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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The cluster decomposition of the configurational energy of multicomponent alloys

Abstract The cluster expansion method (CEM) is a widely used lattice-based technique in the study of multicomponent alloys. Despite its prevalent use, a clear understanding of expansion terms is lacking. We present a modern mathematical formalism of the CEM and introduce thecluster decomposition—a unique and basis-independent decomposition for functions of the atomic configuration in a crystal. We identify the cluster decomposition as an invariant ANOVA decomposition; and demonstrate how functional analysis of variance and sensitivity analysis can be used to interpret interactions among species. Furthermore, we show how the mathematical structure of the cluster decomposition enables numerical evaluation that scales with the number of clusters and is independent of the number of species. Overall, our work enables rigorous interpretations of interactions among species, provides opportunities to explore parameter estimation beyond linear regression, introduces a numerical efficient implementation, and enables analysis of cluster expansions based on established mathematical and statistical principles.

Chemistry↗
DOE OSTI2025

Induced seismicity and geothermal energy production in the Salton Sea Geothermal Field, California

We analyze the relationship between geothermal energy production and seismic hazards in the Salton Sea Geothermal Field (SSGF) between 1972 and 2022. A clear increase in seismic activity accompanies geothermal energy production and is greatest to the east of the Brawley fault, where the amount of injection exceeds the amount of production. We estimate that, whereas there was a 2% chance of a M6.07 earthquake nucleating inside the SSGF within a fifty-year period prior to energy production (pre-production), there is an equal probability that a M6.70 earthquake nucleates there in a fifty-year period during which historically average energy production conditions persist (syn-production). Similarly, we estimate a 2% chance of a M7.15 and M6.92 occurring in the broader Brawley Seismic Zone (excluding the SSGF) within a fifty-year period during the pre- and syn-production eras, respectively. We find that linear regression models fail to reliably forecast the background seismicity rate as a function of fluid production and injection.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Market optimization and technoeconomic analysis of hydrogen-electricity coproduction systems

Decarbonization efforts across North America, Europe, and beyond rely on variable renewable energy sources such as wind and solar, as well as alternative fuels, such as hydrogen, to support the sustainable energy transition. These advancements have prompted a need for more flexibility in the electric grid to complement non-dispatchable energy sources and increased demand from electrification. Integrated energy systems are well suited to provide this flexibility, but conventional technoeconomic modeling paradigms neglect the time-varying dynamic nature of the grid and thus undervalue resource flexibility. In this work, we develop a computational optimization framework for dynamic market-based technoeconomic comparison of integrated energy systems that coproduce low-carbon electricity and hydrogen (e.g., solid oxide fuel cells, solid oxide electrolysis) against technologies that only produce electricity (e.g., natural gas combined cycle with carbon capture) or only produce hydrogen. Our framework starts with rigorous physics-based process models, built in the open-source Institute for the Design of Advanced Energy Systems (IDAES) modeling and optimization platform, for six energy process concepts. Using these rigorous models and a workflow to optimally design each technology, the framework is shown to be capable of evaluating new and emerging technologies in varying energy markets under a plethora of future scenarios (i.e., renewables penetration, carbon tax, etc.). Ultimately, our framework finds that solid oxide fuel cell-based coproduction systems achieve positive profits for 85% of the analyzed market scenarios. From these market optimization results, we use multivariate linear regression (R 2 values up to 0.99) to determine which electricity price statistics are most significant to predict the optimized annual profit of each system. The proposed framework provides a powerful tool for directly comparing flexible, multi-product energy process concepts to help discern optimal technology and integration options.

08 HYDROGEN↗
DOE OSTI2023

BeyondPlanck: XV. Limits on large-scale polarized anomalous microwave emission from Planck LFI and WMAP

We constrained the level of polarized anomalous microwave emission (AME) on large angular scales using Planck Low-Frequency Instrument (LFI) and WMAP polarization data within a Bayesian cosmic microwave background (CMB) analysis framework. We modeled synchrotron emission with a power-law spectral energy distribution, as well as the sum of AME and thermal dust emission through linear regression with the Planck High-Frequency Instrument (HFI) 353 GHz data. This template-based dust emission model allowed us to constrain the level of polarized AME while making minimal assumptions on its frequency dependence. We neglected CMB fluctuations, but show through simulations that these fluctuations have a minor impact on the results. We find that the resulting AME polarization fraction confidence limit is sensitive to the polarized synchrotron spectral index prior. In addition, for prior means β s < -3.1 we find an upper limit of p AME max ≲ 0.6% (95% confidence). In contrast, for means β s = -3.0, we find a nominal detection of p AME = 2.5 ± 1.0% (95% confidence). These data are thus not strong enough to simultaneously and robustly constrain both polarized synchrotron emission and AME, and our main result is therefore a constraint on the AME polarization fraction explicitly as a function of β s . Combining the current Planck and WMAP observations with measurements from high-sensitivity low-frequency experiments such as C-BASS and QUIJOTE will be critical to improve these limits further.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Near-infrared chemical abundances of stars in the Sculptor dwarf galaxy

