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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Hybrid iterative approach for simulation of radio-frequency fields in plasma

In this work, a novel iterative approach for solving discretized linear wave equations in a frequency domain, which combines time evolution with iterative relaxation schemes, is presented. In this hybrid approach, each iteration cycle consists of evolution of electromagnetic (EM) fields in time over a specified number of field periods followed by several iterative relaxations. Provided that there is sufficient dissipation, both the time evolution and the iterative relaxations contribute to the convergence of the EM fields to the solution of the formulated full wave boundary value problem. Time evolution rapidly distributes EM fields, propagating with group velocity, over the simulation domain, while the iterative relaxations smooth the fields, reducing the numerical errors such that iteration cycles converge to a steady state solution, approximating the solution of the formulated problem. This approach is intended for large scale simulations which are beyond the capabilities of direct solvers presently used for solving wave equations in the frequency domain. Lastly, the technique is demonstrated for solving wave equations on a regular grid using a cold plasma dielectric model with collisions for 2D modeling of EM fields in tokamak in an electron cyclotron frequency range.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

The symmetrical quasi-classical approach to electronically nonadiabatic dynamics applied to ultrafast exciton migration processes in semiconducting polymers

In the last several years, a symmetrical quasi-classical (SQC) windowing model applied to the classical Meyer-Miller (MM) vibronic Hamiltonian has been shown to be a simple, efficient, general, and quite-accurate method for treating electronically nonadiabatic processes at the totally classical level. Here, the SQC/MM methodology is applied to ultrafast exciton dynamics in a Frenkel/site-exciton model of oligothiophene (OT) as a model of organic semiconductor polymers. In order to keep the electronic representation as compact and efficient as possible, the adiabatic version of the MM Hamiltonian was employed, with dynamical calculations carried out in the recently developed “kinematic momentum” representation, from which site/monomer-specific (diabatic) excitation probabilities were extracted using a new procedure developed in this work. The SQC/MM simulation results are seen to describe coherent exciton transport driven by planarization of a central torsion defect in the OT oligomer as well as to capture exciton self-trapping effects in good agreement with benchmark quantum calculations using the multi-layer multiconfiguration time-dependent Hartree approach. The SQC/MM calculations are also seen to significantly outperform the standard Ehrenfest approach, which shows serious discrepancies. Furthermore, these results are encouraging, not only because they illustrate a significant further application of the SQC/MM approach and its utility, but because they strongly suggest that classical mechanical simulations (with the potential for linear scaling efficiency) can be used to capture, quantitatively, important dynamical features of electronic excitation energy transfer in semiconducting polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A semiempirical effective Hamiltonian based approach for analyzing excited state wave functions and computing excited state absorption spectra using real-time dynamics

We describe a new approach to extract information about an excited state wave function using a reduced orbital space molecular orbital (MO) decomposition approach for time-dependent density obtained from real-time dynamics. We also show how this information about the excited state wave function can be used to accelerate the convergence of real-time spectra and model excited state electron dynamics. We have combined this approach with our recent implementation of the real-time INDO/S method to study the solvatochromic shift of Nile Red in acetone, ethanol, toluene and n-hexane solvents and, for the first time, the excited state absorption (ESA) spectra of coronene, 5,10,15,20-tetra(4-pyridyl)porphyrin (TPyP), zinc phthalocyanine (ZnPc), and nickel TPyP using a semiempirical Hamiltonian.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A unified view of hierarchy approach and formula of differentiation

The stochastic differential equation is a powerful tool for describing the dynamics of a dissipative system in which noise characterizes the influence of the environment. For the Ornstein-Uhlenbeck noise, both the formula of differentiation and the hierarchy approach provide efficient numerical simulations, with the stochastic differential equation transformed into a set of coupled, linear ordinary differential equations. We show that while these two deterministic schemes result in different sets of equations, they can be regarded as two representations of an underlying linear-dynamics. Moreover, by manipulating the involved Ornstein-Uhlenbeck noise, we propose a unified algorithm that may reduce to the hierarchy approach or the formula of differentiation in different limits. We further analyze the numerical performance of this algorithm and find that the hierarchy approach appears to be more efficient for our numerical model studies.

