a multi-disciplinary, three-tiered approach to assess the geothermal resource and determine the feasibility of implementing a large-scale, direct-use facility for the Hawthorne Army Depot (HAD) and the various town and county facilities in Hawthorne, Nevada. This assessment directly targets a geothermal resource recently characterized by the Navy Geothermal Program Office (GPO) as part of a focused exploration and development campaign. This data batch contains: Zip file containing well and water data from 2002, including reports, isotope analysis, shallow well data, and data by well; Zip folder containing 2009 GPO well data including well logs, geochemistry, and mud logs; Compiled data from 2010 in excel and word format; 2010 Final data from TG, well HAD-1; Zip file containing PDFs and Excel files for wells Haw1, HAD1, HWADD2-3, HWAD4-5; Zip folder containing Word docs and images pertaining to final well reports and discussion.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Development of 3D Geological Model of Tuscarora Sandstone for Feasibility of Deep Direct-Use Geothermal at West Virginia University Main Campus

The subsurface uncertainty at West Virginia University Main Campus is dominated by the uncertainty in the projections of geofluid flowrate in the target formation, the Tuscarora Sandstone. In this paper, three cores from the heterogeneous reservoir, available through West Virginia Geologic and Economic Survey, are analyzed by performing core analysis using CT scanning and permeability measurements via minipermeameter. Additional geological data are collected through cores, published literature, seismic data, and nearby, existing wells to estimate thickness, fracture network configuration and geothermal gradient to minimize the uncertainty of well deliverability. Using these estimated reservoir properties; a 3D conceptual model for the proposed geothermal site is developed. This dataset includes a GRC conference publication along with the data used to produce results explained in the paper including minipermeability measurement data for Preston -119 core and thin section analysis photos and data for Clay-513 core.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗
DOE OSTI2022

INITIAL STUDIES OF CAVITY FAULT PREDICTION AT JEFFERSON LABORATORY

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach and report on initial results. Recent modifications to the low-level RF control system will provide access to streaming signals and we outline a path forward for leveraging deep learning on streaming data

Vidyaratne, Lasitha↗
DOE OSTI2021

INITIAL STUDIES OF CAVITY FAULT PREDICTION AT JEFFERSON LABORATORY

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach and report on initial results. Recent modifications to the low-level RF control system will provide access to streaming signals and we outline a path forward for leveraging deep learning on streaming data

Vidyaratne, Lasitha↗
NASA NTRS2004

Spitzer 24 Micron Observations of Optical/Near-Infrared-Selected Extremely Red Galaxies: Evidence for Assembly of Massive Galaxies at Z approximately equal to 1-2?

We carried out direct measurement of the fraction of dusty sources in a sample of extremely red galaxies with (R - Ks) >= 5.3 mag and Ks < 20:2 mag, using 24 micron data from the Spitzer Space Telescope. Combining deep 24 micron Ks- and R-band data over an area of ~64 arcmin(sup 2) in ELAIS N1 of the Spitzer First Look Survey (FLS), we find that 50% +/- 6% of our extremely red object (ERO) sample have measurable 24 micron flux above the 3 (sigma) flux limit of 40 (micro)Jy. This flux limit corresponds to a star formation rate (SFR) of 12 solar masses per year ~1, much more sensitive than any previous long-wavelength measurement. The 24 micron-detected EROs have 24 micron/2.2 micron and 24 micron/0.7 micron flux ratios consistent with infrared luminous, dusty sources at z >= 1, and are an order of magnitude too red to be explained by an infrared quiescent spiral or a pure old stellar population at any redshift. Some of these 24 micron-detected EROs could be active galactic nuclei; however, the fraction among the whole ERO sample is probably small, 10%-20%, as suggested by deep X-ray observations as well as optical spectroscopy. Keck optical spectroscopy of a sample of similarly selected EROs in the FLS field suggests that most of the EROs in ELAIS N1 are probably at z ~1. The mean 24 micron flux (167 (micro)Jy) of the 24 micron-detected ERO sample roughly corresponds to the rest-frame 12 micron luminosity, (nu)L(nu)(12 micron, of 3x10(exp 10)(deg) solar luminosities at z ~1. Using the c IRAS (nu)L(nu)(12 (micron) and infrared luminosity LIR(8-1000 (micron), we infer that the (LIR) of the 24 micron- detected EROs is 3 x 10(exp 11) and 1 x 10(exp 12) solar luminosities at z = 1.0 and similar to that of local luminous infrared galaxies (LIRGs) and ultraluminous infrared galaxies (ULIRGs). The corresponding SFR would be roughly 50-170 solar masses per year. If the timescale of this starbursting phase is on the order of 108 yr as inferred for the local LIRGs and ULIRGs, the lower limit on the masses of these 24 micron-detected EROs is 5 x 10(exp 9) to 2 x 10(exp 10) solar masses. It is plausible that some of the starburst EROs are in the midst of a violent transformation to become massive early type galaxies at the epoch of z ~1-2.

