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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

ZTF SN Ia DR2 follow-up: Exploring the origin of the Type Ia supernova host galaxy step through Si II velocities

The relation between Type Ia supernovae (SNe Ia) and the stellar masses of their host galaxy is well documented. In particular, Hubble residuals display a distinct luminosity shift based on host mass. This is known as the mass step. This effect is widely used as an additional correction factor in the standardisation of SN Ia luminosities. We investigate the Hubble residuals and the mass step of normal SNe Ia in the context of Si IIλ6355 velocities based on 277 normal SNe Ia that are near their peak in the second data release (DR2) of the Zwicky Transient Facility (ZTF). We divided the sample into high-velocity (HV) and normal-velocity (NV) SNe Ia, separated at 12,000 km s −1 . This produced a sample of 70 HV and 207 NV objects. We then explored potential environment- and/or progenitor-related effects by investigating the Si IIλ6355 velocities with parameters such as the light-curve stretch x 1 , the colour c, and the host galaxy properties. Although we only find a marginal difference between the Hubble residuals of HV and NV SNe Ia, the NV mass step is 0.149 ± 0.024 mag (6.3σ). The HV mass step is smaller, 0.046 ± 0.041 mag (1.1σ), and is consistent with zero. The difference between the NV and HV mass steps is modest, at ∼2.2σ. Moreover, the clearest subtype difference appears for SNe in central regions (d DLR < 1), where NV SNe Ia show a large mass step, whereas HV SNe Ia are consistent with no step, yielding a difference of 3.1–3.6σ between NV and HV SNe Ia. We observe a host-colour step for both subtypes. NV SNe Ia show a step of 0.142 ± 0.024 mag (5.9σ), while HV SNe Ia show a step of 0.158 ± 0.042 mag (3.8σ), where the HV SNe Ia step appears to be larger, but the significance is lower because the sample size is smaller. Overall, the NV and HV colour steps are statistically consistent. HV SNe Ia also show modest (∼2.5–3σ) steps in certain subsets, such as those in outer regions (d DLR > 1), whereas NV SNe display stronger environmental trends. Our results indicate that NV SNe Ia appear to be more environmentally sensitive, particularly in central likely metal-rich and older regions, while HV SNe Ia show weaker and subset-dependent trends. This suggests that applying a universal mass-step correction might introduce biases, and that incorporating refined classifications and/or environment-dependent factors, such as the location within the host, might improve future cosmological analyses beyond the standard x 1 and c cuts.

supernovae: general↗
DOE OSTI2023

O’Hare Airport Short-Term Ground Transportation Modal Demand Forecast Using Gaussian Processes

Here, the principal objective of this study is to analyze the spatial and temporal variation of ground transportation airport demand and provide demand forecast to inform planning capability and explore alternatives for investments to accommodate airport growth. Because of its good adaptability and strong generalization ability for dealing with high-dimensional input, small-sample, and nonlinear spatial data, Gaussian process (GP) regression is used to provide forecast estimates using data from transportation network company (TNC) trips and urban rail passengers at Chicago's O'Hare International Airport. TNC airport trips differ significantly, with three times more distance, more than twice the travel time, and half of the share requests compared with nonairport trips. This highlights the need for separate demand models. Hourly analysis of the rail service indicates that this is likely heavily used by airport workers, whereas TNC services focus on travelers because of variations in the peak demand hours. Heteroscedastic GP regression is implemented because of differences in trip variance between night and day hours. Estimates are given for weekdays and weekend trips, and the 95% confidence intervals are calculated. The introduction of flight schedule information into the models shows marginal improvements in their performance. However, fitting a GP regression becomes computationally expensive with increased sample size and the introduction of spatial components. Transportation planners and policymakers can use the results and methods implemented in this study to optimize transportation assets and provide long-range simulations of the current and future conditions in the area.

