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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Predicting strength distributions of MEMS structures using flaw size and spatial density

The populations of flaws in individual layers of microelectromechanical systems (MEMS) structures are determined and verified using a combination of specialized specimen geometry, recent probabilistic analysis, and topographic mapping. Strength distributions of notched and tensile bar specimens are analyzed assuming a single flaw population set by fabrication and common to both specimen geometries. Both the average spatial density of flaws and the flaw size distribution are determined and used to generate quantitative visualizations of specimens. Scanning probe-based topographic measurements are used to verify the flaw spacings determined from strength tests and support the idea that grain boundary grooves on sidewalls control MEMS failure. The findings here suggest that strength controlling features in MEMS devices increase in separation, i.e., become less spatially dense, and decrease in size, i.e., become less potent flaws, as processing proceeds up through the layer stack. The method demonstrated for flaw population determination is directly applicable to strength prediction for MEMS reliability and design.

Instruments & Instrumentation↗
DOE OSTI2023

Symmetric carbon tetramers forming spin qubits in hexagonal boron nitride

Abstract Point defect quantum bits in semiconductors have the potential to revolutionize sensing at atomic scales. Currently, vacancy-related defects are at the forefront of high spatial resolution and low-dimensional sensing. On the other hand, it is expected that impurity-related defect structures may give rise to new features that could further advance quantum sensing in low dimensions. Here, we study the symmetric carbon tetramer clusters in hexagonal boron nitride and propose them as spin qubits for sensing. We utilize periodic-DFT and quantum chemistry approaches to reliably and accurately predict the electronic, optical, and spin properties of the studied defect. We show that the nitrogen-centered symmetric carbon tetramer gives rise to spin state-dependent optical signals with strain-sensitive intersystem crossing rates. Furthermore, the weak hyperfine coupling of the defect to their spin environments results in a reduced electron spin resonance linewidth that can enhance sensitivity.

Chemistry↗
DOE OSTI2015

Extreme stress gradient effects on microstructural fatigue crack propagation rates in Ni microbeams

The fatigue crack propagation behavior of microstructurally small cracks growing under extreme stress gradients was investigated in Ni microbeams under fully reversed cyclic loading. A technique to calculate the crack growth rates in microbeams with two different normalized stress gradients (17% and 50% μm{sup −1}) is developed and validated. Decreasing crack propagation rates are observed over the first 2 μm, and the rates are more than 1 order of magnitude slower for the devices with 50% μm{sup −1} stress gradients. This fundamental knowledge is critical to predict the fatigue reliability of advanced metallic microcomponents under bending such as in microelectromechanical systems or flexible/stretchable electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Explaining System-Level Prognostics with Established Machine Learning Methods

System-level prognostics is crucial for ensuring reliability and enabling predictive maintenance in complex systems with interconnected components. This study presents a framework that integrates data-driven methods to predict the remaining useful life (RUL) of a subsystem under multiple and concurrent faults within a nuclear power plant system with explainable artificial intelligence (XAI). A nuclear power plant (NPP) operation was simulated to model the degradation behavior of NPP components, and four machine learning models—Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory (LSTM)—were evaluated for prognostics with a novel system RUL parameter. The LSTM model demonstrated potential superior repeatability, while SHAP (SHapley Additive exPlanations) for explainability provided consistent and trustworthy global explanations. In contrast, LIME (Local Interpretable Model-agnostic Explanations) offered localized interpretability but showed reduced stability for sequential data. Key findings include the interplay between component-level degradation and system-wide performance, with LSTM effectively capturing these dynamics through sequence-level predictions. The XAI techniques enhanced transparency by identifying critical features influencing model predictions and aligning with domain knowledge. Furthermore, this framework has significant implications for improving trust and understanding in predictive maintenance, particularly in safety-critical industries like nuclear energy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Femtosecond pulse parameter estimation from photoelectron momenta using machine learning