Yan, Yun-An (ORCID:000000031943459X)↗
DOE OSTI2020

Spectral solar irradiance on inclined surfaces: A fast Monte Carlo approach

Estimating spectral plane-of-array (POA) solar irradiance on inclined surfaces is an important step in the design and performance evaluation of both photovoltaic and concentrated solar plants. This work introduces a fast, line-by-line spectral, Monte Carlo (MC) radiative transfer model approach to simulate anisotropic distributions of shortwave radiation through the atmosphere as photon bundles impinge on inclined surfaces. This fast Monte Carlo approach reproduces the angular distribution of solar irradiance without the undesirable effects of spatial discretization and thus computes detailed POA irradiance values on surfaces at any orientation and also when surfaces are subjected to the anisotropic ground and atmospheric scattering. Polarization effects are also easily incorporated into this approach that can be considered as direct numerical simulation of the physics involved. Here, we compare our Monte Carlo radiative transfer model with the most widely used empirical transposition model, Perez4, under various conditions. The results show that the Perez4 model reproduces the more detailed Monte Carlo simulations with less than 10% deviation under clear skies for all relevant surface tilt and azimuth angles. When optically thin clouds are present, observed deviations are larger, especially when the receiving surface is strongly tilted. Deviations are also observed for large azimuth angle differences between the receiving surface and the solar position. When optically thick clouds are present, the two models agree within 15% deviation for nearly all surface orientation and tilt angles. The overall deviations are smaller when compared with cases for optically thin clouds. The Perez4 model performs very well (6.0% deviation) in comparison with the detailed MC simulations for all cases, thus validating its widespread use for practical solar applications. When detailed atmospheric profiles and cloud optical properties are available, the proposed fast Monte Carlo radiative model reproduces accurate spectral and angular POA irradiance levels for various atmospheric and cloud cover conditions, surface orientations, and different surface and ground properties.

42 ENGINEERING↗
DOE OSTI2022

A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures

A challenge of atomistic machine-learning (ML) methods is ensuring that the training data are suitable for the system being simulated, which is particularly challenging for systems with large numbers of atoms. Most atomistic ML approaches rely on the nearsightedness principle (“all chemistry is local”), using information about the position of an atom’s neighbors to predict a per-atom energy. Here, in this work, we develop a framework that exploits the nearsighted nature of ML models to systematically produce an appropriate training set for large structures. We use a per-atom uncertainty estimate to identify the most uncertain atoms and extract chunks centered around these atoms. It is crucial that these small chunks are both large enough to satisfy the ML’s nearsighted principle (that is, filling the cutoff radius) and are large enough to be converged with respect to the electronic structure calculation. We present data indicating when the electronic structure calculations are converged with respect to the structure size, which fundamentally limits the accuracy of any nearsighted ML calculator. These new atomic chunks are calculated in electronic structures, and crucially, only a single force—that of the central atom—is added to the growing training set, preventing the noisy and irrelevant information from the piece’s boundary from interfering with ML training. The resulting ML potentials are robust, despite requiring single-point calculations on only small reference structures and never seeing large training structures. We demonstrated our approach via structure optimization of a 260-atom structure and extended the approach to clusters with up to 1415 atoms.

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

A simple approach for characterizing the spatially varying sensitivity of microchannel plate detectors

Herein we present a simple approach to characterize the spatial variation of the gain in microchannel plate (MCP) coupled to phosphor detectors using single electron or photon hits. The technique is easy to implement and general enough to be extended to other kinds of detectors. We demonstrate the efficacy of the approach on both laboratory and Monte Carlo generated datasets. Furthermore, we use the approach to measure the variation in gain over time as the MCP is exposed to an increasing number of electrons.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A deep learning approach to fast analysis of collective Thomson scattering spectra