Owing to the recent identification of major substructures in our Milky Way (MW), the astronomical community has started to reevaluate the importance of dissolved and existing dwarf galaxies. In this work, we investigate up to 13 elements in 43 giant stars of the Sculptor dwarf galaxy (Scl) using high-signal-to-noise-ratio near-infrared (NIR) APOGEE spectra. Thanks to the strong feature lines in the NIR, we were able to determine high-resolution O, Si, and Al abundances for a large group of sample stars for the first time in Scl. By comparing the [α/Fe] (i.e., O, Mg, Si, Ca, and Ti) of the stars in Scl, Sagittarius, and the MW, we confirm the general trend that less massive galaxies tend to show lower [α/Fe]. The low [Al/Fe] (~ - 0.5) in Scl demonstrates the value of this ratio as a discriminator with which to identify stars born in dwarf galaxies (from MW field stars). A chemical-evolution model suggests that Scl has a top-light initial mass function (IMF), with a high-mass IMF power index of ~ - 2.7, and a minimum Type Ia supernovae delay time of ~100 Myr. Furthermore, a linear regression analysis indicates a negative radial metallicity gradient and positive radial gradients for [Mg/Fe] and [Ca/Fe], in qualitative agreement with the outside-in formation scenario.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Population-Level Summary of Health Care Utilization for the Management of Patellar Tendinopathy in the Military Health System

Abstract Patellar tendinopathy (PT) is a common nontraumatic orthopaedic disorder of the knee suffered by many service members. Understanding the make-up of usual care for PT at the system level can better frame current clinical gaps and areas that need improvement. Exercise therapy is recommended as a core treatment for PT, but it is unclear how often it is used as a part of usual care for PT within the Military Health System (MHS). The purpose of the study was to identify interventions used in the management of PT and the timing of these interventions. A secondary purpose was to determine if exercise therapy use was associated with reduced recurrence of knee pain. In total, 4,719 individuals aged 17 to 50 years in the MHS diagnosed with PT between 2010 and 2011 were included. Pharmacological and nonpharmacological interventions, visits to specialty providers, and imaging services were captured. Descriptive statistics were used to report the findings. Interventions were further categorized as being part of initial care (within the first 7 days), the initial episode of care (within the first 60 days), or the 2-year time period after diagnosis. Linear regression assessed the relationship between the number of exercise therapy visits in the initial episode of care and recurrences of knee pain. In total, 50.6% of this cohort had no more than one medical visit total for PT. Exercise therapy (18.2%) and nonsteroidal anti-inflammatory drugs (4.3%) were the two most used interventions in the initial episode of care. Radiographs were ordered for 23.1% of the cohort in the initial episode of care. The number of exercise therapy visits a patient received during the initial episode of care was not associated with recurrences of knee pain. Half of the individuals received no further care beyond an initial visit for the diagnosis of PT. Exercise therapy was the most common intervention used during the initial episode of care, but exercise therapy did not influence the recurrence of knee pain.

Orthopedics↗
DOE OSTI2020

Machine learning approaches for structural and thermodynamic properties of a Lennard-Jones fluid

Predicting the functional properties of many molecular systems relies on understanding how atomistic interactions give rise to macroscale observables. However, current attempts to develop predictive models for the structural and thermodynamic properties of condensed-phase systems often rely on extensive parameter fitting to empirically selected functional forms whose effectiveness is limited to a narrow range of physical conditions. Here, we illustrate how these traditional fitting paradigms can be superseded using machine learning. Specifically, we use the results of molecular dynamics simulations to train machine learning protocols that are able to produce the radial distribution function, pressure, and internal energy of a Lennard-Jones fluid with increased accuracy in comparison to previous theoretical methods. The radial distribution function is determined using a variant of the segmented linear regression with the multivariate function decomposition approach developed by Craven et al. [J. Phys. Chem. Lett. 11, 4372 (2020)]. The pressure and internal energy are determined using expressions containing the learned radial distribution function and also a kernel ridge regression process that is trained directly on thermodynamic properties measured in simulation. The presented results suggest that the structural and thermodynamic properties of fluids may be determined more accurately through machine learning than through human-guided functional forms.