Abstract Deep learning models have provided huge interpretation power for image-like data. Specifically, convolutional neural networks (CNNs) have demonstrated incredible acuity for tasks such as feature extraction or parameter estimation. Here we test CNNs on strong-field ionization photoelectron spectra, training on theoretical data sets to ‘invert’ experimental data. Pulse characterization is used as a ‘testing ground’, specifically we retrieve the laser intensity, where ‘traditional’ measurements typically lead to 20% uncertainty. We report on crucial data augmentation techniques required to successfully train on theoretical data and return consistent results from experiments, including accounting for detector saturation. The same procedure can be repeated to apply CNNs in a range of scenarios for strong-field ionization. Using a predictive uncertainty estimation, reliable laser intensity uncertainties of a few percent can be extracted, which are consistently lower than those given by traditional techniques. Using interpretability methods can reveal parts of the distribution that are most sensitive to laser intensity, which can be directly associated with holographic interferences. The CNNs employed provide an accurate and convenient ways to extract parameters, and represent a novel interpretational tool for strong-field ionization spectra.

Szołdra, Tomasz (ORCID:0000000228970506)↗
DOE OSTI2022

Critical role of current-driven instabilities for ELMs in NSTX

The impact of different extended-magnetohydrodynamic (MHD) contributions on the stability thresholds of peeling-ballooning modes in ELMing and ELM-free plasmas in the spherical tokamak NSTX is investigated with the initial value code M3D-C1. We show that ELMing discharges in NSTX are limited by resistive current-driven peeling modes, whereas non-ELMing wide-pedestal H-mode discharges are located near the ideal pressure-driven ballooning threshold. It is demonstrated that extended-MHD can lead to more reliable edge stability predictions than existing ideal-MHD models. Resistive peeling-ballooning modes are found to exist well before the ideal stability threshold is met, and kink-peeling modes exhibit considerable sensitivity to plasma resistivity. Other effects not considered in ideal-MHD models affect PB modes in NSTX in a weaker way. Gyroviscous stress appears stabilizing such that the stability boundary lies closer to the experimental point. Equilibrium rotation can suppress ideal core modes and thus isolate edge modes. These results are important for the development of a predictive pedestal model for low-aspect ratio tokamaks.

pedestal↗
DOE OSTI2023

Deep neural network uncertainty quantification for LArTPC reconstruction

We evaluate uncertainty quantification (UQ) methods for deep learning applied to liquid argon time projection chamber (LArTPC) physics analysis tasks. As deep learning applications enter widespread usage among physics data analysis, neural networks with reliable estimates of prediction uncertainty and robust performance against overconfidence and out-of-distribution (OOD) samples are critical for their full deployment in analyzing experimental data. While numerous UQ methods have been tested on simple datasets, performance evaluations for more complex tasks and datasets are scarce. Here we assess the application of selected deep learning UQ methods on the task of particle classification using the PiLArNet monte carlo 3D LArTPC point cloud dataset. We observe that UQ methods not only allow for better rejection of prediction mistakes and OOD detection, but also generally achieve higher overall accuracy across different task settings. We assess the precision of uncertainty quantification using different evaluation metrics, such as distributional separation of prediction entropy across correctly and incorrectly identified samples, receiver operating characteristic curves (ROCs), and expected calibration error from observed empirical accuracy. We conclude that ensembling methods can obtain well calibrated classification probabilities and generally perform better than other existing methods in deep learning UQ literature.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

