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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

In this paper, we present a physics informed deep neural network (DNN) method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) models. We use PDEs in addition to measurements to train DNNs to approximate unknown parameters and constitutive relationships as well as states. The proposed approach increases the accuracy of DNN approximations of partially known functions when a limited number of measurements is available and allows for training DNNs when no direct measurements of the functions of interest are available. We employ physics informed DNNs to estimate the unknown space-dependent diffusion coefficient in a linear diffusion equation and an unknown constitutive relationship in a non-linear diffusion equation. For the parameter estimation problem, we assume that partial measurements of the coefficient and states are available and demonstrate that under these conditions, the proposed method is more accurate than state-of-the-art methods. For the non-linear diffusion PDE model with a fully unknown constitutive relationship (i.e., no measurements of constitutive relationship are available), the physics informed DNN method can accurately estimate the non-linear constitutive relationship based on state measurements only. Finally, we demonstrate that the proposed method remains accurate in the presence of measurement noise.

42 ENGINEERING↗
DOE OSTI2020

The Evolving Distribution of Relative Humidity Conditional Upon Daily Maximum Temperature in a Warming Climate

Abstract The impacts of heat waves in a warming climate depend not only on changing temperatures but also on changing humidity. Using 35 simulations from the Community Earth System Model Large Ensemble (CESM LENS), we investigate the long‐term evolution of the joint distribution of summer relative humidity (RH) and daily maximum temperature ( Tmax ) near four U.S. cities (New York City, Chicago, Phoenix, and New Orleans) under the high‐emissions Representative Concentration Pathway (RCP) 8.5. We estimate the conditional quantiles of RH given Tmax with quantile regression models, using functions of temperature for each city in July for three time periods (1990–2005, 2026–2035, and 2071–2080). Quality‐of‐fit diagnostics indicate that these models accurately estimate conditional quantiles for each city. As expected, each quantile of Tmax increases from 1990–2005 to 2071–2080, while mean RH decreases modestly. Conditional upon a fixed quantile of Tmax , the median and high quantiles of RH decrease, while those of the Heat Index (HI) and dew point both increase. This result suggests that, despite a modest decrease in median relative humidity, heat stress measured by metrics considering both humidity and temperature in a warming climate will increase faster than that measured by temperatures alone would indicate. For a fixed Tmax , the high quantiles of RH (and thus of HI and dew point) increase from 1990–2005 to 2071–2080 in all four cities. This result suggests that the heat stress of a day at a given Tmax will increase in a warming climate due to the increase of RH.

Yuan, Jiacan↗
DOE OSTI2020

Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process

Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.

Probability debsity function, Fiber length distrib↗
DOE OSTI2016

Maps of the Magellanic clouds from combined South Pole Telescope and Planck data

Here, we present maps of the Large and Small Magellanic Clouds from combined South Pole Telescope (SPT) and Planck data. Both instruments are designed to make measurements of the cosmic microwave background but are sensitive to any source of millimeter-wave (mm-wave) emission. The Planck satellite observes in nine mm-wave bands, while the SPT data used in this work were taken with the three-band SPT-SZ camera. The SPT-SZ bands correspond closely to three of the nine Planck bands, namely those centered at 1.4, 2.1, and 3.0 mm. The angular resolution of the Planck data in these bands ranges from 5 to 10 arcmin, while the SPT resolution in these bands ranges from 1.0 to 1.7 arcmin. The combined maps take advantage of the high resolution of the SPT data and the long-timescale stability of the space-based Planck observations to deliver high signal-to-noise and robust brightness measurements on scales from the size of the maps down to ~1 arcmin. In each of the three bands, we first calibrate and color-correct the SPT data to match the Planck data, then we use noise estimates from each instrument and knowledge of each instrument's beam, or point-spread function, to make the inverse-variance-weighted combination of the two instruments' data as a function of angular scale. Furthermore, we create maps assuming a range of underlying emission spectra (for the color correction) and at a range of final resolutions. We perform several consistency tests on the combined maps and estimate the expected noise in measurements of features in the maps. Finally, we compare the maps of the Large Magellanic Cloud (LMC) from this work to maps from the Herschel HERITAGE survey, finding general consistency between the datasets. Furthermore, the broad wavelength coverage provides evidence of different emission mechanisms at work in different environments in the LMC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Adversarial autoencoder ensemble for fast and probabilistic reconstructions of few-shot photon correlation functions for solid-state quantum emitters

