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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Realizing High-Accuracy Low-Cost Measurement and Verification for Deep Cost Savings (BPA Cost Share CRADA)

Developed practitioner workflows that integrate advanced M&V tools with supplementary automated routines for non-routine adjustments and for quantification of savings uncertainty to indicate rigor. Demonstrated these workflows with Seattle City Light, in collaboration with their implementers and evaluators – determine labor and time/cost savings, and uncertainty of the results obtained. Engaged the Pacific Northwest regulatory community to determine acceptance criteria for the required accuracy and reporting of advanced whole-building M&V. Documented the findings to facilitate broad industry uptake of the solutions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2006

Effects of Nuclear Cross Sections at Different Energies on the Radiation Hazard from Galactic Cosmic Rays

The radiation hazard for astronauts from galactic cosmic rays is a major obstacle in long duration human space explorations. Space radiation transport codes have been developed to calculate radiation environment on missions to the Moon, Mars or beyond. We have studied how uncertainties in fragmentation cross sections at different energies affect the accuracy of predictions from such radiation transport. We find that, in deep space, cross sections between 0.3 and 0.85 GeV/u usually have the largest effect on dose-equivalent behind shielding in solar minimum GCR environments, and cross sections between 0.85 and 1.2 GeV/u have the largest effect in solar maximum GCR environments. At the International Space Station, cross sections at higher energies have the largest effect due to the geomagnetic cutoff.

Lin, Z. W.↗
DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Modeling glass degradation and release of radionuclides from vitrified waste for performance assessment simulations

The release of radionuclides initially encapsulated in a slowly degrading solid waste form and contained in an eventually corroding canister defines the source term for numerical simulations for the assessment of a geologic repository for high-level radioactive waste. While the details of waste degradation, canister corrosion, and dissolution and mobilization of the radionuclides in pore water include complex chemical reaction and transport processes that are coupled to the thermal, hydrological, microbiological, and mechanical conditions in the repository, the source-term model suitable for use in a numerical performance assessment model should be a defensible abstraction of these mechanisms. We developed a radiological source-term model and implemented it into a non-isothermal flow and transport simulator. While the proposed source-term model is applicable to various waste forms, canister systems, and disposal concepts, we specifically considered radionuclide releases from vitrified high-level waste placed in a cylindrical canister disposed in a deep vertical borehole repository. In this model, waste degradation is a function of temperature, and it can be adjusted to evaluate the influence of and propagate uncertainties in pH, passivation reactions, and chemical conditions as well as geometrical factors. The time-dependent, congruent release of safety-relevant radionuclides present in the decaying inventory is then calculated. Finally, the radionuclides are mobilized by diffusive and advective transport according to the thermo-hydraulic conditions prevailing in the near field of the repository, from where they migrate through the geosphere to the accessible environment. We examine the influence of the source-term model’s parameters on performance assessment calculations through sensitivity and uncertainty propagation analyses, identifying influential factors and confirming the upper bound of their impact. These considerations align with the overarching goal of repository design, which is to demonstrate that engineered and natural barriers can collectively delay radionuclide migration for timescales far exceeding human planning, thereby providing multiple, redundant barriers against environmental contamination.

iTOUGH2↗
DOE OSTI2023

Enabling site-specific well leakage risk estimation during geologic carbon sequestration using a modular deep-learning-based wellbore leakage model

Geologic carbon sequestration (GCS) is a promising technology for mitigating net carbon emissions and growing climate concern by storing CO 2 in reservoirs. Oil and gas brownfields are an attractive option for CO 2 storage, but these sites have many historical wellbores from petroleum production and can be a potential leakage pathway for CO 2 or formation brine. Therefore, risk management of GCS operations requires an assessment of potential well leakage. Due to the high uncertainty of the system, stochastic approaches are ideal for quantifying the range of risk behaviors, but they must be computationally efficient in the face of complex physics. Here, we develop a new physics-centric deep learning wellbore model to predict the leakage of CO 2 and brine through leaky wellbores. Multi-physics numerical simulations were used to generate data sets, and physics-informed features were introduced. Neural networks were optimized with an automated searching algorithm. Feature analysis quantifies the impact of each feature on model prediction and confirms the role of physics-inspired parameters. The model shows high predictive performance across a wide range of geologic and injection conditions and well attributes. In conclusion, a case study illustrates how the model is applied to assess well leakage in GCS operations.

58 GEOSCIENCES↗
NASA NTRS2010

A Rat Body Phantom for Radiation Analysis

To reduce the uncertainties associated with estimating the biological effects of ionizing radiation in tissue, researchers rely on laboratory experiments in which mono-energetic, single specie beams are applied to cell cultures, insects, and small animals. To estimate the radiation effects on astronauts in deep space or low Earth orbit, who are exposed to mixed field broad spectrum radiation, these experimental results are extrapolated and combined with other data to produce radiation quality factors, radiation weighting factors, and other risk related quantities for humans. One way to reduce the uncertainty associated with such extrapolations is to utilize analysis tools that are applicable to both laboratory and space environments. The use of physical and computational body phantoms to predict radiation exposure and its effects is well established and a wide range of human and non-human phantoms are in use today. In this paper, a computational rat phantom is presented, as well as a description of the process through which that phantom has been coupled to existing radiation analysis tools. Sample results are presented for two space radiation environments.

