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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Battery Life Prediction Using Reduced-Order Physics Models and Machine Learning (CRADA Final Report)

Phase 1 (Original CRADA, plus no-cost extension modifications #1-3, 6/1/2017 to 3/13/2021): The Australian Department of Defence (AUDoD) is performing accelerated aging tests of Li-ion batteries to benchmark their reliability and degradation characteristics. Using its previously developed battery lifetime predictive model framework, the National Laboratory of the Rockies (NLR) will develop analytical models based the AUDoD data to predict lifetime of the multiple Li-ion battery chemistries under real-world use scenarios of interest to AUDoD. The NLR model is based on physical degradation mechanisms encountered by Li-ion batteries and has been previously validated. Phase 2 (CRADA modification #4, plus no-cost extension modification #5, 2/22/2021 to 3/30/2025): Train and support Australian Department of Defence personnel to use NLR software for model-based estimation of Li-ion battery lifetime using accelerated battery aging data collected by the Australian Department of Defence. Under separate DOE funding from 2019 to 2021, NLR enhanced its battery life-prediction software using machine learning algorithms to automate portions of the model-fitting process, requiring significantly less labor and expert judgment and also adding uncertainty quantification, increasing statistical rigor. Under Phase 2, NLR will customize NLR Software and provide it to AuDoD. NLR will enhance its NLR Model to capture aging modes of AuDoD's multi-cell modules, including cell-balancing effects. NLR will develop example single-cell and multi-cell models based on one AuDoD battery aging dataset. NLR will train AuDoD personnel on NLR Software. By the conclusion of the project, NLR will have provided AuDoD the training materials, a user manual and software needed to perform their own analysis of additional and/or future battery aging datasets.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Modeling the Spectral Energy Distribution of the Active Galactic Nucleus inside NGC 4395

We study the broadband spectral energy distribution (SED) of the prototypical low-mass active galactic nucleus (AGN) in NGC 4395. We jointly model the optical through mid-IR SED with a combination of galaxy and AGN light, and find that on arcsecond scales the AGN dominates at most wavelengths. However, there is still some ambiguity about emission from the galaxy, owing partially to the strong short-term variability of the black hole. We investigate the use of smooth- and clumpy-torus models in order to disentangle the nuclear IR emission, as well as exploring the use of poloidal wind emission to account for the blue spectral slope observed in the near-IR. Even when simultaneously fitting the full optical–IR spectral range, we find that degeneracies still remain in the best-fit models. We conclude that high-spatial-resolution and wider-wavelength coverage with the JWST is needed to understand the mid-IR emission in this complex, highly variable object, which is the best nearby example to provide a blueprint to finding other low-mass AGNs via their mid-IR emission in the future.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1979

The 12- to 20-micron spectrum of Venus - Implications for temperature and cloud structure

The spectrum of Venus was measured between approximately 500 and 800 kaysers (12 to 20 microns) at a resolution of 3.12 kaysers from the NASA C141 G. P. Kuiper Airborne Observatory on 22 and 24 February 1977. The spectrum clearly shows the detailed structure of CO2 absorption in the vicinity of the nu-2 fundamental band at 667 kaysers. In addition, details of model fitting demonstrate the possibility for a cold and thin haze of sulfuric acid droplets along with an optically opaque cloud top near 250 K. Such clouds represent major differences from other H2SO4 main cloud deck models in the recent literature and may be indicative of changes in the vertical distribution of aerosols on a global scale. The temperatures retrieved for pressures at or below 10 mbar are largely independent of the cloud model assumed and they are some 16 to 20 K warmer than the 1972 NASA model. All retrieved temperatures lie within the range of Mariner 5 and Mariner 10 radio occultation inversion results.

Aumann, H. H.↗
NASA NTRS1991

The Galactic distribution of free electrons

Various radioastronomical measurements are synthesized here to derive a large-scale Galactic model for the distribution of free electrons and for microstructure in the distribution that seems to be caused by turbulence in the ISM. A two-component axisymmetric model fitted on all scales from 100 km to a few pc, accounts for most of the data. A population of dense, discrete clouds is also needed, and there is some evidence for spiral structure. The model allows better distance estimates for pulsars to be made. The implications of the model for the structure and ionization of the ISM and for the distribution of interstellar turbulence and the diffusion of cosmic rays are discussed.

Cordes, J. M.↗
DOE OSTI2026

Neural Posterior Estimation for Scalable and Accurate Inverse Parameter Inference in Li-Ion Batteries

Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE can infer parameters equally or more accurately than Bayesian calibration, even if it leads to higher voltage reconstruction errors. We also demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters). The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).