Second-order photon correlation measurements [g (2) (τ) functions] are widely used to classify single-photon emission purity in quantum emitters or to measure the multiexciton quantum yield of emitters that can simultaneously host multiple excitations – such as quantum dots – by evaluating the value of g (2) (τ = 0). Accumulating enough photons to accurately calculate this value is time consuming and could be accelerated by fitting of few-shot photon correlations. Here, we develop an uncertainty-aware, deep adversarial autoencoder ensemble (AAE) that reconstructs noise-free g (2) (τ) functions from noise-dominated, few-shot inputs. The model is trained with simulated g (2) (τ) functions that are facilely generated by Poisson sampling time bins. The AAE reconstructions are performed orders-of-magnitude faster, with reconstruction errors and estimates of g (2) (τ = 0) that are lower in variance and similar in accuracy compared to Maximum likelihood estimation and Levenberg-Marquardt least-squares fitting approaches, for simulated and experimentally measured few-shot g (2) (τ) functions (~100 two-photon events) of InP/ZnS/ZnSe and CdS/CdSe/CdS quantum dots. The deep-ensemble model comprises eight individual autoencoders, allowing for probabilistic reconstructions of noise-free g (2) (τ) functions, and we show that the predicted variance scales inversely with number of shots, with comparable uncertainties to computationally intensive Markov chain Monte Carlo sampling. Furthermore, this work demonstrates the advantage of machine learning models to perform uncertainty-aware, fast, and accurate reconstructions of simple Poisson-distributed photon correlation functions, allowing for on-the-fly reconstructions and accelerated materials characterization of solid-state quantum emitters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Drift-flux Analysis in Horizontal Two-phase Flow

While the most detailed approach to model two-phase flows is the two-fluid model, the drift-flux model may also provide effective methods for estimating one-dimensional two-phase flow parameters. For example, in the current nuclear system analysis codes, the interfacial drag force is a function of the void fraction, drift velocity, and area-averaged relative velocity, which are estimated based on drift-flux approach. While correlations for these parameters exist for vertical two-phase flows, correlations for horizontal two-phase flows have not been developed. (authors)

42 ENGINEERING↗
DOE OSTI2019

Measurement of the Crab Nebula Spectrum Past 100 TeV with HAWC

In this study, we present TeV gamma-ray observations of the Crab Nebula, the standard reference source in ground-based gamma-ray astronomy, using data from the High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory. In this analysis we use two independent energy estimation methods that utilize extensive air shower variables such as the core position, shower angle, and shower lateral energy distribution. In contrast, the previously published HAWC energy spectrum roughly estimated the shower energy with only the number of photomultipliers triggered. This new methodology yields a much-improved energy resolution over the previous analysis and extends HAWC's ability to accurately measure gamma-ray energies well beyond 100 TeV. The energy spectrum of the Crab Nebula is well fit to a log-parabola shape $\left(\tfrac{{dN}}{{dE}}={\phi }_{0}{\left(E/7\mathrm{TeV}\right)}^{-\alpha -\beta \mathrm{ln}\left(E/7\mathrm{TeV}\right)}\right)$ with emission up to at least 100 TeV. For the first estimator, a ground parameter that utilizes fits to the lateral distribution function to measure the charge density 40 m from the shower axis, the best-fit values are ${\phi }_{o}=(2.35\pm {0.04}_{-0.21}^{+0.20})\times {10}^{-13}$ (TeV cm 2 s) -1 , $\alpha =2.79\pm {0.02}_{-0.03}^{+0.01}$, and $\beta =0.10\pm {0.01}_{-0.03}^{+0.01}$. For the second estimator, a neural network that uses the charge distribution in annuli around the core and other variables, these values are ${\phi }_{o}=(2.31\pm {0.02}_{-0.17}^{+0.32})\times {10}^{-13}$ (TeV cm 2 s) -1 , $\alpha =2.73\pm {0.02}_{-0.02}^{+0.03}$, and β = 0.06 ± 0.01 ± 0.02. The first set of uncertainties is statistical; the second set is systematic. Both methods yield compatible results. Finally, these measurements are the highest-energy observation of a gamma-ray source to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Multipoint Observations of Energetic Particle Injections and Substorm Activity During a Conjunction Between Magnetospheric Multiscale (MMS) and Van Allen Probes