Qualls, Garry D.↗
DOE OSTI2023

Mind the gap: the discrepancy between simulation and reality drives interpretations of the Galactic Center Excess

The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (γEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of γEMs with increasing parametric freedom. We calculate the fractional contribution (f src ) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the γEM. For the simplest γEM, we obtain fsrc = 0.10 ± 0.07, while the most complex model yields f src = 0.79 ± 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed γEM. The quoted results for f src do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated γEM iterations. We quantify the reality gap between our γEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed γEMs have gaps to reality. Finally, our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked "out of domain" uncertainties in future interpretations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

Ensemble-based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process-based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite-dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet-based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling-Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics-informed, seq-to-seq streamflow forecasting, which further reduces biases in the pre-trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Simultaneous prediction of structural properties in epitaxially–grown GaN with quantum and conventional multi–output learning algorithms

Hundreds of GaN thin film crystal plasma–assisted molecular beam epitaxy synthesis experiment records spanning two decades were organized into a dataset correlating the growth experiment design parameters with discrete, binary determinations of crystallinity and surface morphology. Conventional data science techniques as well as both quantum and classical multi–output supervised machine learning algorithms were implemented to investigate the relationships between the operating parameter data and the structural figures of merit. Correlation coefficients, decision tree nodes, p–values, and SHAP values all support substrate temperature and gallium effusion cell conditions as being statistically significant for simultaneously influencing GaN crystallinity and surface morphology. Here, a conventional deep neural network learned best from the data, followed by a quantum–classical hybrid gradient boosting algorithm. When combined with calculations of uncertainty intervals based on VennAbers predictors, machine learning predictions of both structural properties show good agreement with results reported in published experimental literature.

36 MATERIALS SCIENCE↗
NASA NTRS1988

X-band noise temperature near the Sun at a 34-meter high efficiency antenna

The X-band system noise temperature near the Sun was measured at the DSS 15, a 34 meter high efficiency (HEF) antenna, in November 1987. Data was taken at angles off the center of the Sun from 0 to 4 degrees. At angles greater than 0.5 degree, the measured results agree with Voyager tracking data taken at solar conjunction in late December 1987. Within the solar disk, at angles lower than 0.27 degree, the temperature measured was lower than the prediction of a model by 30 percent, after adjusting for known receiver nonlinearities. The discrepancy in this extreme case is probably caused by unknown nonlinearities in the receiver, uncertainties in the model, or both. The measurement is nevertheless credible for practically all Sun-Earth-probe angles of interest to deep space missions.

Rebold, T. A.↗
NASA NTRS2007

Impacts of Center of Mass Shifts on Messenger Spacecraft Operations

The MESSENGER (MErcury Surface, Space ENvironment, GEochemistry, and Ranging) has successfully completed its first three years of flight operations following launch on August 3, 2004. As part of NASA s Discovery Program, MESSENGER will observe Mercury during flybys in 2008 and 2009, as well as from orbit beginning in March 2011. This paper discusses the impact that center of mass (CM) location changes have had on many mission activities, particularly angular momentum management and maneuver execution. Momentum trends were altered significantly following the first deep-space maneuver, and these changes were related to a change in the CM. The CM location also impacts maneuver execution, and uncertainties in its location led to the significant direction errors experienced at trajectory correction maneuver 11. Because of the spacecraft sensitivity to CM location, efforts to estimate its position are important to momentum and maneuver prediction. This paper summarizes efforts to estimate the CM from flight data, as well as the operational strategy to handle CM uncertainties and their impact on momentum trends and maneuver execution accuracy.

O'Shaughnessy, D. J.↗
NASA NTRS2015

A Journey with MOM

In late 2013, the Indian Space Research Organization (ISRO) launched its "Mars Orbiter Mission" (MOM). ISRO engaged NASA's Jet Propulsion Laboratory (JPL) for navigation services to support ISRO's objectives of MOM achieving and maintaining Mars orbit. The navigation support included planning, documentation, testing, orbit determination, maneuver design /analysis, and tracking data analysis. Several of MOM's attributes had an impact on navigation processes, e.g., S -band telecommunications, Earth Orbit Phase maneuvers, and frequent angular momentum desaturation s (AMDs). The primary source of tracking data was NASA/ JPL's Deep Space Network (DSN); JPL also conducted a performance assessment of Indian Deep Space Network (IDSN) tracking data. Planning for the Mars Orbit Insertion (MOI) was complicated by a pressure regulator failure that created uncertainty regarding MOM's main engine and raised potential planetary protection issues. A successful main engine test late on approach resolved these issues; it was quickly followed by a successful MOI on 24-September - 2014 at 02:00 UTC. Less than a month later, Comet Siding Spring's Mars flyby necessitated plans to minimize potential spacecraft damage. At the time of this writing, MOM's orbital operations continue, and plans to extend JPL 's support are in progress. This paper covers the JPL 's support of MOM through the Comet Siding Spring event.

