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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Revisiting Experimental Signatures of the Ponderomotive Force

The classical theory of single-electron dynamics in focused laser pulses is the foundation of both the relativistic ponderomotive force (RPF), which underlies models of laser-collective-plasma dynamics, and the discovery of novel strong-field radiation dynamics. Despite this bedrock importance, consensus eludes the community as to whether acceleration of single electrons in vacuum has been observed in experimental conditions. We analyze an early experiment on the RPF with respect to several features that were neglected in modeling and that can restore consistency between theory predictions and experimental data. The right or wrong pulse profile function, laser parameters, or initial electron distribution can each make or break the agreement between predictions and data. The laser phase at which the electron’s interaction with the pulse begins has a large effect, explaining why much larger energies are achieved by electrons liberated in the focal region by photoionization from high-Z atoms and by electrons ejected from a plasma mirror. Finally, we compute the difference in a typical electron spectrum arising from fluctuating focal spot size in state-of-the-art ultra-relativistic laser facilities. Our results emphasize the importance of thoroughly characterizing laser parameters in order to achieve quantitatively accurate predictions and the precision required for discovery science.

Hegelich, Bjorn Manuel↗
DOE OSTI2020

Thermal Model Heat Rate Predictions of the AGR-5/6/7 Experiment

Several fuel and material irradiation experiments have been planned for the U.S. Department of Energy Advanced Gas Reactor Fuel Development and Qualification Program, which supports the development and qualification of tristructural isotropic (TRISO) coated particle fuel for use in high-temperature gas-cooled reactors. The goals of these experiments are to provide irradiation performance data to support fuel process development, qualify fuel for normal operating conditions, support development of fuel performance models and codes, and provide irradiated fuel and materials for post-irradiation examination and safety testing. Originally planned and named as separate fuel experiments, but subsequently combined into a single test train. AGR-5/6/7 will test low-enriched uranium oxycarbide (UCO – a heterogeneous mixture of uranium oxide and uranium carbide) TRISO fuel. The AGR-5/6 portion of the experiment will provide data to support qualification of the selected reference fuel design, while the AGR-7 portion will serve as a margin test, irradiating the fuel beyond normal operating conditions. During irradiation, the temperature in each capsule is controlled by varying the helium / neon gas mixture flowing through the capsule until the desired thermocouple temperature is reached. Further adjustments can be made by changing the lobe power and the neutron filter. Three neutron filters are available to use as the experiment burns up the fissionable fuel. A thermal finite element model has been created for the five capsules comprising the AGR-5/6/7 experiment. Heat rates from a physics analysis are imported into the model along with fast neutron fluence. Graphite shrinkage due to the fast neutron fluence is incorporated into the model. Gas gaps change as a function of fast neutron fluence. Radiation heat transfer is a major contributor in this model. This is a large model with more than 1 million finite element brick elements. More than 150 different parts are modeled in the finite element model and communicate with each other from a heat transfer sense. More than 50 thermocouples are used in the experiment and are compared to actual measurements. The experiment is composed of five separate stainless steel capsules all welded together. This paper presents a method used to predict the gas mixture in the event that all of the thermocouples fail in a particular capsule. Curve fitting of heat rates from previous cycles and projecting them into the future is the basis of the method.

36 MATERIALS SCIENCE↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2024

He bubble-induced phase transformation of W grain boundaries revealed by accelerated molecular dynamics

The growth of He bubbles and the resulting impact on the microstructural evolution of W are of paramount importance to the plasma-facing materials community due to the application of W in Tokamak fusion reactors. Using accelerated molecular dynamics (AMD) techniques, we outline the structural evolution of grain boundaries (GBs) caused by growing He bubbles. It is discovered that when an alternative, low energy, high density GB structure or phase is available, He bubbles can induce a progressive phase transformation of the GB to the higher density phase by the continual nucleation of W Frenkel pairs. Here, we find that the resulting W self-interstitials migrate to sites at the GB which are structurally related to the higher density phase, leading to the transformation. We discuss the implications of this progressive microstructural evolution on the growing He bubble and consider in general how He bubbles will impact the structural evolution of an arbitrary W GB. These findings of GB phase transformation are predicted to impact other damage events in W such as recrystallization, GB migration and defect segregation which must take these findings into account in order to accurately simulate a realistic W microstructure and hence extract experimentally meaningful data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods

Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocation-density-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Integrating multimodal data through interpretable heterogeneous ensembles

Motivation: Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results: We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Women in Data Science Livermore Datathon 2024

The WiDS Datathon 2024 focuses on a prediction task using a roughly 39k record dataset (split into training and test sets) representing patients and their characteristics (age, race, BMI, zip code), their diagnosis and treatment information (breast cancer diagnosis code, metastatic cancer diagnosis code, metastatic cancer treatments, … etc.), their geo (zip-code level) demographic data (income, education, rent, race, poverty, …etc), as well as toxic air quality data (Ozone, PM25 and NO2) that tie health outcomes to environmental conditions. Each row in the data corresponds a single patient and her Diagnosis Period.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Climate change-resilient snowpack estimation in the Western United States

Abstract In the 21st century, warmer temperatures and changing atmospheric circulation will likely produce unprecedented changes in Western United States snowfall 1–3 , with impacts on the timing, amount, and spatial patterns of snowpack 4–7 . The ~900 snow pillow stations are indispensable to water resource management by measuring snow-water equivalent (SWE) 8,9 in strategic but fixed locations 10,11 . However, this network may not be impacted by climate change in the same way as the surrounding area 12 and thus fail to accurately represent unmeasured locations; climate change thereby threatens our ability to measure the effects of climate change on snow. In this work, we show that maintaining the current peak SWE estimation skill is nonetheless possible. We find that explicitly including spatial correlations—either from gridded observations or learned by the model—improves skill at predicting distributed snowpack from sparse observations by 184%. Existing artificial intelligence methods can be useful tools to harness the many available sources of snowpack information to estimate snowpack in a nonstationary climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluating skill in predicting the Interdecadal Pacific Oscillation in initialized decadal climate prediction hindcasts in E3SMv1 and CESM1 using two different initialization methods and a small set of start years

Abstract It is a daunting challenge to conduct initialized hindcasts with enough ensemble members and associated start years to form a drifted climatology from which to compute the anomalies necessary to quantify the skill of the hindcasts when compared to observations. This limits the ability to experiment with case studies and other applications where only a few initial years are needed. Here we run a set of hindcasts with CESM1 and E3SMv1 using two different initialization methods for a limited set of start years and use the respective uninitialized free-running historical simulations to form the model climatologies. Since the drifts from the observed initial states in the hindcasts toward the uninitialized model state are large and rapid, after a few years the drifted initialized models approach the uninitialized model climatological errors. Therefore, hindcasts from the limited start years can use the uninitialized climatology to represent the drifted model states after about lead year 3, providing a means to compute forecast anomalies in the absence of a large hindcast sample. There is comparable skill for predicting spatial patterns of multi-year Pacific sea surface temperature anomalies in the domain of the Interdecadal Pacific Oscillation using this method compared to the conventional methodology with a large hindcast data set, though there is a model dependence to the drifts in the two initialization methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Predicting material microstructure evolution via data-driven machine learning

The recent rapid rise in data-driven practices in the materials science domain has led to the development of efficient, generalizable, and accurate approaches for several applications, including: material property prediction, mining (micro)structure- property and (micro)structure-processing relationships, and characterization of material microstructures. Central to the materials science domain is linking microstructure to properties and performance, and critical to building such linkages is understanding how microstructures evolve as a function of environmental exposure or processing conditions (e.g., time, temperature, applied stress or strain, irradiation). This preview article summarizes recent work on the development of recurrent neural networks for predicting spatio-temporal microstructure evolution detailed by Yang et al in their manuscript entitled 'Self-supervised Learning and Prediction of Microstructure Evolution with Recurrent Neural Networks'.

machine learning (ML), materials characterization↗
DOE OSTI2021

Information Entropy as Quantifier of Potential Predictability in the Tropical Indo-Pacific Basin