Here, this study examines multipoint observations during a conjunction between Magnetospheric Multiscale (MMS) and Van Allen Probes on 7 April 2016 in which a series of energetic particle injections occurred. With complementary data from Time History of Events and Macroscale Interactions during Substorms, Geotail, and Los Alamos National Laboratory spacecraft in geosynchronous orbit (16 spacecraft in total), we develop new insights on the nature of energetic particle injections associated with substorm activity. Despite this case involving only weak substorm activity (maximum AE <300 nT) during quiet geomagnetic conditions in steady, below-average solar wind, a complex series of at least six different electron injections was observed throughout the system. Intriguingly, only one corresponding ion injection was clearly observed. All ion and electron injections were observed at <600 keV only. MMS reveals detailed substructure within the largest electron injection. A relationship between injected electrons with energy <60 keV and enhanced whistler mode chorus wave activity is also established from Van Allen Probes and MMS. Drift mapping using a simplified magnetic field model provides estimates of the dispersionless injection boundary locations as a function of universal time, magnetic local time, and L shell. The analysis reveals that at least five electron injections, which were localized in magnetic local time, preceded a larger injection of both electrons and ions across nearly the entire nightside of the magnetosphere near geosynchronous orbit. The larger ion and electron injection did not penetrate to L < 6.6, but several of the smaller electron injections penetrated to L < 6.6. Due to the discrepancy between the number, penetration depth, and complexity of electron versus ion injections, this event presents challenges to the current conceptual models of energetic particle injections.

58 GEOSCIENCES↗
DOE OSTI2017

Historical and Projected Trends in Landscape Drivers Affecting Carbon Dynamics in Alaska

Modern climate change in Alaska has resulted in widespread thawing of permafrost, increased fire activity, and extensive changes in vegetation characteristics that have significant consequences for socio-ecological systems. Despite observations of the heightened sensitivity of these systems to change, there has not been a comprehensive assessment of factors that drive ecosystem changes throughout Alaska. In this paper, we present research that improves our understanding of the main drivers of the spatiotemporal patterns of carbon dynamics using in situ observations, remote sensing data, and an array of modeling techniques. In the last 60 years, Alaska has seen a large increase in mean annual air temperature (1.7 °C), with the greatest warming occurring over winter and spring. Warming trends are projected to continue throughout the 21st century and will likely result in landscape-level changes to ecosystem structure and function. Wetlands, mainly bogs and fens, which are currently estimated to cover 12.5% of the landscape, strongly influence exchange of methane between Alaska's ecosystems and the atmosphere and are expected to be affected by thawing permafrost and shifts in hydrology. Simulations suggest the current proportion of near-surface (within 1 m) and deep (within 5 m) permafrost extent will be reduced by 9–74% and 33–55% by the end of the 21st century, respectively. Since 2000, an average of 678,595 ha/yr was burned, more than twice the annual average during 1950–1999. The largest increase in fire activity is projected for the boreal forest, which could result in a reduction in late-successional spruce forest (8–44%) and an increase in early-succession deciduous forest (25–113%) that would mediate future fire activity and weaken permafrost stability in the region. Climate warming will also affect vegetation communities across arctic regions, where the coverage of deciduous forest could increase (223–620%), shrub tundra may increase (4–21%), and graminoid tundra might decrease (10–24%). Finally, this study sheds light on the sensitivity of Alaska's ecosystems to change that has the potential to significantly affect local and regional carbon balance, but more research is needed to improve estimates of land-surface and subsurface properties, and to better account for ecosystem dynamics affected by a myriad of biophysical factors and interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Gauge-invariant TMD factorization for Drell-Yan hadronic tensor at small $\mathcal{x}$

