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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry

Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land subsidence, and flood risk management. The high cost and complex logistics of direct bathymetry surveys, i.e, depth imaging, have encouraged the use of indirect measurements such as surface flow velocities. However, estimating high-resolution bathymetry from indirect measurements is an inverse problem that can be computationally challenging. Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements. In our application, the shallow-water equations (SWE) with appropriate boundary conditions (BCs), e.g., the discharge and/or the free surface elevation, constitute the forward problem, to predict flow velocity. Then, ROMs of the SWEs are constructed on a nonlinear manifold of low dimensionality through a variational encoder and the bathymetry inversion problem is derived on the low-dimensional latent space in a Hierarchical Bayesian setting. Further, the reformulation allows variational inference with a small number (e.g., $\mathscr{O}$ (100) of ROM runs and efficient uncertainty quantification. We have tested our inversion approach on a one-mile reach of the Savannah River, GA, USA. Once the neural network is trained (offline stage), the proposed technique can perform the inversion operation orders of magnitude faster than traditional inversion methods that are commonly based on linear projections, such as principal component analysis (PCA), or the principal component geostatistical approach (PCGA). Furthermore, tests show that the algorithm can estimate the bathymetry with good accuracy even with sparse flow velocity measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning inversion from scattering for mechanically driven polymers

A machine learning inversion method is developed for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration and off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the machine learning inversion. Finally, we train a Gaussian process regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters.

Gaussian process regressors↗
DOE OSTI2023

Antimony nanobelt asymmetric membranes for sodium ion battery

In this study, composite asymmetric membranes containing antimony (Sb) nanobelts are prepared via a straightforward phase inversion method in combination with post-pyrolysis treatment. Sb nanobelt asymmetric membranes demonstrate improved cyclability and specific capacity as the alloy anode of sodium ion battery compared to Sb nanobelt thin films without asymmetric porous structure. The unique structure can effectively accommodate the large volume expansion of Sb-based alloy anodes, prohibit the loss of fractured active materials, and aid in the formation of stable artificial solid electrolyte interphases as evidenced by an outstanding capacity retention of ~98% in 130 cycles at 60 mA g -1 . A specific capacity of ~600 mAh g -1 is obtained at 15 mA g -1 (1/40C). When the current density is increased to 240 mA g -1 , ~80% capacity can be maintained (~480 mAh g -1 ). The relations among phase inversion conditions, structures, compositions, and resultant electrochemical properties are revealed through comprehensive characterization.

25 ENERGY STORAGE↗
DOE OSTI2023

Microseismic Monitoring at the Farnsworth CO 2 -EOR Field

The Farnsworth Unit in northern Texas is a field site for studying geologic carbon storage during enhanced oil recovery (EOR) using CO 2 . Microseismic monitoring is essential for risk assessment by detecting fluid leakage and fractures. We analyzed borehole microseismic data acquired during CO 2 injection and migration, including data denoising, event detection, event location, magnitude estimation, moment tensor inversion, and stress field inversion. We detected and located two shallow clusters, which occurred during increasing injection pressure. The two shallow clusters were also featured by large b values and tensile cracking moment tensors that are obtained based on a newly developed moment tensor inversion method using single-borehole data. The inverted stress fields at the two clusters showed large deviations from the regional stress field. The results provide evidence for microseismic responses to CO 2 /fluid injection and migration.

02 PETROLEUM↗
DOE OSTI2024

Estimating Carbon Dioxide Emissions in Two California Cities Using Bayesian Inversion and Satellite Measurements

NASA's Orbiting Carbon Observatories (OCO-2 and OCO-3) provide measurements of column-averaged carbon dioxide concentrations (XCO 2 ) with sufficient spatial resolution and precision to constrain bottom-up estimates of CO 2 fluxes at regional scales. We use Bayesian inversion methods assimilating satellite retrievals to improve estimates of CO 2 fluxes in the South Coast Air Basin (SoCAB) which surrounds Los Angeles, and in the San Francisco Bay Area Air Basin (SFBA). We study 2020 to understand the impact of the COVID-19 lockdowns and an active wildfire season. Our results indicated that a 50% (30%) reduction in CO 2 emissions relative to 2015 during the COVID-19 lockdown period was consistent with OCO measurements for SFBA (SoCAB). We find that posterior wildfire emissions differed significantly from the prior at the scale of individual wildfires, though with large uncertainties, and that wildfire emissions in SFBA are significant, attributing 72% of the region's CO 2 emissions during August 2020 to wildfires.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Analysis of the density field around a supersonic conical projectile using quantitative schlieren