25 ENERGY STORAGE↗
DOE OSTI2024

Kinetic assessment of pulp mill-derived lime mud calcination in high CO 2 atmosphere

The chemical pulping of biomass involves the recycling of calcium through the calcination of lime mud, which is mostly comprised of calcium carbonate (CaCO 3 ). Lime mud decomposes under elevated temperatures to generate calcium oxide (CaO) and carbon dioxide (CO 2 ), the kinetics of which are strongly influenced by the CO 2 partial pressure and temperature. Oxy-fuel combustion and electrified lime kilns for lime mud calcination are intriguing methods to decarbonize this highly polluting operation within the biomass pulping industry. However, the high CO 2 concentration in oxy-fuel and electrified calcination processes alters the kinetics and overall reactivity of lime mud. For the first time, a model-fitting method is used to determine the kinetic parameters for lime mud calcination under a wide range of temperatures (550 °C–1250 °C) and under different concentrations of CO2 (0 %, 15 %, 50 %, and 90 %). A kinetic model is developed that accurately predicts the reaction rates as a function of temperature and CO 2 concentration. The apparent activation of energy for lime mud calcination is elevated under a high CO 2 environment. Relative to inert gas (N 2 , Ar), the temperature window for calcination is much smaller under high CO 2 environments. The presence of Na in lime mud does not seem to affect calcination under a high CO 2 environment. Finally, particle size variation does not have a significant effect on calcination under a high CO 2 environment.

09 BIOMASS FUELS↗
NASA NTRS1995

The second-moment climatology of the GATE rain rate data

The first part of this paper presents the description of the GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE) 1 rain-rate data and its two-dimensional spectral and correlation characteristics, which has made it possible to accomplish the following: to show the concentration of a significant power along the frequency axis in the spatiotemporal spectra; to detect a diurnal cycle (which has a range of variation of about 3.4-5.4 mm/n) as one of the sources of bias in the rain statistics of satellite data; to study the distinction between the north-south and east-west transport of spatial rain-rate field and character of its anisotropy; to evaluate the scales of the distinction between second-moment estimates associated with ground and satellite samples; and to determine the appropriate spatial and temporal scales of simple linear stochastic models fitted to averaged rain-rate fields. The second part of this paper is devoted to an analysis of the diffusion of the rain rate by establishing a relationship between the parameters of the multivariate autoregressive model and the coefficients of a diffusion equation. This analysis led to the use of rain data to estimate the rain advection velocity as well as other coefficients of the diffusion equation of the corresponding field. The results obtained can be used for comparison with corresponding estimates of other sources of data (satellite, Tropical Oceans Global Atmosphere Coupled Ocean - Atmosphere Response Experiment (TOGA, COARE) or simulated by physical models), for generating multiple samples of any size, for solving the inverse problems of some of the hydrodynamic equations, and in some other areas of rain data analysis and modeling.

Polyak, Ilya↗
NASA NTRS1995

PAHs as Probes of Photodissociation Regions in M17 and the Orion Bar

We have obtained narrow-band images of M17 SW and the Orion Bar in the PAH emission bands and pedestal (3.29, 3. 36, 3. 42 microns) The emission bands at 3.3 and 3.4 microns arise from the photodissociation regions (PDRs) between ionized gas and molecular clouds. In both M17 SW and the Orion Bar, the PDRs are nearly edge-on, providing excellent opportunities for comparing models of PDRs with observations. We observe an exponential drop in the strength of the 3.3 micron emission with a 1/e distance of 9 arcsec in Orion and 5 arcsec in M17 SW, in good agreement with previous observations. These results show that the two regions are very similar, and they imply that the mean density is 2.4 times higher in the Orion Bar than in the M17 SW PDR. However, we also find that in the Orion Bar, the ratio of the 3.4 micron emission to the 3.3 micron emission is consistent with the 1/e distance of 3 arcsec determined from PDR models fit to the molecular H and CO in the Orion Bar. We are presently investigating how the main band can imply that the UV field is dropping with a 1/e distance of 9 arcsec while the model PDR predicts a 1/e distance of 3 arcsec.

Sloan, G. C.↗
NASA NTRS2006

Gamma-ray Burst Afterglows as Probes of Environment and Blastwave Physics: Absorption by Host Galaxy Gas and Dust, Circumburst Media and the Distribution of P

We use a new approach to obtain limits on the absorbing columns towards a sample of 10 Gamma-ray Bursts observed by BeppoSAX from simultaneous fits to X-ray, optical and IR data, in counts space and including the effects of metallicity. For half the afterglows the best-fitting model to the SED includes SMC-like extinction (as opposed to LMC or MW) and in one LMC-like extinction, and in no cases is there a preference for MW-like extinction. Gas-to-dust ratios generally do not match those of the 3 standard and most well-known extinction models of SMC, LMC and MW, but tend to be higher. We compare the results from this method to those of previous works using other methods. We constrain the jet models for a subsample of the bursts by constraining the cooling break position and power law spectral slopes, allowing the injected electron energy index to be measured. We derive secure values of p from our spectral fits and comparison with the temporal optical and X-ray slopes for 4 afterglows. The mean of these single value, suggesting that either external factors such as circumburst medium play a strong role or that the microphysics is not identical for each GRB. For GRB 971214 we find that the circumburst medium has a wind-like density profile and the cooling frequency appears to be moving to higher frequencies.