Fast analysis of collective Thomson scattering ion acoustic wave features using a deep convolutional neural network model is presented. The network was trained from spectra to predict the plasma parameters, including ion velocities, population fractions, and ion and electron temperatures. A fully kinetic particle-in-cell simulation was used to model a laboratory astrophysics experiment and simulate a diagnostic image of the ion acoustic wave feature. Network predictions were compared with Bayesian inference of the plasma model parameters for both the simulated and experimentally measured images. Both approaches were fairly accurate predicting the simulated image and the network predictions matched a good portion of the Bayesian results for the experimentally measured image. The Bayesian approach is more robust to noise and motivates future work to train deep learning models with realistic noise. The advantage of the deep learning model is making thousands of predictions in a few hundred milliseconds, compared to a few seconds to minutes per prediction for the optimization and Bayesian approaches presented here. The results demonstrate promising capabilities of deep learning models to analyze Thomson data orders of magnitude faster than conventional methods when using the neural network for standalone analysis. If more rigorous analysis is needed, neural network predictions can be used to quickly initialize other optimization methods and increase chances of success. This is especially useful when the dataset becomes very large or highly dimensional and manually refining initial conditions for the entire dataset are no longer tractable.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Structured light approaches in laser-based plasma diagnostics

There is a growing demand for plasma diagnostics suitable for industrial plasma reactors employed in semiconductor nanofabrication, especially relevant to microelectronics and quantum information systems. Such reactors typically have limited optical access and pose considerable diagnostic challenges, including intense background emission, significant thermal loads, and contamination of optical viewports. In this study, we outline research into structured light techniques (laser beams with tailored spatial, temporal, or phase characteristics) that effectively overcome these issues using laser-induced fluorescence (LIF) as an example. The focus of presented diagnostics is on ion kinetics analysis within an industrial plasma source, although this approach is broadly applicable to other plasma systems and diagnostic contexts. We present a confocal LIF implementation using an axicon-generated Bessel annular beam, achieving spatial resolutions of approximately 5 mm at a focal distance of 300 mm, with potential improvements to about 1 mm. This approach matches conventional orthogonal LIF performance but requires only one optical port. Wavelength-modulation LIF employs nonlinear laser wavelength tuning to measure spectral line derivatives, suppressing background emission and enhancing details of spectral line shape. Additionally, we present new results on applying vortex beams (laser beams carrying orbital angular momentum, OAM) for LIF measurements in an industrial plasma device. These measurements enable simultaneous axial and tangential velocity determination using a single laser beam and have been tested with xenon ion transition. Initial quantification of results was performed. Together, these structured-light approaches provide robust, background-resilient, multi-dimensional diagnostics for complex plasma environments.

Romadanov, Ivan [Princeton Plasma Physics Laborato↗
DOE OSTI2024

Transport coefficient approach for characterizing nonequilibrium dynamics in soft matter

Nonequilibrium states in soft condensed matter require a systematic approach to characterize and model materials, enhancing predictability and applications. Among the tools, X-ray photon correlation spectroscopy (XPCS) provides exceptional temporal and spatial resolution to extract dynamic insight into the properties of the material. However, existing models might overlook intricate details. We introduce an approach for extracting the transport coefficient, denoted as $J(t)$, from the XPCS studies. This coefficient is a fundamental parameter in nonequilibrium statistical mechanics and is crucial for characterizing transport processes within a system. Our method unifies the Green–Kubo formulas associated with various transport coefficients, including gradient flows, particle–particle interactions, friction matrices, and continuous noise. We achieve this by integrating the collective influence of random and systematic forces acting on the particles within the framework of a Markov chain. We initially validated this method using molecular dynamics simulations of a system subjected to changes in temperatures over time. Subsequently, we conducted further verification using experimental systems reported in the literature and known for their complex nonequilibrium characteristics. The results, including the derived $J(t)$ and other relevant physical parameters, align with the previous observations and reveal detailed dynamical information in nonequilibrium states. This approach represents an advancement in XPCS analysis, addressing the growing demand to extract intricate nonequilibrium dynamics. Further, the methods presented are agnostic to the nature of the material system and can be potentially expanded to hard condensed matter systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Application of the coupled-cluster CC( P ; Q ) approaches to the magnesium dimer