Quantitative schlieren imaging is a flow measurement technique that is capable of measuring density fields throughout refractive flowfields. The technique was applied here to measure the density field surrounding supersonic conical projectiles in free flight. Shock waves attached to a supersonic conical projectile offer a simple geometry with the well-established Taylor–Maccoll analytical flow solution to which these experimental measurements were compared. The schlieren images recorded a projection of the index of refraction field surrounding the 10° half-angle cones which was converted to density first through an Abel inversion and then the Gladstone–Dale law. Three Abel inversion methods—two-point, three-point, and arbitrary ray axisymmetric projection (ARAP)—were applied to deconvolute the three-dimensional flow within the constrained axisymmetric flow field. The resulting reconstructed density profiles were compared to the Taylor–Maccoll solution, parameterized by cone geometry and Mach number. The experimental density fields demonstrated strong agreement with the theoretical profiles. Experimental consistency was confirmed across various projectile speeds, demonstrating quantitative schlieren’s capability to accurately reconstruct the density of the flow field, even within the resolution constraints imposed by high-speed imaging. In conclusion, an assessment of experimental uncertainties in the density reconstruction was performed.

Abel Inversion↗
DOE OSTI2023

Assessing parallel path cooling tower performance via artificial neural networks

Real-time monitoring of a research nuclear reactor, a system in which all generated power is dissipated to the environment, can be performed via analysis of the heat rejection from the cooling system. Given an inlet water temperature and flow rate, the reactor power can be well-approximated from the outlet water temperature; however, the instrumentation to measure outlet conditions may not be robust or accurate. If we know how a cooling tower performs from historical data, but cannot measure the outlet temperature, a mathematical representation of the system can be inverted to obtain the outlet water temperature that describes the cooling capacity. Unfortunately, model inversion processes are computationally expensive. To address this, an artificial neural network (ANN) is implemented to assess the performance of a multi-cell cooling tower for a nuclear reactor. This approach leverages the Merkel model to obtain an extensive data set describing performance of the cooling tower cells throughout a wide array of potential operating conditions. The Merkel model is expressed as a function of four parameters: the inlet and outlet water temperatures, inlet air wet bulb temperature, and ratio of liquid-to-gas mass flow rates (L/G), which together provide a non-dimensional number indicative of cooling tower performance, called the Merkel integral. Computing a 4-dimensional data structure that describes finite combinations of the Merkel integral, an inverse model is then generated using an ANN to determine the cell outlet water temperature from the other three model parameters along with the computed Merkel integral. Compared to traditional model inversion methods, the ANN reduces the computational time by approximately 4 orders of magnitude, with effectively no sacrifice to solution accuracy, and could be applied for different cooling towers in the event the performance curve is known. Finally, three use cases of the ANN are then reviewed: (1) determining the cell outlet water temperatures when gas flow at rated conditions (GFRC) is known, (2) performing the prior case without knowledge of the GRFC, and (3) assessing performance differences between the individual tower cells.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Turbulence-induced bias in time-averaged laser absorption tomography of correlated concentration and temperature fields with a first-order correction

The influence of correlated scalar fluctuations on time-averaged laser absorption tomography measurements of temperature and species in a piloted turbulent premixed flame was examined using a coupled spectroscopic and fluid-dynamic analysis. To understand bias associated with turbulence, spatio-temporally resolved temperature and species mole fraction profiles predicted by large eddy simulations (LES) were used to synthetically generate time-resolved line-of-sight absorption measurements at short time scales (microsecond) to reflect the unsteady nature of a canonical jet burner across various transverse measurement planes. Inversion methods were employed on the time-averaged line-of-sight data to produce radially-resolved temperature and mole fraction profiles, analogous to those produced by laser absorption tomography performed on a time-averaged axisymmetric flowfield. It is shown that bias in the measurements compared to true time-averaged scalar fields is a function primarily of temperature dependence in absorptivity and non-zero correlation between temperature and species concentration scalars. Finally, a first-order correction to tomography measurements is proposed to account for the bias based on estimated correlations and the known spectroscopic parameters of the probed absorption transitions.

