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At least 937 records · Page 52

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DOE OSTI2018

Using multiple x-ray emission images of inertially confined implosions to identify spatial variations and estimate confinement volumes (invited)

We describe two methods to analyze multiple x-ray images of a small, self-emitting object, and we apply these methods to the stagnating hotspots in inertial confinement fusion experiments. The first method, the common integrated profile, can be employed to assess and quantify spatial variations in opacity. It is both a simple assessment of consistency and a sophisticated measurement of variations in a region that is otherwise difficult to observe. Second, we present a method to estimate volumes of highly asymmetric objects using multiple images of x-ray emission. The method is based on image intensities and does not require any explicit assumption of symmetry

47 OTHER INSTRUMENTATION↗

NASA NTRS2019

The Role of Thermodynamic Phase Shifts in Cloud Optical Depth Variations with Temperature

We present a novel method that identifies the contributions of thermodynamic phase shifts and processes governing supercooled liquid and ice clouds to cloud optical depth variations with temperature using MODIS observations. Our findings suggest that thermodynamic phase shifts outweigh the net influence of processes governing supercooled liquid and ice clouds in causing increases in mid-latitudinal cold cloud optical depth with temperature. Cloud regime analysis suggests that dynamical conditions appear to have less influence on the contribution of thermodynamic phase shifts to cloud optical depth variations with temperature. Thermodynamic phase shifts also contribute more to increases in cloud optical depth during colder seasons due to the enhanced optical thickness contrast between liquid and ice clouds. The results of this study highlight the importance of thermodynamic phase shifts in explaining cold cloud optical depth increases with temperature in the current climate and may elucidate their role in the cloud optical depth feedback.

cloud optical depth↗

DOE OSTI2023

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Quantifying uncertainty associated with the microstructure variation of a material can be a computationally daunting task, especially when dealing with advanced constitutive models and fine mesh resolutions in the crystal plasticity finite element method (CPFEM). Numerous studies have been conducted regarding the sensitivity of material properties and performance to the mesh resolution and choice of constitutive model. However, a unified approach that accounts for various fidelity parameters, such as mesh resolutions, integration time-steps and constitutive models simultaneously is currently lacking. Further, this paper proposes a novel uncertainty quantification (UQ) approach for computing the properties and performance of homogenized materials using CPFEM, that exploits a hierarchy of approximations with different levels of fidelity. In particular, we illustrate how multi-level sampling methods, such as multi-level Monte Carlo (MLMC) and multi-index Monte Carlo (MIMC), can be applied to assess the impact of variations in the microstructure of polycrystalline materials on the predictions of homogenized materials properties. We show that by adaptively exploiting the fidelity hierarchy, we can significantly reduce the number of microstructures required to reach a certain prescribed accuracy. Finally, we show how our approach can be extended to a multi-fidelity framework, where we allow the underlying constitutive model to be chosen from either a phenomenological plasticity model or a dislocation-density-based model.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis

We present a novel unsupervised machine learning method for quantitative representation of scanning electron micrographs and its applications and performance for nuclear forensic analysis of uranium ore concentrates. The method uses a vector quantizing variational autoencoder followed by a histogram operation to encode a micrograph into a single dimensional representation, called the latent vector. The method requires no extant labeling of the data and can be applied over large datasets of micrographs with minimal human interaction. The representations generated are broadly descriptive of each micrograph and the microstructure of the material imaged. In the case of uranium ore concentrate analysis, the representations were amenable to processing reagent and ore concentrate species classification with accuracy of 81:8%, which is competitive with state-of-the-art supervised networks. The representations were also used to classify previously unseen processing routes, were able to classify imaging parameters such as magnification (to 76:0% accuracy), were able to classify fine grained process parameters such as calcining temperature (to 74:4% accuracy), and their informatic properties indicate that they are generally descriptive of the image represented. This method can be applied across microstructure analysis fields to perform quantitative analysis without the need for labor intensive and possibly biased human analysis.