Mars orbiter↗
DOE OSTI2022

Global analyses of nuclear PDFs with heavy-quark and neutrino data

We discuss the two most recent global analyses of nuclear parton distribution functions within the nCTEQ approach. LHC data on $W/Z$-boson, single-inclusive hadron and heavy quark/quarkonium production are shown to not only significantly reduce the gluon uncertainty down to $x\geq10^{-5}$, but to also influence the strange quark density. The latter is further constrained by neutrino deep-inelastic scattering and charm dimuon production data, whose consistency with neutral-current experiments is also re-evaluated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Deep spectral CNN for laser induced breakdown spectroscopy

This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, ‘Curiosity’.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

DIGS: deep inference of galaxy spectra with neural posterior estimation

Abstract With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of simulation-based inference (SBI) and amortized neural posterior estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is (a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and (b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with Markov chain Monte Carlo (MCMC) methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2 × 10 6 spectra (corresponding to a five-parameter SED model) with NPE. We show that SBI—with its combination of fast and amortized posterior estimations—is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modelling with Bayesian MCMC methods (e.g. 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9–1.2 × 10 5 galaxy spectra via SBI/NPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

spectroscopy↗
DOE OSTI2020

Measurement of the Ratio of the Neutron to Proton Structure Functions, and the Three-Nucleon EMC Effect in Deep Inelastic Electron Scattering Off Tritium and Helium-3 Mirror Nuclei

The proton and neutron structure functions Fp 2 and Fn 2, respectively are fundamental to understanding many studies in nuclear physics. They provide important information about quark distributions. For example, the ratio Fn 2/Fp 2 is one of the best measurements to find the ratio of d quark over u quark distribution inside the proton. While the calculations of structure functions and quark distributions are non-perturbative, they can be determined by the parameterization of experimental data. The understanding of Fn 2/Fp 2 and d/u as x -> 1 has a large influence on global fits and parameterization, and can be used to distinguish the non-perturbative models which give different predictions. However, Fn 2/Fp 2 measured using deuteron and hydrogen targets has large nuclear uncertainties at large x, because the nuclear effects in the deuteron become significant at large x. The MARATHON experiment, which ran in spring 2018 using the upgraded 11 GeV Jefferson Lab electron beam, employs a novel method. It performed deep inelastic scattering off tritium and helium-3 mirror nuclei to measure Fn 2/Fp 2 over the range x = 0.17 to x = 0.82. Since tritium and helium-3 are mirror nuclei, theoretical uncertainties largely cancel out in the ratio. The extracted Fn 2/Fp 2 has much smaller uncertainties compared with previous experiments at large x. The MARATHON experiment also provided results on the EMC effect for tritium and helium-3 nuclei. The results are considered essential for understanding the EMC effect. This thesis describes the MARATHON experiment, and presents results for Fn 2/Fp 2, and the EMC effect for tritium and helium-3.

Liu, Hanjie↗
DOE OSTI2022

A High-End Estimate of Sea Level Rise for Practitioners

Sea level rise (SLR) is a long-lasting consequence of climate change because global anthropogenic warming takes centuries to millennia to equilibrate for the deep ocean and ice sheets. SLR projections based on climate models support policy analysis, risk assessment and adaptation planning today, despite their large uncertainties. The central range of the SLR distribution is estimated by process-based models. However, risk-averse practitioners often require information about plausible future conditions that lie in the tails of the SLR distribution, which are poorly defined by existing models. Here, a community effort combining scientists and practitioners builds on a framework of discussing physical evidence to quantify high-end global SLR for practitioners. The approach is complementary to the IPCC AR6 report and provides further physically plausible high-end scenarios. High-end estimates for the different SLR components are developed for two climate scenarios at two timescales. For global warming of +2°C in 2100 (RCP2.6/SSP1-2.6) relative to pre-industrial values our high-end global SLR estimates are up to 0.9 m in 2100 and 2.5 m in 2300. Similarly, for a (RCP8.5/SSP5-8.5), we estimate up to 1.6 m in 2100 and up to 10.4 m in 2300. The large and growing differences between the scenarios beyond 2100 emphasize the long-term benefits of mitigation. However, even a modest 2°C warming may cause multi-meter SLR on centennial time scales with profound consequences for coastal areas. Earlier high-end assessments focused on instability mechanisms in Antarctica, while here we emphasize the importance of the timing of ice shelf collapse around Antarctica. This is highly uncertain due to low understanding of the driving processes. Hence both process understanding and emission scenario control high-end SLR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Calibrating Bayesian generative machine learning for Bayesiamplification

Recently, combinations of generative and Bayesian deep learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics. The interpretation of a distribution-wide uncertainty however remains ill-defined. We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models. For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution.

97 MATHEMATICS AND COMPUTING↗