42 ENGINEERING↗
DOE OSTI2022

SuperScreen: An open-source package for simulating the magnetic response of two-dimensional superconducting devices

Quantitative understanding of the spatial distribution of magnetic fields and Meissner screening currents in two-dimensional (2D) superconductors and mesoscopic thin film superconducting devices is critical to interpreting the results of magnetic measurements of such systems. Here, we introduce SuperScreen, an open-source Python package for simulating the response of 2D superconductors to trapped flux and applied time-independent or quasi-DC magnetic fields for any value of the effective magnetic penetration depth, Λ. Given an applied magnetic field, SuperScreen solves the 2D London equation using an efficient matrix inversion method to obtain the Meissner currents and magnetic fields in and around structures composed of one or more superconducting thin films of arbitrary geometry. Further, SuperScreen can be used to model screening effects and calculate self- and mutual-inductance in thin film superconducting devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A numerical study of the spectral radiative properties of packed bed with mixed bauxite and silica spheres

Bauxite and silica particles have gained increasing attention for applications in the field of concentrated solar power. In this work, a Monte Carlo ray-tracing simulation is performed to predict the radiative properties (absorptance, reflectance, and transmittance) of packed beds with mixed bauxite and silica spherical particles at wavelengths of 0.5, 2.6, 9.0, and 9.35 µm. These wavelengths are representative for the visible, near-infrared, and the mid-infrared regions that are important for solar and thermal radiation, respectively. A repeating unit-column approach is used to mathematically represent the particle bed. The effects of particle mixing ratio, volume fraction, and wavelength on the predicted radiative properties are examined. The obtained radiative properties are inputted to an inverse method to retrieve the effective absorption and scattering coefficients as well as the scattering albedo, which may be later used in a continuous-scale radiative heat transfer analysis. Furthermore, the independent scattering model is used to obtain the absorption coefficient and scattering albedo based on the absorption and scattering cross sections predicted by a Monte Carlo algorithm for a single particle. It is shown that the independent scattering model underpredicts the scattering coefficient for opaque particles but overpredicts the scattering coefficient for semitransparent particles for sufficiently high particle volume fractions. Here, the radiative properties calculated from the independent scattering model are compared to the full Monte Carlo simulation of the particle bed to examine the influence of particle mixing on dependent scattering.

42 ENGINEERING↗
DOE OSTI2024

Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets

Here, this study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Implementation of higher-order velocity mapping between marker particles and grid in the particle-in-cell code XGC

The global total-f gyrokinetic particle-in-cell code XGC, used to study transport in magnetic fusion plasmas or to couple with a core gyrokinetic code while functioning as an edge gyrokinetic code, implements a five-dimensional continuum grid to perform the dissipative operations, such as plasma collisions, or to exchange the particle distribution function information with a core code. To transfer the distribution function between marker particles and a rectangular two-dimensional velocity-space grid, XGC employs a bilinear mapping. The conservation of particle density and momentum is accurate enough in this bilinear operation, but the error in the particle energy conservation can become undesirably large and cause non-negligible numerical heating in a steep edge pedestal. In the present work we update XGC to use a novel mapping technique, based on the calculation of a pseudo-inverse, to exactly preserve moments up to the order of the discretization space. Here we describe the details of the implementation and we demonstrate the reduced interpolation error for a tokamak test plasma using first- and second-order elements with the pseudo-inverse method and comparing with the bilinear mapping.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Black carbon aerosol number and mass concentration measurements by picosecond short-range elastic backscatter lidar

Abstract Black carbon aerosol emissions are recognized as contributors to global warming and air pollution. There remains, however, a lack of techniques to remotely measure black carbon aerosol particles with high range and time resolution. This article presents a direct and contact-free remote technique to estimate the black carbon aerosol number and mass concentration at a few meters from the emission source. This is done using the Colibri instrument based on a novel technique, referred to here as Picosecond Short-Range Elastic Backscatter Lidar (PSR-EBL). To address the complexity of retrieving lidar products at short measurement ranges, we apply a forward inversion method featuring radiometric lidar calibration. Our method is based on an extension of a well-established light-scattering model, the Rayleigh–Debye–Gans for Fractal-Aggregates (RDG-FA) theory, which computes an analytical expression of lidar parameters. These parameters are the backscattering cross-sections and the lidar ratio for black carbon fractal aggregates. Using a small-scale Jet A-1 kerosene pool fire, we demonstrate the ability of the technique to quantify the aerosol number and mass concentration with centimetre range-resolution and millisecond time-resolution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Temperature and density dependent pair potential for deuterium under shock