Several coupled-cluster approaches with an approximate treatment of connected triply or quadruply excited clusters, including the CCSD(T), CCSD(2) T , and CR-CC(2,3) corrections to CCSD, the active-space CCSDt and CCSDTq methods, and the CC(t;3) and CC(q;4) corrections to CCSDt and CCSDTq derived using the CC(P;Q) framework, and their CCSDT and CCSDTQ parents are applied to the ground-state potential curve and vibrational term values of the magnesium dimer. The correlation-consistent aug-cc-pV(n+d)Z and aug-cc-pwCVnZ (n = T and Q) basis sets are used. Among the noniterative triples corrections to CCSD, the CR-CC(2,3) approach performs the best, but, in analogy to the previously studied beryllium dimer [I. Magoulas et al., J. Phys. Chem. A 122, 1350 (2018)], the CC(t;3) and CC(q;4) methods outperform other employed approaches in reproducing the CCSDT and CCSDTQ data. Composite calculations combining the nearly all-electron CCSDT and valence CC(q;4) and CCSDTQ computations reproduce the experimentally resolved part of the vibrational spectrum of Mg2 to within ~1 cm -1 when the vibrational spacings are examined, while predicting five extra vibrational states near the dissociation threshold, which have not been experimentally resolved. The corresponding binding energies and equilibrium bond lengths are within a few cm -1 and 0.01 A from their experimentally derived values.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Dosimetry and uncertainty approaches for the million person study of low-dose radiation health effects: overview of the recommendations in NCRP Report No. 178

Purpose: Scientific Committee 6–9 was established by the National Council on Radiation Protection and Measurements (NCRP), charged to provide guidance in the derivation of organ doses and their uncertainty, and produced a report, NCRP Report No. 178, Deriving Organ Doses and their Uncertainty for Epidemiologic Studies with a focus on the Million Person Study of Low-Dose Radiation Health Effects (MPS). This review summarizes the conclusions and recommendations of NCRP Report No. 178, with a concentration on and overview of the dosimetry and uncertainty approaches for the cohorts in the MPS, along with guidelines regarding the essential approaches used to estimate organ doses and their uncertainties (from external and internal sources) within the framework of an epidemiologic study. Conclusions: The success of the MPS is tied to the validity of the dose reconstruction approaches to provide realistic estimates of organ-specific radiation absorbed doses that are as accurate and precise as possible and to properly evaluate their accompanying uncertainties. Further, the dosimetry aspects for the MPS are challenging in that they address diverse exposure scenarios for diverse occupational groups being studied over a period of up to 70 y. Specific dosimetric reconstruction issues differ among the varied exposed populations that are considered: atomic veterans, U.S. Department of Energy workers exposed to both penetrating radiation and intakes of radionuclides, nuclear power plant workers, medical radiation workers, and industrial radiographers. While a major source of radiation exposure to the study population comes from external gamma- or x-ray sources, for some of the study groups, there is also a meaningful component of radionuclide intakes that requires internal radiation dosimetry assessments.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

A Multi-Analysis Approach for Estimating Regional Health Impacts from the 2017 Northern California Wildfires

In the evening of October 8 and early hours of October 9, 2017, high winds in Northern California downed trees and power lines, igniting some of the most devastating wildfires the state had seen, and within hours unhealthy air quality impacted millions of people. We simulated these air quality conditions using fire detection information from the MODIS, VIIRS, and GOES-16 ABI satellite instruments, and applying a set of three WRF–CMAQ simulations, one data fusion, and three machine learning methods. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ air quality modeling system. Interestingly, this approach did not necessarily improve results compared to the baseline case, which used a default time profile. However, this approach was key to simulating the initial 12-hr explosive fire activity and smoke impacts. The WRF-CMAQ simulations compared well with observational data for the October 8-15 time period and tended to overestimate concentrations October 16-20. To improve these results, we applied three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across the WRF-CMAQ, data fusion, and machine learning datasets, the best Pearson correlation was 0.5. The data fusion and machine learning results were biased low and WRF-CMAQ results were biased high. Finally, we applied the optimized PM2.5 exposure estimate in a short-term exposure-response function. Total estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to wildland fire smoke.