FIRST NEW SOLAR MODELS WITH OPAS OPACITY TABLES

Stellar seismology appears more and more as a powerful tool for a better determination of the fundamental properties of solar-type stars. However, the particular case of the Sun is still challenging. For about a decade now, the helioseismic sound-speed determination has continued to disagree with the standard solar model (SSM) prediction, questioning the reliability of this model. One of the sources of uncertainty could be in the treatment of the transport of radiation from the solar core to the surface. In this Letter, we use the new OPAS opacity tables, recently available for solar modeling, to address this issue. We discuss first the peculiarities of these tables, then we quantify their impact on the solar sound-speed and density profiles using the reduced OPAS tables taken on the grids of the OPAL ones. We use the two evolution codes, Modules for Experiments in Stellar Astrophysics and Code Liégeois d’Evolution Stellaire, that led to similar conclusions in the solar radiative zone. In comparison to commonly used OPAL opacity tables, the new solar models are computed for the most recent photospheric composition with OPAS tables and present improvements to the location of the base of the convective zone and to the description of the solar radiative zone in comparison to the helioseismic observations, even if the differences in the Rosseland mean opacity do not exceed 6%. We finally carry out a comparison to a solar model computed with the OP opacity tables.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Uncertainties in the production of iron-group nuclides in core-collapse supernovae from Monte Carlo variations of reaction rates

Core-collapse supernovae, occurring at the end of massive star evolution, produce heavy elements, including those in the iron peak. Although the explosion mechanism is not yet fully understood, theoretical models can reproduce optical observations and observed elemental abundances. However, many nuclear reaction rates involved in explosive nucleosynthesis have large uncertainties, impacting the reliability of abundance predictions. To address this, we have previously developed a Monte Carlo-based nucleosynthesis code that accounts for reaction rate uncertainties and has been applied to nucleosynthesis processes beyond iron. Our framework is also well suited for studying explosive nucleosynthesis in supernovae. In this paper, we investigate 1D explosion models using the ‘PUSH method’ , focusing on progenitors with varying metallicities and initial masses around $M_{\rm ZAMS} = 16\, {\rm M}_{\odot }$. Detailed post-process nucleosynthesis calculations and Monte Carlo analyses are used to explore the effects of reaction rate uncertainties and to identify key reaction rates in explosive nucleosynthesis. We find that many reactions have little impact on the production of iron-group nuclei, as these elements are primarily synthesized in the nuclear statistical equilibrium. However, we identify a few ‘key reactions’ that significantly influence the production of radioactive nuclei, which may affect astrophysical observables. In particular, for the production of ${}^{44}{\rm Ti}$, we confirm that several traditionally studied nuclear reactions have a strong impact. However, determining a single reaction rate is insufficient to draw a definitive conclusion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC–ROC of over 89%, and it was more accurate than five other plasmid classification methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 kb long plasmid, demonstrating Deeplasmid's ability to detect novel plasmids.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Direct cross-section measurement of the weak r -process Sr 88 ( α , n ) Zr 91 reaction in ν -driven winds of core-collapse supernovae

About half of the heavy elements beyond iron are known to be produced by the rapid neutron capture process, known as the r process. However, the astrophysical site producing the r process is still uncertain. Chemical abundances observed in several cosmic sites indicate that different mechanisms should be at play. For instance, the abundances around silver measured in a subset of metal-poor stars indicate the presence of a weak r process. This process may be active in neutrino-driven winds of core collapse supernovae where (α,n) reactions dominate the synthesis of Ζ ≈ 40 elements in the expelled materials. Scarcely measured, the rates of (α,n) reactions are determined from statistical Hauser-Feshbach calculations with α-optical-model potentials, which are still poorly constrained. Further, the uncertainties of the (α,n) reaction rates therefore make a significant contribution to the uncertainties of the abundances determined from stellar modeling. In this work, the 88 Sr (α,n)⁢ 91 Zr reaction which impacts the weak r-process abundances has been probed at astrophysics energy for the first time; directly measuring the total cross sections at astrophysical energies of 8.37–13.09 MeV in the center of mass (3.8–7.5 GK). Two measurements were performed at ATLAS with the electrically segmented ionization chamber MUSIC, in inverse kinematics, while following the active target technique. The cross sections of this α-induced reaction on 88 Sr, located at the shell closure N = 50, have been found to be lower than expected, by a factor of 3, despite recent statistical calculations validated by measurements on neighboring nuclei. This result encourages more experimental investigations of (α,n) reactions, at Ν = 50 and towards the neutron-rich side, to further test the predictive power and reliability of such calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Fundamental Principles for Calculating Charged Defect Ionization Energies in Ultrathin Two-Dimensional Materials