Starling, R. L. C.↗
DOE OSTI2021

Comparison of simulated neutrino emission models with data on Supernova 1987A

Here we compare models of supernova (SN) neutrino emission with the Kamiokande II data on SN 1987A using the Bayesian approach. These models are taken from simulations and are representative of current one-dimensional SN models. We find that models with a brief accretion phase of neutrino emission are the most favored. This result is not affected by varying the overall flux normalization or considering neutrino oscillations. We also check the compatibility of the best-fit models with the data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Single-crystal elasticity of (Al,Fe)-bearing bridgmanite up to 82 GPa

Abstract Thermoelastic properties of mantle candidate minerals are essential to our understanding of geophysical phenomena, geochemistry, and geodynamic evolutions of the silicate Earth. However, the lower-mantle mineralogy remains much debated due to the lack of single-crystal elastic moduli (Cij) and aggregate sound velocities of (Al,Fe)-bearing bridgmanite, the most abundant mineral of the planet, at the lower mantle pressure-temperature (P-T) conditions. Here we report single-crystal Cij of (Al,Fe)-bearing bridgmanite, Mg0.88Fe0.1Al0.14Si0.90O3 (Fe10-Al14-Bgm) with Fe3+/ΣFe = ~0.65, up to ~82 GPa using X-ray diffraction (XRD), Brillouin light scattering (BLS), and impulsive stimulated light scattering (ISLS) measurements in diamond-anvil cells (DACs). Two crystal platelets with orientations of (–0.50, 0.05, –0.86) and (0.65, –0.59, 0.48), that are sensitive to deriving all nine Cij, are used for compressional and shear wave velocity (νP and νS) measurements as a function of azimuthal angles over 200° at each experimental pressure. Our results show that all Cij of singe-crystal Fe10-Al14-Bgm increase monotonically with pressure with small uncertainties of 1–2% (±1σ), except C55 and C23, which have uncertainties of 3–4%. Using the third-order Eulerian finite-strain equations to model the elasticity data yields the aggregate adiabatic bulk and shear moduli and respective pressure derivatives at the reference pressure of 25 GPa: KS = 326 ± 4 GPa, µ = 211 ± 2 GPa, KS′ = 3.32 ± 0.04, and µ′ = 1.66 ± 0.02 GPa. The high-pressure aggregate νS and νP of Fe10-Al14-Bgm are 2.6–3.5% and 3.1–4.7% lower than those of MgSiO3 bridgmanite end-member, respectively. These data are used with literature reports on bridgmanite with different Fe and Al contents to quantitatively evaluate pressure and compositional effects on their elastic properties. Comparing with one-dimensional seismic profiles, our modeled velocity profiles of major lower-mantle mineral assemblages at relevant P-T suggest that the lower mantle could likely consist of about 89 vol% (Al,Fe)-bearing bridgmanite. After considering uncertainties, our best-fit model is still indistinguishable from pyrolitic or chondritic models.

Geochemistry & Geophysics↗
DOE OSTI2022

Preliminary Results for Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions.

As machine learning (ML) models are deployed into an ever-diversifying set of application spaces, ranging from self-driving cars to cybersecurity to climate modeling, the need to carefully evaluate model credibility becomes increasingly important. Uncertainty quantification (UQ) provides important information about the ability of a learned model to make sound predictions, often with respect to individual test cases. However, most UQ methods for ML are themselves data-driven and therefore susceptible to the same knowledge gaps as the models themselves. Specifically, UQ helps to identify points near decision boundaries where the models fit the data poorly, yet predictions can score as certain for points that are under-represented by the training data and thus out-of-distribution (OOD). One method for evaluating the quality of both ML models and their associated uncertainty estimates is out-of-distribution detection (OODD). We combine OODD with UQ to provide insights into the reliability of the individual predictions made by an ML model.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Thermal stresses, differential subsidence, and flexure at oceanic fracture zones

Geosat geoid undulations over four Pacific fracture zones have been analyzed. After correcting for the isostatic thermal edge effect, the amplitudes of the residuals are shown to be proportional to the age offset. The shape of the residuals seems to broaden with increasing age. Both geoid anomalies and available ship bathymetry data suggest that slip must sometimes occur on the main fracture zone or secondary faults. Existing models for flexure at fracture zones cannot explain the observed anomalies. A combination model accounting for slip and including flexure from thermal stresses and differential subsidence is presented. This model accounts for lateral variations in flexural rigidity from brittle and ductile yielding due to both thermal and flexural stresses and explains both the amplitudes and the shape of the anomalies along each fracture zone. The best fitting models have mechanical plate thicknesses that are described by the depth to the 600-700 C isotherms.