The Drell-Yan hadronic tensor for electromagnetic (EM) current is calculated in the Sudakov region $s\gg Q^2 \gg q^2_⊥$ with $\frac{1}{Q^2}$ accuracy, first at the tree level and then with the double-log accuracy. It is demonstrated that in the leading order in $N_c$ the higher-twist quark-quark-gluon TMDs reduce to leading-twist TMDs due to QCD equation of motion. The resulting tensor for unpolarized hadrons is EM gauge-invariant and depends on two leading-twist TMDs: $f_1$ responsible for total DY cross section, and Boer-Mulders function $h\frac{⊥}{1}$. The order-of-magnitude estimates of angular distributions for DY process seem to agree with LHC results at corresponding kinematics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Extension of the Concept of Invariance and Statistically Weakly Invariant Sets of Controllable Systems

We continue the study of statistically invariant and statistically weakly invariant sets with respect to controllable systems and differential inclusions launched by Prof. E. L. Tonkov. We examine properties of such statistical characteristics as the lower freq{sub *}(𝜑) and upper freq{sup *}(𝜑) relative frequencies of hitting a solution 𝜑(t) of a differential inclusion in a prescribed set. We obtain estimates and conditions of coincidence of these characteristics for functions whose difference tends to zero at infinity. We also present conditions of statistically weak invariance of a given set of a relatively controllable system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

To the Calculations of Scattering Amplitudes in Diffraction Problems for Elongated Bodies of Revolution

This paper is a complement to the article “Scattering amplitudes in a neighborhood of limit rays in short-wave diffraction by elongated bodies of revolution”. It contains discussions of some points of the article, which worth of more detailed considerations, such as the influence of the integration limits on the computation result of scattering amplitudes and the estimation of permissible values of scattering angle intervals as functions of parameters of the problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Synergy of tensile strength and high cycle fatigue properties in a novel additively manufactured Al-Ni-Ti-Zr alloy with a heterogeneous microstructure

Alloy design strategies in additive manufacturing (AM) to achieve grain refinement and terminal eutectic solidification have been introduced to engineer Al alloys having microstructural hierarchy and heterogeneity. Such alloy design strategies enable crack-free builds with an expanded AM processing window and pushed the strength limit in Al alloys. However, fatigue performance of Al alloys made by AM is restricted by the presence of process induced defects and its stochasticity. In this work, tensile and high cycle fatigue (HCF) behavior of a novel Al-Ni-Ti-Zr alloy with a heterogeneous microstructure is studied in the as-built condition, supplemented by detailed microstructural and mechanical characterization. Excellent strength-ductility synergy of 342 MPa and 16% failure strain achieved in the alloy was associated with the microstructural attributes that pertain to the novel alloy. Additionally, the alloy showed excellent HCF performance with a fatigue endurance limit to ultimate tensile strength ratio of 0.29 in flexural fatigue mode. The study revealed the existence of multiple crack retardation mechanisms and favorable crack propagation pathways through the fine-grained regions which enabled good fatigue performance to the alloy. Further, a probabilistic model has been used to estimate the fatigue life of the alloy as a function of the stochastic microstructure by utilizing the statistical distribution of pores, solid-state inclusions, and grains in the AM Al alloy. Finally, the model parametric trends are consistent with the experimental observations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effective thermal conductivity of insulation materials for cryogenic LH 2 storage tanks: A review

An accurate estimation of the effective thermal conductivity of various insulation materials is essential in the evaluation of heat leak and boil-off rate from liquid hydrogen storage tanks. In this work, we review the existing experimental data and various proposed correlations for predicting the effective conductivity of insulation systems consisting of powders, foams, fibrous materials, and multilayer systems. We also propose a first principles-based correlation that may be used to estimate the dependence of the effective conductivity as a function of temperature, interstitial gas composition, pressure, and structural properties of the material. We validate the proposed correlation using available experimental data for some common insulation materials. Further improvements and testing of the proposed correlation using laboratory scale data obtained using potential LH 2 tank insulation materials are also discussed.