Defects in two-dimensional (2D) materials are becoming prominent candidates for quantum emitters and scalable optoelectronic applications. However, several physical properties that characterize their behavior, such as charged defect ionization energies, are difficult to simulate with conventional first-principles methods, mainly because of the weak and anisotropic dielectric screening caused by the reduced dimensionality. We establish fundamental principles for accurate and efficient calculations of charged defect ionization energies and electronic structure in ultrathin 2D materials. We propose to use the vacuum level as the reference for defect charge transition levels (CTLs) because it gives robust results insensitive to the level of theory, unlike commonly used band-edge positions. Furthermore, we determine the fraction of Fock exchange in hybrid functionals for accurate band gaps and band-edge positions of 2D materials by enforcing the generalized Koopmans’ condition of localized defect states. We found that the obtained fractions of Fock exchange vary significantly from 0.2 for bulk hexagonal boron nitride (h-BN) to 0.4 for monolayer h-BN, whose band gaps are also in good agreement with experimental results and calculated GW results. The combination of these methods allows for the reliable and efficient prediction of defect ionization energies (the difference between CTLs and band-edge positions). We motivate and generalize these findings with several examples including different defects in monolayer to few-layer h-BN, monolayer MoS 2 , and graphane. Lastly, we show that increasing the number of layers of h-BN systematically lowers defectionization energies, mainly through CTLs shifting towards vacuum, with conduction band minima kept almost unchanged.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling Offshore Wind Farm Performance in Coastal Low-Level Jets Using Coupled Mesoscale-Microscale Large Eddy Simulations

Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.

17 WIND ENERGY↗
DOE OSTI2015

Physics-Based Compact Model for CIGS and CdTe Solar Cells: From Voltage-Dependent Carrier Collection to Light-Enhanced Reverse Breakdown: Preprint

In this paper, we develop a physics-based compact model for copper indium gallium diselenide (CIGS) and cadmium telluride (CdTe) heterojunction solar cells that attributes the failure of superposition to voltage-dependent carrier collection in the absorber layer, and interprets light-enhanced reverse breakdown as a consequence of tunneling-assisted Poole-Frenkel conduction. The temperature dependence of the model is validated against both simulation and experimental data for the entire range of bias conditions. The model can be used to characterize device parameters, optimize new designs, and most importantly, predict performance and reliability of solar panels including the effects of self-heating and reverse breakdown due to partial-shading degradation.

14 SOLAR ENERGY↗
DOE OSTI2024

Water use of co‐occurring loblolly ( Pinus taeda ) and shortleaf ( Pinus echinata ) in a loblolly pine plantation in the Piedmont

Abstract Measuring water use in co‐occurring loblolly pine (Pinus taedaL.) and shortleaf pine (Pinus echinataMill.) enhances our understanding of their competitive water use and aids in refining watershed water budget model parameters. This study was conducted in a 12‐ha forested headwater catchment in the Piedmont of North Carolina, southeastern U.S., from 2018 to 2019 (pre‐thinning) to 2020 (post‐thinning). Sap flux density (J s ), species‐level transpiration (T s ), and watershed‐level transpiration (T w ) were quantified. Water use efficiency (WUE) in loblolly and shortleaf pines was compared, alongside an investigation into how both species'J s andT s responded to atmospheric vapor pressure deficit (VPD). Loblolly pine had 19%–36% higherJ s than shortleaf pine. DailyT s for loblolly pine ranged from 15.0 to 29.0 L/day whileT s in shortleaf pine ranged from 3.0 to 6.8 L/day. TheT s was significantly higher in loblolly pine when compared to shortleaf pine likely due to higher canopy position and higher growth rates of the former. WUE, defined by annual tree biomass growth per tree water use, was not significantly different between the two. DailyJ s andT s in both species responded nonlinearly to VPD, with loblolly pine being more sensitive and variable. Species‐specific water use should be considered when quantifyingT w and developing reliable models to predict the effects of forest management practices on water resources.