Large-scale classical molecular dynamics (CMD) simulations naturally include the microscopic physics necessary for atomistic modeling of shock release at the ablator-fuel interface in an inertial confinement fusion (ICF) capsule. Here, the multi-megabar shocks utilized in ICF experiments can drive the deuterium fuel from ambient to electron volt temperatures (T) and multi-fold compression. Modeling interatomic interactions over such an extreme range of conditions is challenging for empirical bond order potentials. We generate a pair potential for deuterium with explicit temperature and mass density dependence from ab initio density functional theory molecular dynamics using the iterative Boltzmann inversion method. This potential accurately reproduces the radial distribution functions and pressures from DFT in CMD equilibrium simulations across a wide range of thermodynamic conditions, yet fails to return the expected Hugoniot relations when used in direct CMD shock simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Statistical inference of collision frequencies from x-ray Thomson scattering spectra

Thomson scattering spectra measure the response of plasma particles to incident radiation. In warm dense matter, which is opaque to visible light, x-ray Thomson scattering (XRTS) enables a detailed probe of the electron distribution and has been used as a diagnostic for electron temperature, density, and plasma ionization. In this work, we examine the sensitivities of inelastic XRTS signatures to modeling details, including the dynamic collision frequency and the electronic density of states. Applying verified Monte Carlo inversion methods to dynamic structure factors obtained from time-dependent density functional theory, we assess the utility of XRTS signals as a way to inform the dynamic collision frequency, especially its direct-current limit, which is directly related to the electrical conductivity.

Collision frequency↗
DOE OSTI2025

Sequential spectral line analysis for accurate density and temperature diagnosis of laboratory opacity measurements

The accuracy of iron opacity calculated in stellar interiors has been questioned since the discovery of the “solar problem” and the discrepancies between the measured and modeled iron opacity reported in 2015. Experimental opacity benchmarks require accurate temperature and density measurements, which were inferred by analyzing tracer magnesium spectra in those experiments. Could the observed discrepancy be explained by insufficient accuracy in the inferred temperature, density, and their uncertainties? Previous analyses may have yielded biased results due to three limitations: (1) simultaneous multi-line fitting, (2) approximations in line-shape models, and (3) exclusion of certain spectral lines due to insufficient background characterization. Notably, the first issue is a common concern for many inversion methods, including Bayesian inferences. We present a refined analysis method that overcomes these limitations, applied to three categories of iron opacity experiments (Anchor 1, 2, and 3). In particular, the sequential fitting method yields unbiased results with more realistic uncertainties by accounting for line inconsistencies in the parameter uncertainties. The average electron temperature and density values are 162 ± 6 eV and (7.0 ± 1.9) × 10 21 cm −3 for six Anchor 1 experiments, 189 ± 7 eV and (3.4 ± 0.3) × 10 22 cm −3 for 21 Anchor 2 experiments, and 201 ± 6 eV and (4.8 ± 1.1) × 10 22 cm −3 for nine Anchor 3 experiments. These results show ∼4% temperature and ∼20% density reproducibility over a decade, which also aligns with the inferred parameter uncertainties. In conclusion, the resulting temperature and density uncertainties lead to a quasi-continuum iron opacity variation of ±4%–7% for wavelengths below 9.5 Å, which is insufficient to explain the significant model-data discrepancies reported in 2015.

Absorption spectroscopy↗
DOE OSTI2022

Development of coated heat flux gauges for fast responding measurements

Thermal systems often exhibit transient behaviors that have important implications for the operation of the system and can be difficult to predict. For these reasons, experimental testing is often required to ensure system durability requirements are achieved. One important parameter governing the survivability of components in hot, high-stress environments is the heat flux into the part that dictates the temperature distribution for the component. However, sensors required to experimentally characterize heat fluxes in extreme environments must also be resilient. Here, this study presents the development of coated heat transfer gauges capable of robust, high-frequency measurements in turbine research facilities. The addition of a protective coating increases the durability of the gauge, but inherent of that coating is the attenuation of high-frequency temperature penetrations. As a result, this study first outlines the use of analytical solutions to define a gauge design for a specific frequency range and heat transfer, ensuring that subsurface signals can be rectified to surface conditions through inverse methods. Then, the fabrication of polyimide substrate sensors with a parylene-F coating is described. Micro surface heaters added to the custom sensors were used to determine important geometric and thermal properties necessary to calculate accurate surface heat flux. Ultimately, this work shows increased sensor robustness in a turbine test bed and experimentally validates that the frequency response of the fabricated sensors meet the design intent.

47 OTHER INSTRUMENTATION↗