high redshift↗
NASA NTRS1973

Tracking and data system support for the pioneer project. Volume 11 Pioneers 6-9. Extended missions: 1 July 1971 - 1 July 1973

The Tracking and Data System supported the deep space phases of the Pioneer 6, 7, 8, and 9 missions, with two spacecraft in an inward trajectory and two spacecraft in an outward trajectory from the earth in heliocentric orbits. Scientific instruments aboard each of the spacecraft continued to register information relative to interplanetary particles and fields, and radio metric data generated by the network continued to improve our knowledge of the celestial mechanics of the solar system. In addition to network support activity detail, network performance and special support activities are covered.

Renzetti, N. A.↗
DOE OSTI2021

The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

Electrochemical impedance spectroscopy (EIS) is an experimental technique ubiquitously used to study electrochemical systems. However, conventional EIS data interpretation through physical and equivalent circuit models is challenging because physical models are problem-specific, and equivalent circuits are often just lumped-element analogs lacking physical meaning. The distribution of relaxation times (DRT) has emerged as a complementary approach to resolve these issues. One drawback of conventional DRT deconvolution is that the EIS data is understood to be (only) a function of frequency (i.e. 1D data) and deconvolved accordingly. This work proposes a novel deconvolution method based on deep neural networks (DNNs), allowing the analysis of multidimensional EIS spectra to bridge data dependency on both frequencies and experimental conditions. Two particularly appealing traits of the deep-DRT method developed in this article are that neither regularization nor specific spacing on the state variables defining the experiment are required. Finally, leveraging DNN to examine complex EIS spectra and their dependence on experimental conditions, this work opens a new research direction in the area of EIS analysis and DRT deconvolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2022

CLEAR: Boosted Lyα Transmission of the Intergalactic Medium in UV-bright Galaxies

Reionization is an inhomogeneous process, thought to begin in small ionized bubbles of the intergalactic medium (IGM) around overdense regions of galaxies. Recent Lyα studies during the epoch of reionization show evidence that ionized bubbles formed earlier around brighter galaxies, suggesting higher IGM transmission of Lyα from these galaxies. We investigate this problem using IR slitless spectroscopy from the Hubble Space Telescope (HST) Wide-Field Camera 3 (WFC3) G102 grism observations of 148 galaxies selected via photometric redshifts at 6.0 < z < 8.2. These galaxies have spectra extracted from the CANDELS Lyα Emission at Reionization (CLEAR) survey. We combine the CLEAR data for 275 galaxies with the Keck Deep Imaging Multi-Object Spectrograph and MOSFIRE data set from the Texas Spectroscopic Search for Lyα Emission at the End of Reionization Survey. We constrain the Lyα equivalent width (EW) distribution at 6.0 < z < 8.2, which is described by an exponential form, dN / dEW ∝ exp(-EW) W(sub 0), with the characteristic e-folding scale width (W(sub 0)). We confirm a significant drop in the Lyα strength (i.e., W(sub 0)) at z > 6. Furthermore, we compare the redshift evolution of W(sub 0) between galaxies at different UV luminosities. UV-bright (M(sub UV) < −21 [i.e., L(sub UV) > L*]) galaxies show weaker evolution with a decrease of 0.4 ( ± 0.2) dex in W(sub 0) at z > 6, while UV-faint (M(sub UV) > −21 [LUV < L*]) galaxies exhibit a significant drop of 0.7–0.8 (±0.2) dex in W(sub 0) from z < 6 to z > 6. If the change in W(sub 0) is proportional to the change in the IGM transmission for Lyα photons, then this is evidence that the transmission is “boosted” around UV-brighter galaxies, suggesting that reionization proceeds faster in regions around such galaxies.

