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At least 757 records · Page 42

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DOE OSTI2023

Methods, systems, and apparatuses for calculating global fluence for neutron and photon monte carlo transport using expected value estimators

Global fluence estimators may be calculated on accelerators and processors for neutron and photon Monte Carlo transport. Monte Carlo random walk simulation may be performed on the processors and the calculation of a Volumetric-Ray-Casting (VRC) estimator may be offloaded to the accelerators. The VRC estimator may modify an expected-value estimator to extend a pseudo-particle ray along the direction of the emitted particle from source and collision event through not only the event volume, but also through all volumes that describe the problem geometry. Additionally, many pseudo-particle rays may be sampled per event, rather than just a single pseudo-particle ray per event, in order to provide more complete angular coverage.

Sweezy, Jeremy Ed↗

DOE OSTI2026

A physics informed model for the prediction of surface energies and the shape of metal particles

Surface energies of metal-based systems are important for determining the Wulff-constructed shapes of metal nanoparticles and understanding the stability. We have developed a physics informed method to predict the total energy of metal-based systems across a wide range of configurations. Our method has been tested against density functional theory (DFT) calculations for late transition metals. This method enables on-the-fly surface energy predictions based on the fundamental of materials property and allows for the Wulff construction of metal particles for a random number of elemental atoms and without the need for DFT calculations. By making a division between atoms in the different layers of the model system, we can improve the accuracy of the model, suggesting a dissimilarity between the electronic structure due to an alternating compression and expansion of atomic layers. We find that our model accurately and effectively provides valuable insights into the distribution and stability of nanoparticles.

Mandal, Shyama Charan (ORCID:0000000245882874)↗

DOE OSTI2020

Controlled thermal coefficient product system and method

A controlled thermal coefficient product manufacturing system and method is disclosed. The disclosed product relates to the manufacture of metallic material product (MMP) having a thermal expansion coefficient (TEC) in a predetermined range. The disclosed system and method provides for a first material deformation (FMD) of the MMP that comprises at least some of a first material phase (FMP) wherein the FMP comprises martensite randomly oriented and a first thermal expansion coefficient (FTC). In response to the FMD at least some of the FMP is oriented in at least one predetermined orientation. Subsequent to deformation, the MMP comprises a second thermal expansion coefficient (STC) that is within a predetermined range and wherein the thermal expansion of the MMP is in at least one predetermined direction. The MMP may be comprised of a second material phase (SMP) that may or may not transform to the FMP in response to the FMD.

Monroe, James Alan↗

DOE OSTI2023

Controlled thermal coefficient product system and method

Monroe, James Alan↗

DOE OSTI2024

Magnetic dipole γ-ray strength functions in the crossover from spherical to deformed neodymium isotopes

We calculate the magnetic dipole $\gamma$-ray strength functions in a chain of even-mass neodymium isotopes $^{144-152}$Nd in the framework of the configuration-interaction (CI) shell model. We infer the strength function by applying the maximum entropy method (MEM) to the exact imaginary-time response function calculated with the shell-model Monte Carlo (SMMC) method. The success of the MEM depends on the choice of a good strength function as a prior distribution. We investigate two choices for the prior strength function: the static path approximation (SPA) and the quasiparticle random-phase approximation (QRPA). We find that the QRPA is a better approximation at low temperatures (i.e., near the ground state), while the SPA is a better choice at finite temperatures. We identify a low-energy enhancement (LEE) in the MEM deexcitation $M1$ strength functions of the even-mass neodymium isotopes and compare with recent experimental results for the total deexcitation $\gamma$-ray strength functions. The LEE is already seen in the SPA strength function but not in the QRPA strength function, indicating the importance of large-amplitude static fluctuations around the mean field in reproducing the LEE. Our method is currently the only one which can reproduce LEE in heavy open-shell nuclei where conventional CI shell model calculations are prohibited. With the onset of deformation as number of neutrons increases along the chain of neodymium isotopes, we observe that some of the LEE strength transfers to a low-energy excitation, which we interpret as a finite-temperature ``scissors'' mode. Here, we also observe a finite-temperature spin-flip mode.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Neutron Capture Cross Sections of Radioactive Nuclei