Global warming is posed to modify the modes of variability that control much of the climate predictability at seasonal to interannual scales. The quantification of changes in climate predictability over any given amount of time, however, remains challenging. Here we build upon recent advances in non-linear dynamical systems theory and introduce the climate community to an information entropy quantifier based on recurrence. The entropy, or complexity of a system is associated with microstates that recur over time in the time-series that define the system, and therefore to its predictability potential. A computationally fast method to evaluate the entropy is applied to the investigation of the information entropy of sea surface temperature in the tropical Pacific and Indian Oceans, focusing on boreal fall. In this season the predictability of the basins is controlled by two regularly varying non-linear oscillations, the El Niño-Southern Oscillation and the Indian Ocean Dipole. We compute and compare the entropy in simulations from the CMIP5 catalog from the historical period and RCP8.5 scenario, and in reanalysis datasets. Discrepancies are found between the models and the reanalysis, and no robust changes in predictability can be identified in future projections. The Indian Ocean and the equatorial Pacific emerge as troublesome areas where the modeled entropy differs the most from that of the reanalysis in many models. A brief investigation of the source of the bias points to a poor representation of the ocean mean state and basins' connectivity at the Indonesian Throughflow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Nanocrystal programmable assembly beyond hard spheres (or shapes) and other (simple) potentials

Ligands are the key to almost any strategy in the assembly of programmable nanocrystals (or nanoparticles) and must be accurately considered in any predictive model. Hard Spheres (or Shapes) provide the simplest and yet quite successful approach to assembly, with remarkable sophisticated predictions verified in experiments. There are, however, many situations where hard spheres/shapes predictions fail. This prompts three important questions: In what situations should hard spheres/shapes models be expected to work? and when they do not work, Is there a general model that successfully corrects hard sphere/shape predictions? and given other successful models where ligands are included explicitly, and of course, numerical simulations, can we unify hard sphere/shape models, explicit ligand models and all atom simulations? . Further, the Orbifold Topological Model (OTM) provides a positive answer to these three questions. In this paper, I give a detailed review of OTM, describing the concept of ligand vortices and how it leads to spontaneous valence and nanoparticle “eigenshapes” while providing a prediction of the lattice structure, without fitting parameters, which accounts for many body effects not captured by (two-body) potentials. I present a thorough survey of experiments and simulations and show that, to this date, they are in full agreement with the OTM predictions. I conclude with a discussion on whether NC superlattices are equilibrium structures and some significant challenges in structure prediction.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exclusive four pion photoproduction in ultraperipheral Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV

The intense photon fluxes from relativistic nuclei provide an opportunity to study photonuclear interactions in ultraperipheral collisions. The measurement of coherently photoproduced π + π − π + π − final states in ultraperipheral Pb–Pb collisions at √s NN = 5.02 TeV is presented for the first time. The cross section, dσ/dy, times the branching ratio (ρ →π + π + π − π − ) is found to be 47.8±2.3 (stat.)± 7.7 (syst.) mb in the rapidity interval |y| < 0.5. The invariant mass distribution is not well described with a single Breit-Wigner resonance. The production of two interfering resonances, ρ(1450) and ρ(1700), provides a good description of the data. The values of the masses (m) and widths (Γ) of the resonances extracted from the fit are m 1 = 1385±14 (stat.)±3 (syst.) MeV/c 2 , Γ 1 = 431±36 (stat.)±82 (syst.) MeV/c 2 , m 2 = 1663±13 (stat.)±22 (syst.) MeV/c 2 and Γ 2 = 357 ± 31 (stat.)±49 (syst.) MeV/c 2 , respectively. The measured cross sections times the branching ratios are compared to recent theoretical predictions.

FOS: Physical sciences↗
DOE OSTI2024

High-precision measurement of the W boson mass with the CMS experiment at the LHC

In the standard model of particle physics, the masses of the carriers of the weak interaction, the W and Z bosons, are uniquely related. Physics beyond the standard model could change this relationship through the effects of quantum loops of virtual particles, thus making it of great importance to measure these masses with the highest possible precision. Although the mass of the Z boson is known to the remarkable precision of 22 parts per million (2.0 MeV), the W boson mass is known much less precisely, given the difficulty of the measurement. A global fit to electroweak data, used to predict the W boson mass in the standard model, yields an uncertainty of 6 MeV. Reaching a comparable experimental precision would be a sensitive and fundamental test of the standard model. Furthermore, a precision measurement of the W boson mass performed by the CDF Collaboration at the Fermilab Tevatron has challenged the standard model by significantly disagreeing with the prediction of the global electroweak fit and the average of other $m_\mathrm{W}$ measurements. We report the first W boson mass measurement by the CMS Collaboration at the CERN LHC, based on a data sample collected in 2016 at the proton-proton collision energy of 13 TeV. The W boson mass is measured using a large sample of W$\toμν$ events via a highly granular binned maximum likelihood fit to the kinematic properties of the muons produced in the W$^{+}$ and W$^{-}$ boson decays. The significant in situ constraints of theoretical inputs and their corresponding uncertainties, together with an accurate determination of the experimental effects, lead to a precise W boson mass measurement, $m_\mathrm{W} =$ 80$\,$360.2 $\pm$ 9.9 MeV, in agreement with the standard model prediction.

