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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Three-dimensional stacking of canted antiferromagnetism and pseudospin current in undoped Sr 2 IrO 4 : Symmetry analysis and microscopic model realization

Recent optical second-harmonic generation experiments observed unexpected broken spatial symmetries in the undoped spin-orbit Mott insulator Sr2 IrO4, leading to intensive debates on the nature of its ground state. Here we propose that it is a canted antiferromagnetism (CAF) with a hidden order of circulating staggered pseudospin current. Symmetry analysis shows that a proper c-axis stacking of the CAF and the pseudospin current lead to a magnetoelectric coexistence state that breaks the twofold rotation, inversion, and time-reversal symmetries, consistent with experimental observations. We construct a three-dimensional Hubbard model with spin-orbit coupling and structural distortion for the five localized 5d Wannier orbitals centered at Ir sites and demonstrate the microscopic realization of the desired coexistence state in a wide range of band parameters via a combination of self-consistent Hartree-Fock and variational calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Prediction of tissue optical properties using the Monte Carlo modeling of photon transport in turbid media and integrating spheres

Monte Carlo methods are an established technique for simulating light transport in biological tissue. Integrating spheres make experimental measurements of the reflectance and transmittance of a sample straightforward and inexpensive. This work presents an extension to existing Monte Carlo photon transport methods to simulate integrating sphere experiments. Crosstalk between spheres in dual-sphere experiments is accounted for in the method. Analytical models, previous works on Monte Carlo photon transport, and experimental measurements of a synthetic tissue phantom validate this method. We present two approaches for using this method to back-calculate the optical properties of samples. Experimental and simulation uncertainties are propagated through both methods. Both back-calculation methods find the optical properties of a sample accurately and precisely. Our model is implemented in standard Python 3 and CUDA C++ [J. Nickolls, I. Buck, M. Garland, and K. Skadron, ACM Queue 6 , 40 ( 2008 ) ] and is publicly available in Code 1.

Cook, Patrick D. (ORCID:0000000279345428)↗
DOE OSTI2023

Modeling Wavelength Dependent Mid-infrared (5.5–25 μm) Optical Constants of Silicate Glasses: A Genetic Algorithm Approach

Wavelength-dependent mid-infrared (400–1,800 cm -1 ; 5.56–25 μm) optical constants (real and imaginary indices of refraction; n and k) are determined using reflectance spectra at a spectral sampling of 4 cm -1 for several silicate glasses of varying SiO 2 wt% which include (a) basaltic volcanic glass from Kilauea, Hawaii, (b) synthetic andesite, (c) two synthetic dacites, (d) obsidian volcanic glass from Mount Lassen, California, (e) synthetic rhyodacite, and (f) rhyolitic volcanic glass from Mexico. Because glasses are optically isotropic, no specific orientation was required for spectral measurements, and polished glass samples were measured at random orientations using micro-FTIR spectrometer. Lorentz-Lorenz dispersion theory and Fresnel reflectance model for high symmetry materials were used to model the optical constants by optimizing oscillator parameters in modeled spectra to match laboratory spectra. A genetic algorithm (GA) approach automatically finds the natural oscillators and their parameters for each spectrum, and then uses these parameters in a non-linear least squares optimization routine. The study compared spectral parameters such as Christiansen feature, reststrahlen bands (RBs), and the peaks centered around 860–1,100 (peak 1) and 400–480 cm -1 (peak 2) of n and k to their respective SiO 2 wt% of the glasses. CF, RBs, peak 1 of n and k, and peak 2 of n shift to higher wavenumbers with increased SiO 2 wt%, whereas peak 2 of k shifts to lower wavenumbers with increased SiO 2 wt%. Derived optical constants of these glasses will improve quantitative abundance mapping of volcanic materials on the surfaces of silicate targets in the Solar System.