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

Accurate parameterization of the kinetic energy functional

The absence of a reliable formulation of kinetic energy density functional has hindered the development of orbital free density functional theory. Using the data-aided learning paradigm, we propose a simple prescription to accurately model the kinetic energy density of any system. Our method relies on a dictionary of functional forms for local and nonlocal contributions which have been proposed in the literature and the appropriate coefficients are calculated via a linear regression framework. To model the nonlocal contributions, we explore two new nonlocal functionals - a functional that captures fluctuations in electronic density and a functional that incorporates gradient information. Since, the analytical functional forms of the kernels present in these nonlocal terms are not known from theory, we propose a basis function expansion to model these seemingly difficult nonlocal quantities. This allows us to easily reconstruct kernels for any system using only a few structures. The proposed method is able to learn kinetic energy densities and total kinetic energies of molecular and periodic systems, such as H 2 , LiH, LiF and a one-dimensional chain of 8 hydrogens using data from Kohn-Sham density functional theory calculations for only a few structures. For the ease of reproduction, codes used to generate the models are provided in the supporting materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Emerging opportunities for hybrid perovskite solar cells using machine learning

While there are several bottlenecks in hybrid organic–inorganic perovskite (HOIP) solar cell production steps, including composition screening, fabrication, material stability, and device performance, machine learning approaches have begun to tackle each of these issues in recent years. Different algorithms have successfully been adopted to solve the unique problems at each step of HOIP development. Specifically, high-throughput experimentation produces vast amount of training data required to effectively implement machine learning methods. Here, we present an overview of machine learning models, including linear regression, neural networks, deep learning, and statistical forecasting. Experimental examples from the literature, where machine learning is applied to HOIP composition screening, thin film fabrication, thin film characterization, and full device testing, are discussed. These paradigms give insights into the future of HOIP solar cell research. As databases expand and computational power improves, increasingly accurate predictions of the HOIP behavior are becoming possible.

Hering, Abigail R. (ORCID:0000000270806953)↗
DOE OSTI2024

On-the-fly machine learned force fields for the study of warm dense matter: Application to diffusion and viscosity of CH

We develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme based on the kernel method and Bayesian linear regression, with the training data generated from the Kohn–Sham density functional theory (DFT) using the Gauss spectral quadrature method, within which we calculate energies, atomic forces, and stresses. We verify the accuracy of the formalism by comparing the predicted properties of warm dense carbon with recent Kohn–Sham DFT results in the literature. In so doing, we demonstrate that ab initio MD simulations of WDM can be accelerated by up to three orders of magnitude, while retaining ab initio accuracy. We apply this framework to calculate the diffusion coefficients and shear viscosity of CH at a density of 1 g/cm3 and temperatures in the range of 75 000–750 000 K. We find that the self- and inter-diffusion coefficients and the viscosity obey a power law with temperature, and that the diffusion coefficient results suggest a weak coupling between C and H in CH. In addition, we find agreement within standard deviation with previous results for C and CH but disagreement for H, demonstrating the need for ab initio calculations as presented here.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Mining experimental magnetized liner inertial fusion data: Trends in stagnation morphology

In magnetized liner inertial fusion (MagLIF), a cylindrical liner filled with fusion fuel is imploded with the goal of producing a one-dimensional plasma column at thermonuclear conditions. However, structures attributed to three-dimensional effects are observed in self-emission x-ray images. Despite this, the impact of many experimental inputs on the column morphology has not been characterized. We demonstrate the use of a linear regression analysis to explore correlations between morphology and a wide variety of experimental inputs across 57 MagLIF experiments. Results indicate the possibility of several unexplored effects. For example, we demonstrate that increasing the initial magnetic field correlates with improved stability. Although intuitively expected, this has never been quantitatively assessed in integrated MagLIF experiments. We also demonstrate that azimuthal drive asymmetries resulting from the geometry of the “current return can” appear to measurably impact the morphology. In conjunction with several counterintuitive null results, we expect the observed correlations will encourage further experimental, theoretical, and simulation-based studies. Finally, we note that the method used in this work is general and may be applied to explore not only correlations between input conditions and morphology but also with other experimentally measured quantities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Enhancing fire emissions inventories for acute health effects studies: integrating high spatial and temporal resolution data

Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies. This paper provides a method for improving an FEI dataset with readily available information to create a more robust dataset with daily fire progression. High temporal and spatial resolution burned area information from two FEI products are combined into a single dataset, and a linear regression model fills gaps in daily fire progression. The combined dataset provides up to 71% more PM 2.5 emissions, 69% more burned area, and 367% more fire days per year than using a single source of burned area information. The FEI combination method results in improved FEI information with no gaps in daily fire emissions estimates. The combined dataset provides a functional improvement to FEI data that can be achieved with currently available data.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Data-driven electrolyte design for lithium metal anodes

Improving Coulombic efficiency (CE) is key to the adoption of high energy density lithium metal batteries. Liquid electrolyte engineering has emerged as a promising strategy for improving the CE of lithium metal batteries, but its complexity renders the performance prediction and design of electrolytes challenging. Here, we develop machine learning (ML) models that assist and accelerate the design of high-performance electrolytes. Using the elemental composition of electrolytes as the features of our models, we apply linear regression, random forest, and bagging models to identify the critical features for predicting CE. Our models reveal that a reduction in the solvent oxygen content is critical for superior CE. We use the ML models to design electrolyte formulations with fluorine-free solvents that achieve a high CE of 99.70%. This work highlights the promise of data-driven approaches that can accelerate the design of high-performance electrolytes for lithium metal batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Spectra and characteristics of somatic mutations induced by ionizing radiation in hematopoietic stem cells

Spectra and frequencies of spontaneous and X-ray-induced somatic mutations were revealed with mouse long-term hematopoietic stem cells (LT-HSCs) by whole-genome sequencing of clonal cell populations propagated in vitro from single isolated LT-HSCs. SNVs and small indels were the most common types of somatic mutations, and increased up to twofold to threefold by whole-body X-irradiation. Base substitution patterns in the SNVs suggested a role of reactive oxygen species in radiation mutagenesis, and signature analysis of single base substitutions (SBS) revealed a dose-dependent increase of SBS40. Most of spontaneous small deletions were shrinkage of tandem repeats, and X-irradiation specifically induced small deletions out of tandem repeats (non-repeat deletions). Presence of microhomology sequences in non-repeat deletions suggested involvement of microhomology mediated end-joining repair mechanisms as well as nonhomologous end-joining in radiation-induced DNA damages. We also identified multisite mutations and structural variants (SV), i.e., large indels, inversions, reciprocal translocations, and complex variants. The radiation-specificity of each mutation type was evaluated from the spontaneous mutation rate and the per-Gy mutation rate estimated by linear regression, and was highest with non-repeat deletions without microhomology, followed by those with microhomology, SV except retroelement insertions, and multisite mutations; these types were thus revealed as mutational signatures of ionizing radiation. Further analysis of somatic mutations in multiple LT-HSCs indicated that large fractions of postirradiation LT-HSCs originated from single LT-HSCs that survived the irradiation and then expanded in vivo to confer marked clonality to the entire hematopoietic system, with varying clonal expansion and dynamics depending on radiation dose and fractionation.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

A Better Method to Calculate Fuel Burnup in Pebble Bed Reactors Using Machine Learning

Burnup measurement is an important step in material control and accountancy (MC&A) at nuclear reactors, and may be done by examining gamma spectra of fuel samples. Traditional approaches rely on known correlations to specific photopeaks (e.g. 137 Cs) and operate via a standard linear regression method. However, the quality of these regression methods is limited even in the best case, and is significantly poorer at short fuel cool-down times, due to the elevated radiation background by short life-time isotopes, and self-shielding effect of the fuel. For practical operation of pebble bed reactors (PBRs), quick measurements (in minutes) and short cooling times (in hours) are required from a safety and security perspective. We investigated the efficacy and performance of machine learning (ML) methods to predict the burnup of the pebble fuel from full gamma spectra (rather than specific discrete photopeaks) and found a full-spectrum ML approach to far outperform baseline regression predictions in all measurement and cooling conditions - including in operational-like measurement conditions. We also performed model and data ablation experiments to determine the relative performance impact of our ML methods' capacity to model data nonlinearities and the inherent additional information in full spectra. Applying our ML methods, we found a number of surprising results, including improved accuracy at shorter fuel cooling times (the opposite of the norm), remarkable robustness to spectrum compression (via rebinning), and competitive burnup predictions even when using background signal only (i.e. explicitly omitting known isotope photopeaks).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Detection of Isotopes in Urban Source Search Low-Count Gamma Spectra Using Hopfield Neural Networks