O'Neill, Susan↗
DOE OSTI2021

Advancing a toolkit of diverse futures approaches for global environmental assessments

Global Environmental Assessments (GEAs) support national and international policy making for sustainable development. They rely on quantitative scenarios produced by Integrated Assessment Models (IAMs) to assess alternative futures. IAM-based scenarios can provide a coherent assessment framework that integrates different subsystems and their interactions. However, these top-down approaches have limited ability to represent key local dynamics, unexpected events, or the role of diverse actors. Other methods, including participatory scenarios, can help co-create narratives that emphasize diverse contents with clear links to existing decision-making. However, these bottom-up approaches provide only limited insight into global processes and are not constrained by measurable parameters. In our review, we identify four key challenges for current GEA scenarios: surprise, scale, diversity and imagination. We illustrate how these challenges can be overcome by combining top-down and bottom-up scenario approaches and conclude with a call to action for future research to take on this trans-disciplinary challenge.

Pereira, Laura M.↗
DOE OSTI2022

Sub Coulomb barrier d+ 208 Pb scattering in the time-dependent basis function approach

We employ the non-perturbative time-dependent basis function (tBF) approach to study the scattering of the deuteron on 208 Pb below the Coulomb barrier. We obtain the bound and discretized scattering states of the projectile, which form the basis representation of the tBF approach, by diagonalizing a realistic Hamiltonian in a large harmonic oscillator basis. We find that the higher-order inelastic scattering effects are noticeable for sub barrier scatterings with the tBF method. We have successfully reproduced experimental sub Coulomb barrier elastic cross section ratios with the tBF approach by considering only the electric dipole (E1) component of the Coulomb interaction between the projectile and the target during scatterings. Here, we find that the correction of the polarization potential to the Rutherford trajectory is dominant in reproducing the data at very low bombarding energies, whereas the role of internal transitions of the deuteron projectile induced by the E1 interaction during the scattering becomes increasingly significant at higher bombarding energies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Ground state properties of 3d metals from self-consistent GW approach

The self consistent GW approach (scGW) has been applied to calculate the ground state properties (equilibrium Wigner–Seitz radius S WZ and bulk modulus B) of 3d transition metals Sc, Ti, V, Fe, Co, Ni, and Cu. The approach systematically underestimates S WZ with average relative deviation from the experimental data of about 1% and it overestimates the calculated bulk modulus with relative error of about 25%. We show that scGW is superior in accuracy as compared to the local density approximation but it is less accurate than the generalized gradient approach for the materials studied. If compared to the random phase approximation, scGW is slightly less accurate, but its error for 3d metals looks more systematic. Lastly, the systematic nature of the deviation from the experimental data suggests that the next order of the perturbation theory should allow one to reduce the error.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Parameterizing empirical interatomic potentials for predicting thermophysical properties via an irreducible derivative approach: the case of ThO 2 and UO 2

The accuracy of classical physical property predictions using molecular dynamics simulations is determined by the quality of the interatomic potentials. Here we introduce a training approach for empirical interatomic potentials (EIPs) which is well suited for capturing phonons and phonon-related properties. Our approach is based on direct comparisons of the second- and third-order irreducible derivatives (IDs) between an EIP and the Born–Oppenheimer potential within density functional theory (DFT) calculations. IDs fully exploit space group symmetry and allow for training without redundant information. We demonstrate the fidelity of our approach in the context of ThO 2 and UO 2 , where we optimize parameters of an embedded-atom method potential in addition to core–shell interactions. Our EIPs provide thermophysical properties in good agreement with DFT and outperform widely utilized EIPs for phonon dispersion and thermal conductivity predictions. Reasonable estimates of thermal expansion and formation energies of Frenkel pairs are also obtained.

empirical interatomic potential↗
DOE OSTI2026

SALSAA: a statistical approach to line shapes from an average atom

Ion-Stark line broadening is a key density diagnostic for hot dense plasmas relevant to inertial fusion and astrophysics. It is caused by interactions of a radiating ion with nearby perturbing ions, whose electric microfields lead to changes in bound-bound transition energies. Ion-Stark broadening becomes increasingly difficult to compute for complex, many-electron ions with myriad transitions. In this paper, we propose a simplified approach to ion-Stark broadening based on self-consistent ion distributions and electronic structure from an average-atom model. We find that this approach reproduces the line shape predictions of one traditional method for high-n K-shell emission lines in aluminum ions with accuracy sufficient for density diagnostics in thermal plasmas with equal ion and electron temperatures. We expect that this approach can be extended to provide a reasonable picture of ion-Stark broadening in many-electron ions, enabling rapid calculations of line profiles in complex spectra.

average-atom↗