58 GEOSCIENCES↗
DOE OSTI2023

Mutual optical intensity propagation through non-ideal two-dimensional mirrors

The mutual optical intensity (MOI) model is a partially coherent radiation propagation tool that can sequentially simulate beamline optics and provide beam intensity, local degree of coherence and phase distribution at any location along a beamline. This paper extends the MOI model to non-ideal twodimensional (2D) optical systems, such as ellipsoidal and toroidal mirrors with 2D figure errors. Simulation results show that one can tune the trade-off between calculation efficiency and accuracy by varying the number of wavefront elements. The focal spot size of an ellipsoidal mirror calculated with 100 $\times$ 100 elements gives less than 0.4% deviation from that with 250 $\times$ 250 elements, and the computation speed is nearly two orders of magnitude faster. Effects of figure errors on 2D focusing are also demonstrated for a non-ideal ellipsoidal mirror and by comparing the toroidal and ellipsoidal mirrors. Finally, the MOI model is benchmarked against the multi-electron Synchrotron Radiation Workshop (SRW) code showing the model’s high accuracy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel

Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0–100 μg mL –1 ), europium (0–150 μg mL –1 ), samarium (0–250 μg mL –1 ), praseodymium (0–350 μg mL –1 ), neodymium (0–1000 μg mL –1 ), and HNO 3 (2–4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet–visible–near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9–5.7% for the seven factors. Further, the optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Uncertainty of SW Cloud Radiative Effect in Atmospheric Models Due to the Parameterization of Liquid Cloud Optical Properties

Clouds are largely responsible for the spread of climate models predictions. Here we focus on the uncertainties in cloud shortwave radiative effect due to the parameterization of liquid cloud single scattering properties (SSPs) from liquid water content (LWC) and droplet number concentration (N), named parameterization of cloud optical properties. Uncertainties arise from not accounting for the droplet size distribution (DSD)—which affects the estimation of the effective radius (r eff ) and modulates the r eff -dependency of the SSPs—and from averaging SSPs over wide spectral bands. To assess these uncertainties a series of r eff -dependent SSPs parameterizations corresponding to various DSDs and spectral averaging methods are derived and implemented in a radiative code. Combined with the DSD-dependent estimation of r eff they are used to compute the bulk radiative properties (reflectance, transmittance, absorptance) of various clouds (defined in terms of LWC and N), including a homogeneous cloud, more realistic case studies, and outputs of a climate model. The results show that the cloud radiative forcing can vary up to 20% depending on the assumed DSD. Likewise, differences up to 20% are obtained for heating rates. The estimation of r eff is the main source of uncertainty, while the SSPs parameterization contributes to around 20% of the total uncertainty. Spectral averaging is less an issue, except for atmospheric absorption. Overall, global shortwave cloud radiative effect can vary by 6 W m –2 depending on the assumed DSD shape, which is about 13% of the best observational estimate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multifrequency Models of Black Hole Photon Rings from Low-luminosity Accretion Disks

Images of black holes encode both astrophysical and gravitational properties. Detecting highly lensed features in images can differentiate between these two effects. We present an accretion disk emission model coupled to the Adaptive Analytical Ray Tracing (AART) code that allows a fast parameter space exploration of black hole photon ring images produced from synchrotron emission from 10 to 670 GHz. As an application, we systematically study several disk models and compute their total flux density, average radii, and optical depth. The model parameters are chosen around fiducial values calibrated to general relativistic magnetohydrodynamic (GRMHD) simulations and observations of M87*. For the parameter space studied, we characterize the transition between optically thin and thick regimes and the frequency at which the first photon ring is observable. Our results highlight the need for careful definitions of photon ring radius in the image domain, as in certain models the highly lensed photon ring is dimmer than the direct emission at certain angles. We find that at low frequencies the ring radii are set by the electron temperature, while at higher frequencies the magnetic field strength plays a more significant role, demonstrating how multifrequency analysis can also be used to infer plasma parameters. Lastly, we show how our implementation can qualitatively reproduce multifrequency black hole images from GRMHD simulations when adding time variability to our disk model through Gaussian random fields. This approach provides a new method for simulating observations from the Event Horizon Telescope and the proposed Black Hole Explorer space mission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Dynamics of Gas Bubbles From a Submarine Hydrocarbon Seep Within the Hydrate Stability Zone

We validate a new model for mass transfer and bubble transport for natural seeps on the continental margins using an integrated observation of a seep at 883 m in the Northern Gulf of Mexico. In the model, mass transfer is assumed to transition from clean to dirty bubble mass transfer rates following a characteristic hydrate formation time that depends on the initial bubble surface area and the hydrate subcooling. We show that buoyancy-induced upwelling is negligible for the bubble stream. We initialize the model using precise data observed at the seafloor and validate the model results to optical and acoustic measurements within the bubble stream. The model accurately predicts the acoustic attenuation of the bubble flare by lateral spreading and bubble shrinkage throughout the water column. In conclusion, the model shows that up to 99.4% by mass of the released gases from this seep dissolves into the ocean within the hydrate stability zone.