Alternative methods to calculate neutron capture cross sections on radioactive nuclei are reported using the theory of Inclusive Non-Elastic Breakup (INEB) developed by Hussein and McVoy. The statistical coupled-channels theory proposed in Ref. [2] is further extended in the realm of random matrices. The case of reactions with the projectile and the target being twocluster nuclei is also analyzed and applications are made for scattering from a deuteron target. An extension of the theory to a three-cluster projectile incident on a two-cluster target is also discussed. The theoretical developments described here should open new possibilities to obtain information on the neutron capture cross sections of radioactive nuclei using indirect methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10 4 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Influence of Wetting on Viscous Fingering Via 2D Lattice Boltzmann Simulations

We present simulations of two-phase flow using the Rothman and Keller colour gradient Lattice Boltzmann method to study viscous fingering when a “red fluid” invades a porous model initially filled with a “blue” fluid with different viscosity. We conducted eleven suites of 81 numerical experiments totalling 891 simulations, where each suite had a different random realization of the porous model and spanned viscosity ratios in the range $$M\in [0.01,100]$$ M ϵ [ 0.01 , 100 ] and wetting angles in the range $$\theta _w\in [180^\circ ,0^\circ ]$$ θ w ϵ [ 180 ° , 0 ° ] to allow us to study the effect of these parameters on the fluid-displacement morphology and saturation at breakthrough (sweep). Although sweep often increased with wettability, this was not always so and the sweep phase space landscape, defined as the difference in saturation at a given wetting angle relative to saturation for the non-wetting case, had hills, ridges and valleys. At low viscosity ratios, flow at breakthrough is localized through narrow fingers that span the model. After breakthrough, the flow field continues to evolve and the saturation continues to increase albeit at a reduced rate, and eventually exceeds 90% for both non-wetting and wetting cases. The existence of a complicated sweep phase space at breakthrough, and continued post-breakthrough evolution suggests the hydrodynamics and sweep is a complicated function of wetting angle, viscosity ratio and time, which has major potential implications to Enhanced Oil Recovery by water flooding, and hence, on estimates of global oil reserves. Validation of these results via experiments is required to ensure they translate to field studies.

Engineering↗

DOE OSTI2023

Contact-Less Integrity Verification of Microelectronics Using Near-Field EM Analysis

Modern microelectronics life-cycle and supply chain ecosystem bring multiple untrusted entities, which can compromise their integrity. A major integrity issue of microelectronics stems from piracy of intellectual properties (IP) and counterfeiting, which causes significant revenue loss to the semiconductor manufacturers. Further, these components often lead to compromised functionality, reliability, security, and safety of an electronic system. This paper presents secure information transmission and probing methods for verifying the integrity of digital integrated circuit (ICs) based on their electromagnetic (EM) near-field emissions and thereby protecting systems against counterfeit components. The proposed method has been tested on both high-level instructions executed by microprocessors or Systems-on-Chip (serving as examples of software), and also logic circuits within FPGA fabrics and ASICs (serving as examples of hardware). The authentication information required by each digital system is generated using a pseudo-random number generator circuit and securely transmitted via near-field magnetic emissions. The authorized party can probe these emissions using a near-field probe, process the acquired signals to improve the signal-to-noise ratio (SNR), and then recover the secure information through matched filtering. Experimental results from commercial SoCs are used to demonstrate the proposed technique. Methods for reducing EM interference during integrity verification of both FPGAs and ASICs are also described.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Landsat 8 monitoring of multi-depth suspended sediment concentrations in Lake Erie’s Maumee River using machine learning