Scanning Electron Microscopy, Vector Quantizing Va↗

NASA NTRS1957

Rocket thrust variation with foamed liquid propellants

An analysis is presented on a method for varying rocket thrust by varying the bulk density of the propellants. This density variation was accomplished by uniformly dispersing an inert, insoluble gas in the liquid propellants. Only qualitative agreement with theory was obtained from preliminary experiments with a 1000-pound-thrust ammonia - nitric acid rocket engine; the required experimental gas-flow rates were two to six times greater than those predicted by theory. It was demonstrated, however, that this method of rocket-thrust variation is feasible.

Morrell, G↗

DOE OSTI2024

Contrasting roles of rice root iron plaque in retention and plant uptake of silicon, phosphorus, arsenic, and selenium in diverse paddy soils

Abstract Background and aims Iron (Fe) plaque on rice roots is a mixture of Fe oxide and oxyhydroxide minerals thought to protect rice from high levels of arsenic (As) in flooded paddy soils. Silicon (Si), phosphorus (P), and selenium (Se) also exist as oxyanions in rice paddies, but the impacts of Fe plaque on uptake of these nutrients are unknown. Methods We used natural variation in paddy soil chemistry to test how Si, P, As, and Se move from porewater to plaque to plant via multiple techniques. In a pot study, we monitored Fe plaque deposition and porewater chemistry in 5 different soils over time and measured plaque/plant chemistry and Fe plaque mineralogy at harvest. We normalized oxyanion concentrations by Fe to determine the preferential retention on plaque or plant uptake. Results Low phosphorus availability increased root Fe-oxidizing activity, while Fe, Si, P, As, and Se concentrations in plaque were strongly correlated with porewater. Plaque did not appreciably retain Si and Se, and the oxyanions did not compete for adsorption sites on the Fe plaque. Conclusion Root Fe plaque seems to protect rice from As uptake, does not interfere with Si and Se uptake, and roots adapt to maintain P nutrition even with retention of porewater P on plaque.

Agriculture↗

DOE OSTI2025

Theory and numerics of subspace approximation of eigenvalue problems

Large-scale eigenvalue problems arise in various fields of science and engineering and demand computationally efficient solutions. In this study, we investigate the subspace approximation for parametric linear eigenvalue problems, aiming to mitigate the computational burden associated with high-fidelity systems. Furthermore, we provide general error estimates under non-simple eigenvalue conditions, establishing some theoretical foundations for understanding the convergence behavior of subspace approximations. Numerical examples, including problems with one-dimensional to three-dimensional spatial domain and one-dimensional to two-dimensional parameter domain, are presented to demonstrate the efficacy of reduced basis method in handling parametric variations in boundary conditions and coefficient fields to achieve significant computational savings while maintaining high accuracy, making them promising tools for practical applications in large-scale eigenvalue computations.

Eigenvalue problems↗

DOE OSTI2019

Anomaly detection in scientific data using joint statistical moments

We propose an anomaly detection method for multi-variate scientific data based on analysis of high-order joint moments. Using kurtosis as a reliable measure of outliers, we suggest that principal kurtosis vectors, by analogy to principal component analysis (PCA) vectors, signify the principal directions along which outliers appear. The inception of an anomaly, then, manifests as a change in the principal values and vectors of kurtosis. Obtaining the principal kurtosis vectors requires decomposing a fourth order joint cumulant tensor for which we use a simple, computationally less expensive approach that involves performing a singular value decomposition (SVD) over the matricized tensor. We demonstrate the efficacy of this approach on synthetic data, and develop an algorithm to identify the occurrence of a spatial and/or temporal anomalous event in scientific phenomena. The algorithm decomposes the data into several spatial sub-domains and time steps to identify regions with such events. Feature moment metrics, based on the alignments of the principal kurtosis vectors, are computed at each sub-domain and time step for all features to quantify their relative importance towards the overall kurtosis in the data. Accordingly, spatial and temporal anomaly metrics for each sub-domain are proposed using the Hellinger distance of the feature moment metric distribution from a suitable nominal distribution. Finally, we apply the algorithm to two turbulent auto-ignition combustion cases and demonstrate that the anomaly metrics reliably capture the occurrence of auto-ignition in relevant spatial sub-domains at the right time steps.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Quantum Simulation of Molecular Electronic States with a Transcorrelated Hamiltonian: Higher Accuracy with Fewer Qubits