03 NATURAL GAS↗
DOE OSTI2024

Optical constants measurements of liquids for modeling aerosolized threats

Spectroscopic identification of aerosolized chemical threats is challenging due to the complex nature of the photon/particle interaction, as well as the diversity of possible particle sizes, morphologies, and compositions. Constructing a database of laboratory - measured transmittance spectra that covers each permutation is not practical. However, calculation of the spectra using the measured optical constants as a function of wavenumber, n(?) and k(?), for each of the liquids and/or solids composing the aerosol particles provides a viable alternative. These synthetic spectra (vis-à-vis laboratory-measured spectra) can be used to identify chemicals of interest and subtract out background interferents in field measurements. Using a well-established multiple pathlength approach, we measure the optical constants n/k for liquids that may be found as chemical species of interest or common background interferents aerosolized in plumes. These measurements are also used to generate several test case aerosol synthetic spectra.

Lockwood, Schuyler↗
DOE OSTI2022

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.

Rao, Ashwin P. (ORCID:0000000319312568)↗
DOE OSTI2024

A model to separate conduction and radiation in high temperature laser flash measurements for semi-transparent materials

Laser flash analysis (LFA) is a widely used technique in the scientific and industrial communities for measuring the thermal diffusivity and conductivity of materials. However, traditional fitting methods based solely on heat conduction do not account for radiation heat transfer in transparent and semi-transparent materials, which becomes significant at high temperatures. In this study, we have developed a coupled radiation-conduction model that considers the optical properties of materials to accurately extract thermal diffusivity from LFA measurements. We conducted LFA measurements on fused silica glass samples of varying thicknesses and within the temperature range of 25 to 1000 °C and fitted the data with the model. Since the coupled model requires optical properties as input parameters, which are often unavailable for new materials, we have also proposed a modified transparent model. Compared to the coupled model, results from the modified transparent model generally overestimate thermal conductivity, but the errors are acceptable in silica glass with small thickness. Here, we also investigated the applicability of the modified transparent model with materials of different optical properties.

14 SOLAR ENERGY↗
DOE OSTI2025

Solving tricky quantum optics problems with assistance from large language models

The capabilities of modern artificial intelligence (AI) as a “scientific collaborator” are explored by engaging it with three nuanced problems in quantum optics: state populations in optical pumping, resonant transitions between decaying states (the Burshtein effect), and degenerate mirrorless lasing. Through iterative dialogue, the authors observe that AI models–when prompted and corrected–can reason through complex scenarios, refine their answers, and provide expert-level guidance, closely resembling the interaction with an adept colleague. The findings highlight that AI can democratize access to sophisticated modeling and analysis, shifting the focus in scientific practice from technical mastery to the generation and testing of ideas, and reducing the time for completing research tasks from days to minutes.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Ultrafast relaxation of acoustic and optical phonons in a topological nodal-line semimetal ZrSiS

Recently, nodal line semimetals based on ZrSiS-family have garnered massive research interests contributing numerous experimental and theoretical works. Despite being the most studied nodal-line semimetal, a clear understanding of the transient state relaxation dynamics and the underlying mechanism in ZrSiS is lacking. Using time- and angle-resolved photoemission spectroscopy, we study the ultrafast relaxation dynamics in ZrSiS and reveal a unique relaxation in the bulk nodal-line state which is well-captured by a simple model based on optical and acoustic phonon cooling. Our model predicts linear decay processes for both optical and acoustic phonon relaxations with optical cooling dominant at higher temperatures. Our results reveal different decay mechanisms for the bulk and surface states and pave a way to understand the mechanism of conduction in this material.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness

Abstract The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3‐HAM2.3 global atmosphere‐aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON‐A (icon‐aes‐1.3.00) global atmosphere model to yield the new ICON‐A‐HAM2.3 atmosphere‐aerosol model. The ICON‐A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub‐grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6‐HAM2.3 two‐moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON‐A‐HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON‐A‐HAM2.3 and ECHAM6.3‐HAM2.3, but the bias is larger in ICON‐A‐HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub‐tropical oceans. ICON‐A‐HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON‐A‐HAM2.3 compared to standard ICON‐A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON‐A‐HAM2.3 and in ECHAM6.3‐HAM2.3. In the extra‐tropics, a smaller multivariable bias is found for ICON‐A‐HAM2.3 than for ECHAM6.3‐HAM2.3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