Engineering↗
DOE OSTI2013

Structure of NADP + -dependent glutamate dehydrogenase from Escherichia coli - reflections on the basis of coenzyme specificity in the family of glutamate dehydrogenases

Glutamate dehydrogenases catalyse the oxidative deamination of L -glutamate to α-ketoglutarate, using NAD + and/or NADP + as a cofactor. Subunits of homo-hexameric bacterial enzymes comprise a substrate-binding domain I followed by a nucleotide-binding domain II. The reaction occurs in a catalytic cleft between the two domains. Although conserved residues in the nucleotide-binding domains of various dehydrogenases have been linked to cofactor preferences, the structural basis for specificity in the GDH family remains poorly understood. Here, the refined crystal structure of Escherichia coli GDH in the absence of reactants is described at 2.5-Å resolution. Modelling of NADP + in domain II reveals the potential contribution of positively charged residues from a neighbouring α-helical hairpin to phosphate recognition. In addition, a serine that follows the P7 aspartate is presumed to form a hydrogen bond with the 2'-phosphate. Mutagenesis and kinetic analysis confirms the importance of these residues in NADP + recognition. Surprisingly, one of the positively charged residues is conserved in all sequences of NAD + -dependent enzymes, but the conformations adopted by the corresponding regions in proteins whose structure has been solved preclude their contribution to the coordination of the 2'-ribose phosphate of NADP + . These studies clarify the sequence–structure relationships in bacterial GDHs, revealing that identical residues may specify different coenzyme preferences, depending on the structural context. Primary sequence alone is therefore not a reliable guide for predicting coenzyme specificity. We also consider how it is possible for a single sequence to accommodate both coenzymes in the dual-specificity GDHs of animals.

Sharkey, Michael A.↗
DOE OSTI2014

SU-E-T-323: The FLUKA Monte Carlo Code in Ion Beam Therapy

Purpose: Monte Carlo (MC) codes are increasingly used in the ion beam therapy community due to their detailed description of radiation transport and interaction with matter. The suitability of a MC code demands accurate and reliable physical models for the transport and the interaction of all components of the mixed radiation field. This contribution will address an overview of the recent developments in the FLUKA code oriented to its application in ion beam therapy. Methods: FLUKA is a general purpose MC code which allows the calculations of particle transport and interactions with matter, covering an extended range of applications. The user can manage the code through a graphic interface (FLAIR) developed using the Python programming language. Results: This contribution will present recent refinements in the description of the ionization processes and comparisons between FLUKA results and experimental data of ion beam therapy facilities. Moreover, several validations of the largely improved FLUKA nuclear models for imaging application to treatment monitoring will be shown. The complex calculation of prompt gamma ray emission compares favorably with experimental data and can be considered adequate for the intended applications. New features in the modeling of proton induced nuclear interactions also provide reliable cross section predictions for the production of radionuclides. Of great interest for the community are the developments introduced in FLAIR. The most recent efforts concern the capability of importing computed-tomography images in order to build automatically patient geometries and the implementation of different types of existing positron-emission-tomography scanner devices for imaging applications. Conclusion: The FLUA code has been already chosen as reference MC code in many ion beam therapy centers, and is being continuously improved in order to match the needs of ion beam therapy applications. Parts of this work have been supported by the European FP7 project ENVISION (grant agreement no. 241851)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2018

Implementing and evaluating a Gaussian mixture framework for identifying gene function from TnSeq data

The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cell’s fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization algorithm and a hierarchical Bayesian model sampled using Stan’s Hamiltonian Monte-Carlo sampler. We compare these implementations against the frequentist method used in current TnSeq literature. From simulations and real data produced by E.coli TnSeq experiments, we show that the Bayesian implementation of the Gaussian mixture framework provides the most consistent classification results.

Li, Kevin↗