Simulation of electronic structure is one of the most promising applications on noisy intermediate-scale quantum (NISQ) era devices. However, NISQ devices suffer from a number of challenges like limited qubit connectivity, short coherence times, and sizable gate error rates. Thus, desired quantum algorithms should require shallow circuit depths and low qubit counts to take advantage of these devices. Here, we attempt to reduce quantum resource requirements for molecular simulations on a quantum computer while maintaining the desired accuracy with the help of classical quantum chemical theories of canonical transformation and explicit correlation. In this work, compact ab initio Hamiltonians are generated classically, in the second quantized form, through an approximate similarity transformation of the Hamiltonian with (a) an explicitly correlated two-body unitary operator with generalized pair excitations that remove the Coulombic electron–electron singularities from the Hamiltonian and (b) a unitary one-body operator to efficiently capture the orbital relaxation effects required for accurate description of the excited states. The resulting transcorrelated Hamiltonians are able to describe both the ground and the excited states of molecular systems in a balanced manner. Using the variational quantum eigensolver (VQE) method based on the unitary coupled cluster with singles and doubles (UCCSD) ansatz and only a minimal basis set (ANO-RCC-MB), we demonstrate that the transcorrelated Hamiltonians can produce ground state energies comparable to the reference CCSD energies with the much larger cc-pVTZ basis set. This leads to a reduction in the number of required CNOT gates by more than 3 orders of magnitude for the chemical species studied in this work. Furthermore, using the quantum equation of motion (qEOM) formalism in conjunction with the transcorrelated Hamiltonian, we are able to reduce the deviations in the excitation energies from the reference EOM-CCSD/cc-pVTZ values by an order of magnitude. In conclusion, the transcorrelated Hamiltonians developed here are Hermitian and contain only one- and two-body interaction terms and thus can be easily combined with any quantum algorithm for accurate electronic structure simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Modeling L 2,3 -edge X-ray absorption spectroscopy with linear response exact two-component relativistic time-dependent density functional theory

X-ray absorption spectroscopy (XAS) is a powerful tool that can provide physical insights into element-specific chemical processes and reactivities. Although relativistic time-dependent density functional theory (TDDFT) has been previously applied to model the L-edge region in XAS, there has not been a more comprehensive study of the choices of basis sets and density functional kernels available for variational relativistic excited state methods. In this work, we introduce the implementation of the generalized preconditioned locally harmonic residual algorithm to solve the complex-valued relativistic TDDFT for modeling the L-edge X-ray absorption spectra. Further, we investigate the L 2,3 -edge spectra of a series of molecular complexes using relativistic linear response TDDFT with a hybrid iterative diagonalization algorithm. A systematic error analysis was carried out with a focus on the energetics, intensities, and magnitude of L 2 –L 3 splitting compared to experiments. Additionally, the results from relativistic TDDFT calculations are compared to those computed using other theoretical methods, and the multideterminantal effects on the L-edge XAS were investigated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Ab initio molecular dynamics on quantum computers

Ab initio molecular dynamics (AIMD) is a valuable technique for studying molecules and materials at finite temperatures where the nuclei evolve on potential energy surfaces obtained from accurate electronic structure calculations. In this work, we present an approach to running AIMD simulations on noisy intermediate-scale quantum (NISQ)-era quantum computers. The electronic energies are calculated on a quantum computer using the variational quantum eigensolver (VQE) method. Algorithms for computation of analytical gradients entirely on a quantum computer require quantum fault-tolerant hardware, which is beyond NISQ-era. Therefore, we compute the energy gradients numerically using finite differences, the Hellmann-Feynman theorem, and a correlated sampling technique. This method only requires additional classical calculations of electron integrals for each degree of freedom without any additional computations on a quantum computer beyond the initial VQE run. As a proof of concept, AIMD simulations are demonstrated for the H-2 molecule on IBM quantum devices. In addition, we demonstrate the validity of the method for larger molecules using full configuration interaction wave functions. As quantum hardware and noise mitigation techniques continue to improve, the method can be utilized for studying larger molecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Attaining the promise of plant gene editing at scale