Satellite remote sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. However, due to the complexity of sediment-water interactions, it has been difficult to derive linear and non-linear regression equations to reliably predict SSC, especially when trying to estimate depth of integrated sediment. Herein, this study uses Landsat 8 OLI (Operational Land Imager) sensor to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). Simple linear least squares regression (LLSR), and three common machine learning models: random forest (RF), support vector regression (SVR), and model averaged neural network (MANN) were used to estimate SSC at the depth intervals. All machine learning models significantly outperformed LLSR while RF performed the best. In both RF and MANN, R2 (coefficient of determination) increases with depth with a maximum R2 of 0.89 and 0.83, respectively, at a depth of 0–182 cm. The results show that machine learning models can implement nonlinear relationships that produce better predictions than traditional linear regression methods in estimating depth integrated SSC, especially when samples are limited.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

GANISP: A GAN-Assisted Importance Splitting Probability Estimator

Designing manufacturing processes with high yield and strong reliability relies on effective methods for rare event estimation. Genealogical importance splitting reduces the variance of rare event probability estimators by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is difficult when applied to deterministic systems where the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. However, this random perturbation strategy may be effective for some systems while failing for others, preventing variance reduction in the probability estimate. This work seeks to address this limitation using a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The proposed GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).

generative adversarial network↗

DOE OSTI2022

Minimizing thickness variation in monolithic U-10Mo fuel foil and Zr interlayer during hot rolling: A microstructure-based finite element method analysis

Low-enriched uranium alloyed with 10 wt. % molybdenum (U-10Mo) has been identified as a promising alternative to highly enriched uranium fuel for the United States’ high performance research reactors. The monolithic U-10Mo fuel plate consists of a metallic U-10Mo fuel foil with a 25 µm Zr interlayer and a relatively thick cladding of aluminum alloy 6061. The Zr interlayer is typically applied during the hot co-rolling process, and this process dictates the uniformity of the Zr interlayer. Thickness variation observed in the U-10Mo and Zr interlayer has been attributed to several sources: the initial grain size of the U-10Mo castings, can materials, rolling temperature, inhomogeneous molybdenum content, and porosity in the cast U-10Mo. This thickness variation limits the ability to meet the dimensional specification; thus, a better understanding of the factors causing the nonuniform thickness is needed. In this work, we used a novel, microstructure-based finite element method to model the hot rolling process to address these concerns. Grain microstructures in U-10Mo were tessellated and explicitly considered in the finite element model. Each grain was assigned a random material property to mimic the grain strength variations induced by different grain orientations. Simulations were performed using six steel can thicknesses, four grain sizes, and with or without a Zr interlayer to investigate the influences of those variables on the thickness nonuniformity. The simulation results showed that a thinner steel can and finer U-10Mo grain size reduce thickness variations in both the U-10Mo fuel foil and Zr interlayer. The direct findings from the simulations and analysis can be used to optimize the hot rolling schedule, reduce fabrication defects, and meet the dimensional specifications. The proposed microstructure-based finite element model can be also coupled with experimental microstructure characterization data, images, and models to simulate multi-pass hot rolling.

36 MATERIALS SCIENCE↗

DOE OSTI2022

An efficient method to propagate model uncertainty when inverting seismic data for time domain seismic moment tensors

SUMMARY We present a computationally efficient method to approximately propagate uncertainty when linearly inverting seismic data for point source, time variable moment tensor components. The method is based on the assumption that the data residual, given by the difference between the observed seismic data and the data predicated by a linear inversion, contains the effects of both data and model uncertainty. Our method uses a distribution of data residuals, added directly to the data, in a pseudo-Monte Carlo scheme. Using the assumption that the data residual is a stochastic process, we use the well-known Karhunen–Loève (KL) theorem to construct a distribution of data residuals, where the required basis functions are constructed using Fourier series. The Fourier series are scaled by a product of a random variable and the real-valued spectral amplitudes of the original data residual’s spectrum. Thus, the Fourier series and spectral amplitudes are eigenfunction-eigenvalue pairs used in the KL-based construction of data residual distribution. Using tests with synthetic data, we show that our method compares closely with a Finite Difference Monte Carlo (FDMC) method that we presented previously. More importantly, the method presented here is computationally several orders of magnitude faster than our previous FDMC method, and requires no a priori assumptions of model and/or data uncertainty.