Source search campaigns involve measurements of background gamma-ray spectra with a mobile detector-spectrometer traveling along arbitrarily chosen trajectories over a wide screening area. Radiation counts are typically measured with a tellurium-doped sodium iodide [NaI(Tl)] scintillator detector-spectrometer in short acquisition intervals, usually 1 s. The objective is to detect orphan isotopes with half-lives shorter than those of the isotopes in the natural background. In principle, radioisotopes can be identified by their unique gamma emission spectrum. However, detecting orphan isotopes in search data is challenging because low counts measured in short acquisition intervals result in incomplete spectral lines. In this study, we investigate the performance of a Hopfield neural network (HNN) that implements an auto-associative memory for the detection of isotopes of interest in an urban search campaign. The HNN is trained on one example of gamma spectra with well-resolved spectral lines of each isotope of interest. During testing, the auto-associative memory implementation of the HNN processes low-count gamma spectra with partially complete isotopic lines by matching incoming measurements to the closest one of its memory-stored patterns. The testing database consisted of almost 10 000 1-s gamma spectra, including measurements of orphan isotopes 137 Cs, 241 Am, and 131 I, obtained during two urban search surveys with a NaI(Tl) detector. The performance of the HNN detection algorithm was evaluated using precision, recall, and F1 scores, and benchmarked with a multiple linear regression (MLR) identification algorithm. In conclusion, the test results demonstrate that HNN outperforms MLR in the detection of all the isotopes of interest.

Auto associative memory↗
DOE OSTI2025

Welch Method and Bootstrapping Applied to Subcritical Gamma Noise

We measured the prompt neutron decay constant 𝛼 of the CROCUS zero-power reactor at the Swiss Federal Institute of Technology Lausanne using cross-power spectral density (CPSD) analysis of gamma-gamma correlations from two trans-stilbene organic scintillators positioned near the reactor core. We measured critical and subcritical states, with water levels ranging from 960 mm (critical) to 800 mm (𝜌=−1.4 $ subcritical). Our analysis used the Welch method, dividing signal segments for fast Fourier transform (FFT) frequency analysis and applying bootstrapping uncertainty quantification that uses Welch-defined segments. Results demonstrated a clear increase in the measured 𝛼 as reactor reactivity decreased, distinguishing critical from subcritical conditions. At the 960-mm critical level, 𝛼 was estimated at 155.9 ± 0.7 s −1 , and for the 800-mm subcritical level, 𝛼 increased significantly to 367.3 ± 6.9 s –1 . A linear regression of subcritical states yielded a critical estimate of 154.0 ± 3.1 s –1 , aligning with the static 𝛼 estimate at critical. The bootstrapping method produced normally distributed 𝛼 estimates, confirming data consistency. The gamma CPSD 𝛼 estimates clearly distinguish reactor states and improve monitoring of zero-power reactors. The future deployment of modular and microreactors as potential candidates for noise analysis is demonstrated in CROCUS, particularly zero-power mock-ups of new designs. The improvement of noise analysis in the subcritical domain from this work will support experimental data for reactor deployment and procedure.

CROCUS↗
DOE OSTI2020

Quantifying CTFE content in FK-800 using ATR-FTIR and time to peak crystallization

The chlorotrifluoroethylene (CTFE) content in vinylidene fluoride (VDF)-CTFE copolymers greatly influences the chemical and physical properties and ultimately determines the application. The copolymer known as FK-800 has a VDF:CTFE ratio of ~1:3 and is used almost exclusively as the binder in the insensitive high explosive (HE) formulation PBX 9502. Due to the dangerous nature of HE work and the effect CTFE content variations have, the ability to quickly and easily quantify CTFE content is of interest. Two techniques, attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and time to peak crystallization measured by heat flow calorimetry, were evaluated here as potential means for quantifying CTFE content. Based on the certificate of analyses (COAs), FK-800 lots with varied CTFE content encompassing the range historically used in HE applications were used as standards for both methodologies. For FTIR, a linear regression was performed on the peak area of the C-Cl IR stretch and the CTFE content; the methodology was then demonstrated on four samples, two lots of unadulterated material and material recovered from two HE samples. The calculated CTFE content and COAs were in good agreement for all four samples. Heat flow calorimetry revealed the relationship between time to peak crystallization and CTFE content was best fit by a power regression; the methodology was then tested on two different FK-800 lots. Only one sample showed good agreement between the calculated CTFE content and the COA. This discrepancy indicates the method developed is not suitable for quantifying CTFE, but provides valuable insight regarding the crystallization behavior of VDF-CTFE copolymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