Crop improvement relies heavily on genetic variation that arises spontaneously through mutation. Modern breeding methods are very adept at combining this genetic variation in ways that achieve remarkable improvements in plant performance. Novel traits have also been created through mutation breeding and transgenesis. The advent of gene editing, however, marks a turning point: With gene editing, synthetic variation will increasingly supplement and, in some cases, supplant the genetic variation that occurs naturally. We are still in the very early stages of realizing the opportunity provided by plant gene editing. At present, typically only one or a few genes are targeted for mutation at a time, and most mutations result in loss of gene function. New technological developments, however, promise to make it possible to perform gene editing at scale. RNA virus vectors, for example, can deliver gene-editing reagents to the germ line through infection and create hundreds to thousands of diverse mutations in the progeny of infected plants. With developmental regulators, edited somatic cells can be induced to form meristems that yield seed-producing shoots, thereby increasing throughput and shrinking timescales for creating edited plants. As these approaches are refined and others developed, they will allow for accelerated breeding, the domestication of orphan crops and the reengineering of metabolism in a more directed manner than has ever previously been possible.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Characterization of the arc in crossflow using a two-temperature nonequilibrium plasma flow model

Diverse industrial applications such as circuit breakers and wire arc spraying involve the interaction between an electric arc and a stream of gas impinging perpendicular to it, a configuration commonly referred to as the arc in crossflow. The arc in crossflow is simulated using a three-dimensional time-dependent two-temperature (heavy-species and electrons) plasma flow model to better capture plasma-gas interactions and deviations from Local Thermodynamic Equilibrium (LTE). The coupled fluid-electromagnetic flow model is solved in a monolithic manner using Variational Multiscale Finite Element Method. Simulation findings are validated with experimental findings and contrasted against results obtained with a LTE model. Results from the two-temperature model corroborate experimental observations while providing quantification of the deviation between heavy-species and electron temperatures. The model is used to characterize the arc in crossflow as a function of the Reynolds and Enthalpy dimensionless numbers, which encapsulate the inter-dependence among the main parameters total current, inflow velocity, and inter-electrode spacing. Furthermore, the characterization revealed the behavior of arc shape, voltage drop, arc power, the degree of nonequilibrium, as well as the characteristic plasma front thickness, with varying controlling parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Application of an efficient generator-coordinate subspace-selection algorithm to neutrinoless double- β decay

The generator coordinate method begins with the variational construction of a set of nonorthogonal mean-field states that span a subspace of the full many-body Hilbert space. These states are then often projected onto states with good quantum numbers to restore symmetries, leading to a set with members that can be similar to one another, and it is sometimes possible to reduce this set without greatly affecting results. Here, we propose a greedy algorithm that we call the energy-transition-orthogonality procedure (ENTROP) to select subsets of important states. As applied here, the approach selects on the basis of diagonal energy, orthogonality, and contribution to the matrix element that governs neutrinoless double-β decay. We present both shell-model and preliminary ab initio calculations of this matrix element for the decay of 76Ge, with quadrupole deformation parameters and the isoscalar pairing strength as generator coordinates. ENTROP converges quickly, reducing significantly the number of basis states needed for an accurate calculation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Negative thermal expansion in the Ruddlesden-Popper calcium titanates

Materials exhibiting negative thermal expansion (NTE) are important for the fabrication and operation of microelectronic devices and optical systems. In this paper, combining density-functional-theory calculations and the self-consistent quasiharmonic approximation method, we investigate the variation of the NTE mechanisms active in layered calcium titanate oxides. We find a NTE trend with layer number for the orthorhombic materials comprising the RP series, but the monoclinic polymorph is an outlier.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Numerical Simulations of Noisy Variational Quantum Eigensolver Ansatz Circuits