Wessel, Pal↗
DOE OSTI2026

Quantifying mean, variability, and uncertainty in indoor radon exposure in Pennsylvania using random forest and quantile regression forest models

Radon is a naturally occurring radioactive gas that poses a serious health risk as the primary cause of lung cancer in non-smokers. Despite the well-known adverse association with health outcomes, current radon exposure assessments are limited to county-level or average-level estimates, which fail to capture regional variability. This study uses Machine Learning models, including Random Forest (RF) and Quantile Regression Forest (QRF), to estimate the indoor radon concentrations at the ZCTA (Zip code tabulation area)-level and characterize uncertainties in model estimates. Incorporating geological, meteorological, and building-specific data, the models aim to improve radon risk assessment by capturing mean exposure, variability, and extreme concentration levels. Processed radon test data (n = 718,111) were analyzed using average, variability, and quantile prediction methods. Models that estimate the average radon exposure at the ZCTA-level can yield promising model-fit results, but they do not capture the underlying variability of indoor radon exposure within a ZCTA. We utilize volatility analyses to identify characteristics indicative of high variability of indoor radon exposure. We also show that a QRF model can be used to estimate upper quantiles of residential radon exposure, thereby uncovering localized areas of elevated exposure that were not apparent in mean estimates. The results highlighted the need for a deep characterization of exposure risk and show that regions with moderate average exposure levels could still harbor extreme outliers with implications for evaluating health risks. Utilizing multiple radon exposure models allows for a deeper characterization of radon risk within a geographic area and can better identify high-risk areas. The results from this study provide a foundation for developing mitigation strategies and examining associations between radon exposure and health outcomes at fine scales. Future research should extend the geographic scope and incorporate additional environmental risk factors to establish a comprehensive framework for risk assessment.

Lee, Heechan [ORNL]↗
NASA NTRS1975

Recovery of atmospheric refractivity profiles from simulated satellite-to-satellite tracking data

Techniques for recovering atmospheric refractivity profiles from simulated satellite-to-satellite tracking data are documented. Examples are given using the geometric configuration of the ATS-6/NIMBUS-6 Tracking Experiment. The underlying refractivity model for the lower atmosphere has the spherically symmetric form N = exp P(s) where P(s) is a polynomial in the normalized height s. For the simulation used, the Herglotz-Wiechert technique recovered values which were 0.4% and 40% different from the input values at the surface and at a height of 33 kilometers, respectively. Using the same input data, the model fitting technique recovered refractivity values 0.05% and 1% different from the input values at the surface and at a height of 50 kilometers, respectively. It is also shown that if ionospheric and water vapor effects can be properly modelled or effectively removed from the data, pressure and temperature distributions can be obtained.

Murray, C. W., Jr.↗
NASA NTRS2018

Generating Exploration Mission-3 Trajectories to a 9:2 NRHO using Machine Learning

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA's Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can bean abstract task and relies on an orbit analyst to structure a mission based on an established mission design methodology associated to the performance of Orion and NASA's Space Launch System. Introducing a machine learning algorithm trained to construct mission scenarios within the feasible range of known trajectories reduces the required interaction of the orbit analyst by removing the needed step of optimizing the orbit to fit an expected translational response required of the spacecraft. In this study, k-Nearest Neighbor and Bayesian Linear Regression successfully predicted classical orbital elements for the launch windows observed. However both algorithms had limitations due to their approaches to model fitting. Training machine learning algorithms off of classical orbital elements introduced a repetitive approach to reconstructing mission segments for different arrival opportunities through the launch window and can prove to be a viable method of launch window scan generation for future missions.

Guzman, Esteban↗
DOE OSTI2025

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗
NASA NTRS2010

Assessment of Response Surface Models using Independent Confirmation Point Analysis

This paper highlights various advantages that confirmation-point residuals have over conventional model design-point residuals in assessing the adequacy of a response surface model fitted by regression techniques to a sample of experimental data. Particular advantages are highlighted for the case of design matrices that may be ill-conditioned for a given sample of data. The impact of both aleatory and epistemological uncertainty in response model adequacy assessments is considered.

DeLoach, Richard↗