08 HYDROGEN↗
DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗
DOE OSTI2017

A phenomenological force model of Li-ion battery packs for enhanced performance and health management

A 1-D phenomenological force model of a Li-ion battery pack is proposed to enhance the control performance of Li-ion battery cells in pack conditions for efficient performance and health management. The force model accounts for multiple swelling sources under the operational environment of electric vehicles to predict swelling-induced forces in pack conditions, i.e. mechanically constrained. The proposed force model not only incorporates structural nonlinearities due to Li-ion intercalation swelling, but also separates the overall range of states of charge into three ranges to account for phase transitions. Additionally an approach to study cell-to-cell variations in pack conditions is proposed with serial and parallel combinations of linear and nonlinear stiffness, which account for battery cells and other components in the battery pack. The model is shown not only to accurately estimate the reaction force caused by swelling as a function of the state of charge, battery temperature and environmental temperature, but also to account for cell-to-cell variations due to temperature variations, SOC differences, and local degradation in a wide range of operational conditions of electric vehicles. Considering that the force model of Li-ion battery packs can account for many possible situations in actual operation, the proposed approach and model offer potential utility for the enhancement of current battery management systems and power management strategies.

25 ENERGY STORAGE↗
DOE OSTI2026

Pressure-dependent kinetics of cyclopentene consumption

Recent experiments have uncovered deficiencies in the ability of current kinetic models for cyclopentane combustion to predict species profiles as a function of O2 concentration. One likely source of these discrepancies flows from the limited availability of relevant kinetic data for key functionalized intermediates, requiring the use of distant analogies to estimate their rates. This work fills this gap for cyclopentene, the most prominent C5 intermediate in low-temperature cyclopentane combustion. Ab initio transition-state-theory based master equation methods are used to calculate pressure-dependent rate constants across four potential energy surfaces (C5H8 + OH, C5H8 + HO2, C5H7, and C5H7 + O2). Potential energy surfaces are characterized at the CCSD(T)-F12/cc-pVDZ-F12//revDSD-PBEP86-D3BJ/def2-TZVPP level of theory. Pressure-dependent rate constants and branching fractions are determined via 1D master equation calculations. The results are used to analyze the competition for radicals between cyclopentene and cyclopentane in the intermediate temperature region, and to identify the dominant pathways flowing from these initiation channels. On the OH initiation surface, we find good agreement with recently published experimental rate constants at high temperature, and we elucidate the complex temperature dependence below 800 K, where 𝜋𝜋 addition becomes prominent. On the HO2 initiation surface, we find an interesting alternative route to bicyclic ether formation via well-skipping over the HO2 adduct well. On the cyclopentenyl radical surface, the major product is cyclopentadiene plus H. On the cyclopentenyl + O2 surface, we find that resonance stabilization largely prevents the allylic radical from undergoing oxidation, whereas the alkylic radical has a somewhat elevated propensity for oxidation pathways relative to cyclopentyl.

ab initio calculations↗
DOE OSTI2019

Diffusional Dynamics of Tetraalkylphosphonium Ionic Liquid Films Measured by Fluorescence Correlation Spectroscopy

Fluorescence correlation spectroscopy (FCS) is enacted to investigate the diffusional dynamics of hydrophilic (Atto 590) and amphiphilic (DiD) fluorophores in a series of alkylphosphonium ionic liquid (IL) films ([P4448][Cl], [P6668][Cl], [P66614][Cl], and [P66614][NTf2]) in order to determine diffusional parameters and to elucidate nanoscale structural heterogeneities within the IL. From the measured correlation functions, the diffusion coefficients of the fluorescent molecules are estimated, rendering values that span from 0.39-1.2 and 0.146-5.2 µm2/s, for Atto 590 and DiD, respectively. A decrease in the diffusion coefficients is measured as the alkyl chain increases, coincident with a decrease in their viscosity. Interestingly, deviations from the Brownian diffusion behavior of the fluorescent probes in the ILs are observed, showing a time-dependent diffusion coefficient in most of the cases. These deviations can be attributed to the presence of nanoscale structural heterogeneities in the tetraalkylphosphonium ILs. These conclusions validate experimentally the presence of nanosegregation in tetraalkylphosphonium ILs, which has previously been observed in molecular dynamics (MD) studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