Poppeliers, Christian (ORCID:0000000159526849)↗

DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Highly Oriented Organic Ferroelectric Films with Single-Crystal-Level Properties from Restrained Crystallization

Device applications often require thin film growth with quasi-two-dimensional morphology and crystallization which are not always compatible. In this work, we exploit the method of low-temperature deposition followed by restrained crystallization (LDRC) in the growth of crystalline organic molecular thin films, which tend to grow three-dimensionally with random orientations. We demonstrate that, for 2-methylbenzimidazole (MBI), a molecular ferroelectric that tends to crystalize in spherulites, the quasi-two-dimensional films can be grown using LDRC with highly oriented polar axes and single-crystal-level ferroelectric properties. The crystallization process was shown to occur during the post-deposition annealing process using the in situ electrical measurements. The limited diffusion, low nucleation density, and low activation energy was found critical for the formation of the plate-shaped quasi-two-dimensional films. These results mark an important step in elucidating the LDRC as an effective and general approach for fabricating films with balanced crystallinity and morphology which are critical for applications.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Benchmarking SARS CoV-2 Infection in the Workplace to Support Continuity of Operations

Objective: The COVID-19 pandemic jeopardizes continuity of operations of workplaces and the health and safety of workers. Exemplar workplace-related SARS-CoV-2 benchmarks are described and illustrated with empirical data. Methods: Benchmarks were collected over a 9-month period on a large workplace (N = 5500+). These ranged from quantitative indices associated with RT-qPCR targeted testing and random surveillance screening, surveillance for new variants of SARS-CoV-2, intensive contact tracing, case management, return to work procedures, to monitoring of antibody seropositive status. Results: Data and analyses substantiated effectiveness of interventions. This was evidenced in suppressed infection rates, rapid case identification and isolation, acceptance of the program by employees, documentation of presumptive immunity, and working relationships with senior management. Conclusions: These SARS-CoV-2 exemplar benchmarks provided an evidence-base for practice and contributed strategically to organizational decisions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Stochastic quantum Krylov protocol with double-factorized Hamiltonians

Here we propose a class of randomized quantum Krylov diagonalization (rQKD) algorithms capable of solving the eigenstate estimation problem with modest quantum resource requirements. Compared to previous real-time evolution quantum Krylov subspace methods, our approach expresses the time evolution operator e –i$\widehat{H}$$\tau$ as a linear combination of unitaries and subsequently uses a stochastic sampling procedure to reduce circuit depth requirements. While our methodology applies to any Hamiltonian with fast-forwardable subcomponents, we focus on its application to the explicitly double-factorized electronic-structure Hamiltonian. To demonstrate the potential of the proposed rQKD algorithm on near-term quantum devices, we provide numerical benchmarks for a variety of molecular systems with circuit-based state-vector simulators including the effects of sampling noise, achieving ground-state energy errors of less than 1 kcal mol -1 with circuit depths orders of magnitude shallower than those required for low-rank deterministic Trotter-Suzuki decompositions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Primary fission fragment mass yields across the chart of nuclides

In this work, we have calculated a complete set of primary fission fragment mass yields, Y(A), for heavy nuclei across the chart of nuclides, including those of particular relevance to the rapid neutron capture process (r process) of nucleosynthesis. We assume that the nuclear shape dynamics are strongly damped, which allows for a description of the fission process via Brownian shape motion across nuclear potential-energy surfaces. The macroscopic energy of the potential was obtained with the Finite-Range Liquid-Drop Model (FRLDM), while the microscopic terms were extracted from the single-particle level spectra in the fissioning system by the Strutinsky procedure for the shell energies and the BCS treatment for the pairing energies. For each nucleus considered, the fission fragment mass yield, Y(A), is obtained from 50 000 to 500 000 random walks on the appropriate potential-energy surface. The full mass and charge yield, Y(Z,A), is then calculated by invoking the Wahl systematics. With this method, we have calculated a comprehensive set of fission-fragment yields from over 3800 nuclides bounded by 80 ≤ Z ≤ 130 and A ≤ 330; these yields are provided as an ASCII formatted database in the Supplemental Material. We compare our yields to known data and discuss general trends that emerge in low-energy fission yields across the chart of nuclides.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