NeuralMie (v1.0): an aerosol optics emulator

The direct interactions of atmospheric aerosols with radiation significantly impact the Earth's climate and weather and are important to represent accurately in simulations of the atmosphere. This work introduces two contributions to enable a more accurate representation of aerosol optics in atmosphere models: (1) NeuralMie, a neural network Mie scattering emulator that can directly compute the bulk optical properties of a diverse range of aerosol populations and is appropriate for use in atmosphere simulations where aerosol optical properties are parameterized, and (2) TAMie, a fast Python-based Mie scattering code based on the Toon and Ackerman (1981) Mie scattering algorithm that can represent both homogeneous and coated particles. TAMie achieves speed and accuracy comparable to established Fortran Mie codes and is used to produce training data for NeuralMie. NeuralMie is highly flexible and can be used for a wide range of particle types, wavelengths, and mixing assumptions. It can represent core-shell scattering and, by directly estimating bulk optical properties, is more efficient than existing Mie code and Mie code emulators while incurring negligible error compared to existing aerosol optics parameterization schemes (0.08 % mean absolute percentage error).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

POPSnet-SGP: A Pilot Aerosol Microphysics Network for Targeting Climate Model Uncertainty Interim Field Campaign Report

Aerosols mediate the radiative fluxes in clear and cloudy skies and dominate the uncertainty in the radiative forcing of climate. Relatively dense networks of aerosol optical depth measurements have been used to effectively constrain simulated aerosol optical properties, but model diversity of aerosol microphysical properties is much larger (Mann et al. 2014, Myhre et al. 2009). A better understanding of aerosol microphysics is essential as they are the fundamental pieces of information required to convert aerosol emissions information to radiative and cloud-nucleating properties that drive radiative effects and forcing of climate. Model representation of aerosol microphysical properties has lagged in part due to their inherently greater spatial variability, but also due to a lack of available observations. This project – the Printed Optical Particle Spectrometer Network (POPSnet)-Southern Great Plains (SGP) Pilot – launched the first spatially dense network of aerosol size distribution measurements over an area the size of a global model grid cell and demonstrated its use in providing valuable information regarding the spatial variability of aerosol microphysical properties and its drivers for improving model constraints.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Optical conductivity of the two-dimensional Hubbard model: Vertex corrections, emergent Galilean invariance, and the accuracy of the single-site dynamical mean field approximation

We compute the frequency-dependent conductivity of the two-dimensional square lattice Hubbard model at zero temperature as a function of density to second order in the interaction strength, and compare the results to the predictions of single-site dynamical mean field theory computed at the same order. We find that despite the neglect of vertex corrections, the single-site dynamical mean field approximation produces semiquantitatively accurate results for most carrier concentrations, but fails qualitatively for the nearly empty or nearly filled band cases where the model exhibits an emergent Galilean invariance. The DMFT approximation also becomes qualitatively inaccurate very near half filling if nesting is important.

2-dimensional systems↗
DOE OSTI2023

Fringing Analysis and Simulation for the Vera C. Rubin Observatory’s Legacy Survey of Space and Time

Abstract The presence of fringing in astronomical Charge-Coupled Device (CCD) images will have an impact on photometric quality and measurements. Yet its impact on the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) has not been fully studied. We present a detailed study on fringing for CCDs already implemented on the Rubin Observatory LSST Camera’s focal plane. After making physical measurements and knowing the compositions, we developed a model for the e2v CCDs. We present a method to fit for the internal height variation of the epoxy layer within the sensors based on fringing measurements in a laboratory setting. This method is generic enough that it can be easily modified to work for other CCDs. Using the derived fringing model, we successfully reproduce comparable fringing amplitudes that match the observed levels in images taken by existing telescopes with different optical designs. This model is then used to forecast the expected level of fringing in a single LSST y -band sky background exposure with Rubin telescope optics in the presence of a realistic time-varying sky spectrum. The predicted fringing amplitude in LSST images ranges from 0.04% to 0.2% depending on the location of a CCD on the focal plane. We find that the predicted variation in surface brightness caused by fringing in LSST y -band sky background images is about 0.6 μ Jy arcsec − 2 , which is 40 times larger than the current measurement error. We conclude that it is necessary to include fringing correction in the Rubin’s LSST image processing pipeline.

Astronomy & Astrophysics↗