This is a case study of the variational quantum eigensolver (VQE) method using numerical simulations to test the influence of noise on the accuracy of the underlying circuit ansatz. We investigate a computational chemistry application of VQE to calculate the electronic ground state and its energy for Sodium Hydride (NaH), a prototypical two-electron problem. Using a one-parameter ansatz derived from unitary coupled cluster (UCC) theory, we simulate the effects of noise on the energy expectation value and variance with respect to the ansatz parameter. These numerical simulations provide insights into the accuracy of the prepared quantum state and the efficiency of the classical optimizer that iteratively refines the ansatz. We conduct a comparative study between analytical results derived for the UCC ansatz in the absence of noise and the noisy numerical simulation results obtained using an isotropic depolarizing noise model for each gate. We also compare the relative increase in noise on logically equivalent UCC ansatz circuits generated by randomized compiling. Notably, we observe that the intrinsic variance in the energy due to the simplicity of the ansatz itself compares with the noise induced by the bare circuit.

Gowrishankar, Meenambika↗

DOE OSTI2020

Characterization and differentiation of aluminum powders used in improvised explosive devices – Part 1: Proof of concept of the utility of particle micromorphometry

Abstract Aluminum (Al) powders are commonly used in improvised explosive devices as metallic fuels, a component of explosive mixtures. These powders can be obtained readily from industrial‐scale and consumer products, and produced using unsophisticated “kitchen chemistry” techniques. This research demonstrates the potential of automated particle micromorphometry for comparisons between known source and questioned Al powders recovered from IEDs, as well as for insight into the method of Al powder manufacture. Al powder samples were obtained from legitimate manufacturers, and 56 samples were produced “in‐house” from Al‐containing spray paints and ball‐milled Al foils. Transmitted light microscope images of Al powder particles were acquired using an automated stage with automated z‐focus; 17 size and shape parameters were measured for all particles. Approximately 37,000–2,500,000 particles/sample were analyzed using an open‐source statistical package with customized code. Dimensionality reduction was required for processing the large datasets: eight of the 17 measured variables were selected based on inspection of the correlation matrix. Data from four subsamples from each of the 56 samples produced using “in‐house” methods were analyzed using ANOVA to assess the within‐ and between‐sample variation. High within‐sample variation was noted; however, ANOVA and post‐hoc Tukey's honestly significant difference (HSD) tests demonstrated that the between‐sample variation was substantially larger than the within‐sample variation. Each sample could be differentiated from all other samples in the test set. Future experiments will focus on ways to reduce the within‐sample variation, and additional statistical and microanalytical methods to classify sources and confidently constrain the method of Al powder manufacture.

Baldaino, JenaMarie↗

DOE OSTI2023

Recent Hydrodynamic Modeling Enhancements in OpenFAST

To improve the hydrodynamic modeling capabilities of the offshore wind design and modeling tool OpenFAST, a restructuring of the HydroDyn hydrodynamics module was undertaken with several new features implemented. The generation of the wave field is now separated from HydroDyn into a new module called SeaState. Unlike previous versions of HydroDyn, which precomputed the wave kinematics at the undisplaced positions of the hydrodynamic nodes, the new SeaState module computes the complete time history of the wave field at the vertices of a user-defined wave grid. During the simulation, the wave kinematics at any point within the grid can be efficiently interpolated, allowing the strip-theory wave loads to be evaluated based on the wave kinematics at the displaced structure position. A phase correction was also implemented for the potential-flow wave-exciting loads to account for large structure displacements. Several wave-stretching methods were implemented for the strip-theory solution along with a wave-load redistribution method that ensures smooth variation of the nodal loads as the nodes of a discrete hydrodynamic mesh enter and exit the water. The load smoothing is included to avoid the excitation of unphysical high-frequency structural vibration during hydroelastic simulations. The MacCamy-Fuchs diffraction correction for the strip-theory solution and the capability to insert a constrained NewWave into extreme stochastic sea states with directional spreading were also added. The present article documents the formulations of the new improvements to HydroDyn with example applications and numerical results.

constrained wave↗