FOS: Physical sciences↗
DOE OSTI2022

Strong parallel evidence of selection during switchgrass sward establishment in hybrid and lowland ecotypes

Switchgrass sward establishment results in up to 90% seedling mortality. The degree of selection during sward establishment has not been reported using modern genetic methods. Pooled leaf samples were sequenced from replicated swards of 46 half-sib families from two breeding groups (lowland and hybrid) before and through 3 years of stand establishment. Pooled allele frequencies were then assessed using fixation indices (Fst) and an independent data set was used to predict the polygenic impact of establishment selection on two traits (heading date and winter survivorship). Last, the DNA pools were assigned survival rankings to predict the sward survival genomically estimated breeding values within the training data set. Strong and parallel selection occured in both breeding groups. Five genomic regions exceeded the significant threshold of 99.9% in >10 families, indicating consistent selection across families and breeding groups. Polygenic trait predictions determined that establishment selection was partially associated with winter survivorship but resulted in variable heading date alterations. The genomewide variation is consistent with selection for a small number of related parental lines. This study observed strong selection for a small number of hybrid and coastal ecotype individuals which are promising germplasm sources for improved sward survival. This confirms prior reports of sward selection during grassland establishment and highlights the strength of pooled DNA sequencing for survival traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

EAM-HLR: Enhancing the low-resolution E3SM Atmosphere Model with an ML model of high-low-resolution residual in convective processes

Focal Area(s). (2) Augment the low-resolution (LR) E3SM Atmosphere Model (EAM) with a machine learning (ML) column model of the residual in convective processes with respect to high-resolution (HR) and ultra-HR (UHR) simulation and observational data. Science Challenge. Substantially improve LR EAM climatology to increase multidecadal water cycle (WC) predictability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The effects of bias, drift, and trends in calculating anomalies for evaluating skill of seasonal-to-decadal initialized climate predictions

In initialized seasonal to decadal (S2D) predictions, model hindcasts rapidly drift away from the initial observed state and converge toward a preferred state characterized by systematic error, or bias. Bias and drift are among the greatest challenges facing initialized prediction today. Differences in trends between initial states and drifted states, combined with bias and drift, introduce complexities in calculating anomalies to assess skill of initialized predictions. We examine several methods of calculating anomalies using the Decadal Prediction Large Ensemble (DPLE) using the Community Earth System Model (CESM) initialized hindcasts and focus on Pacific and Atlantic SSTs to illustrate issues with anomaly calculations. Three methods of computing anomalies, one as differences from a long term model climatology, another as bias-adjusted differences from the previous 15 year average from observations, and a third as differences from the previous 15 year average from the model, are contrasted and each is shown to have limitations. For the first, trends in bias and drift introduce higher skill estimates earlier and later in the hindcast period due to the trends that contribute to skill. For the second, higher skill can be introduced in situations where low frequency variability in the observations is large compared to the hindcasts on timescales greater than 15 years, while lower skill can result if the predicted signal is small and the bias-correction itself produces a transition of SST anomalies to the opposite sign of those that are observed. The third method has somewhat lower skill compared to each of the others, but has less difficulties with not only the long term trends in the model climatology, but also with the unrealistic situational skill from using observations as a reference. However, the first 15 years of the hindcast period cannot be evaluated due to having to wait to accumulate the previous 15 year model climatology before the method can be applied. Here, the IPO transition in the 2014–2016 time frame from negative to positive (predicted by Meehl et al. in in Nat Commun, 10.1038/NCOMMS11718, 2016) did indeed verify using all three methods, though each provides somewhat different skill values as a result of the respective limitations. There is no clear best method, as all are roughly comparable, and each has its own set of limitations and caveats. However, all three methods show generally higher overall skill in the AMO region compared to the IPO region.

54 ENVIRONMENTAL SCIENCES↗