Reionization↗
NASA NTRSDate not supplied

Assessing the Variability of Radiation, Water and Energy in the Deep Tropics Over the Last 3 Decades

Global surface energy closure and its variability depends heavily upon the surface radiative energy budget in the deep tropics. A key process observed is the change in convection regimes from disorganized convection to organized deep convection. During these periods an imbalance is observed between energy and water fluxes in terms of energy closure in some atmospheric reanalysis systems, implying that processes are not simulated well. A recent paper by Hsaio et al (2023, pre-published version) found that the transition process has wind shear and longwave cloud radiative feedback signatures. In this presentation, we assess the surface radiative budget in the deep tropics by comparing multiple data products (GEWEX SRB Rel4IP, CERES Ed4.1, ISSCP FH, etc.) and describing the variability across the deep tropics for the period from 1988 to near present. We assess this variability of the radiative flux anomalies (including the net TOA, surface and the atmospheric divergence fluxes) against water vapor divergence, cloud properties (include ISCCP “Weather States, Tselioudis et al. 2021) and larger scale wind shear. We include further analysis contrasting 6 key tropical oceanic regions (Indian Ocean east and west, tropical western, central and eastern Pacific Ocean, and Tropical Atlantic Ocean) and the sensitivity of the fluxes as a function of fluctuations in cloud types in response to various larger scale atmospheric processes (e.g. El Niño, Indian Ocean Dipole). These are contrasted against the modeled radiative fluxes from ERA-5 and MERRA-2. Ocean buoy measurements of radiative fluxes are utilized to help assess data radiative flux uncertainty over the nearly 40 year period. In general, the variability for overlap periods of these various data products agrees well, but there are significant differences in the net fluxes that vary according to the rendering of surface, atmospheric, aerosol and cloud properties. We conclude with recommendations for continuing work.

Radiation budget↗
NASA NTRS1974

Tracking and data system support for the Pioneer project. Pioneers 6-9, extended missions: 1 July 1972 - 1 July 1973, volume 12

The Tracking and Data System supported the deep space phases of the Pioneer 6, 7, 8, and 9 missions, with two spacecraft in an inward trajectory and two spacecraft in an outward trajectory from the earth in heliocentric orbits. During the period of this report, scientific instruments aboard each of the spacecraft continued to register information relative to interplanetary particles and fields, and radiometric data generated by the network continued to contribute to knowledge of the celestial mechanics of the solar system. In addition, to network support activity detail, network performance and special support activities are covered.

Miller, R. B.↗
DOE OSTI2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗
DOE OSTI2023

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity, via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias–variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Furthermore, our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.

42 ENGINEERING↗
DOE OSTI2015

SHARDS: A global view of the star formation activity at z ∼ 0.84 and z ∼ 1.23

In this paper, we present a comprehensive analysis of star-forming galaxies (SFGs) at intermediate redshifts (z ∼ 1). We combine the ultra-deep optical spectro-photometric data from the Survey for High-z Absorption Red and Dead Sources (SHARDS) with deep UV-to-FIR observations in the GOODS-N field. Exploiting two of the 25 SHARDS medium-band filters, F687W17 and F823W17, we select [O ii] emission line galaxies at z ∼ 0.84 and z ∼ 1.23 and characterize their physical properties. Their rest-frame equivalent widths (EW{sub rf}([O ii])), line fluxes, luminosities, star formation rates (SFRs), and dust attenuation properties are investigated. The evolution of EW{sub rf}([O ii]) closely follows the SFR density evolution of the universe, with a trend of EW{sub rf}([O ii]) ∝ (1 + z){sup 3} up to redshift z ≃ 1, followed by a possible flattening. The SF properties of the galaxies selected on the basis of their [O ii] emission are compared with complementary samples of SFGs selected by their MIR and FIR emission, and also with a general mass-selected sample of galaxies at the same redshifts. We demonstrate observationally that the UVJ diagram (or, similarly, a cut in the specific SFR) is only partially able to distinguish the quiescent galaxies from the SFGs. The SFR–M{sub *} relation is investigated for the different samples, yielding a logarithmic slope ∼1, in good agreement with previous results. The dust attenuations derived from different SFR indicators (UV(1600), UV(2800), [O ii], IR) are compared and show clear trends with respect to both the stellar mass and total SFR, with more massive and highly star-forming galaxies being affected by stronger dust attenuation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Randomized probe imaging through deep k-learning

Randomized probe imaging (RPI) is a single-frame diffractive imaging method that uses highly randomized light to reconstruct the spatial features of a scattering object. The reconstruction process, known as phase retrieval, aims to recover a unique solution for the object without measuring the far-field phase information. Typically, reconstruction is done via time-consuming iterative algorithms. In this work, we propose a fast and efficient deep learning based method to reconstruct phase objects from RPI data. The method, which we call deep k-learning, applies the physical propagation operator to generate an approximation of the object as an input to the neural network. This way, the network no longer needs to parametrize the far-field diffraction physics, dramatically improving the results. Deep k-learning is shown to be computationally efficient and robust to Poisson noise. The advantages provided by our method may enable the analysis of far larger datasets in photon starved conditions, with important applications to the study of dynamic phenomena in physical science and biological engineering.

Guo, Zhen (ORCID:0000000213473451)↗
DOE OSTI2025

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects.

47 OTHER INSTRUMENTATION↗