42 ENGINEERING↗
DOE OSTI2020

A hybrid approach to excited-state-specific variational Monte Carlo and doubly excited states

We extend our hybrid linear-method/accelerated-descent variational Monte Carlo optimization approach to excited states and investigate its efficacy in double excitations. In addition to showing a superior statistical efficiency when compared to the linear method, our tests on small molecules show good energetic agreement with benchmark methods. We also demonstrate the ability to treat double excitations in systems that are too large for a full treatment by using selected configuration interaction methods via an application to 4-aminobenzonitrile. Finally, we investigate the stability of state-specific variance optimization against collapse to other states’ variance minima and find that symmetry, Ansatz quality, and sample size all have roles to play in achieving stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Aerosol jet printed 3 omega sensors for thermal conductivity measurement

The 3 omega (3ω) method is a trusted technique for measuring thermal conductivity—a fundamental material property of critical importance in a broad range of applications. However, traditional 3ω sensor processing requires some form of physical vapor deposition, such as metal evaporation or sputtering. These 3ω sensor deposition techniques limit the materials and sample sizes applicable to the 3ω method. This work demonstrates an aerosol jet printing method to directly print silver 3ω sensors that yield accurate temperature-dependent measurement up to 300 °C on materials with thermal conductivity ranging from 1 to 150 W/m K. The interrelationship between printed sensor geometry, sensor thermal stability, and applicability to the 3ω method is examined. Thermal conductivity measurement with 3ω sensors conventionally sintered at 300 °C agrees to independent laser flash measurement within 4% from room temperature to 150 °C. An unconventional rapid high-temperature sintering method is shown to produce sensors that agree within 3% of the laser flash measurements from room temperature to 300 °C. The rapid sintering profiles also reduced the sensor–substrate thermal boundary resistance of the printed sensors by as much as 88%. The direct printing of 3ω sensors creates opportunities for measurement of thermal transport properties in applications previously inapplicable to the 3ω method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Achieving high spatial resolution in a large field-of-view using lensless x-ray imaging

X-ray ptychography, a powerful scanning lensless imaging technique, has become attractive for nondestructively imaging internal structures at nanoscale. Stage positioning overhead in conventional step-scan ptychography is one of the limiting factors on the imaging throughput. In this work, we demonstrate the use of advanced fly scan ptychography to achieve high-resolution ptychograms of modern integrated circuits on a large field-of-view at millimeter scale. By completely removing stage overheads between scan points, the imaging time for millimeter-size sample can be significantly reduced. Furthermore, we implement the orthogonal probe relaxation technique to overcome the variation of illumination across the large scan area as well as local vibrations. The capability of x-ray ptychography shown here is broadly applicable for various studies, which requires both high spatial resolution and large scan area.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Development of an operando characterization stage for multi-modal synchrotron x-ray experiments

It is widely accepted that micro- and nanoscale inhomogeneities govern the performance of many thin-film solar cell absorbers. These inhomogeneities yield material properties (e.g., composition, structure, and charge collection) that are challenging to correlate across length scales and measurement modalities. The challenge is compounded if a correlation is sought during device operation or in conditions that mimic aging under particular stressors (e.g., heat and electrical bias). Correlative approaches, particularly those based on synchrotron x-ray sources, are powerful since they can access several material properties in different modes (e.g., fluorescence, diffraction, and absorption) with minimal sample preparation. Small-scale laboratory x-ray instruments have begun to offer multi-modality but are typically limited by low x-ray photon flux, low spatial resolution, or specific sample sizes. To overcome these limitations, a characterization stage was developed to enable multi-scale, multi-modal operando measurements of industrially relevant photovoltaic devices. The stage offers compatibility across synchrotron x-ray facilities, enabling correlation between nanoscale x-ray fluorescence microscopy, microscale x-ray diffraction microscopy, and x-ray beam induced current microscopy, among others. Furthermore, the stage can accommodate device sizes up to 25 × 25 mm 2 , offering access to multiple regions of interest and increasing the statistical significance of correlated properties. The stage materials can sustain humid and non-oxidizing atmospheres, and temperature ranges encountered by photovoltaic devices in operational environments (e.g., from 25 to 100 °C). As a case study, we discuss the functionality of the stage by studying Se-alloyed CdTe photovoltaic devices aged in the stage between 25 and 100 °C.

14 SOLAR ENERGY↗
DOE OSTI2022

Local measurement of bulk thermal diffusivity using photothermal radiometry

Here, an experimental methodology using photothermal radiometry is developed for the accurate measurement of bulk thermal diffusivity of nuclear fuels and materials irradiated to high doses. Under these conditions, nuclear fuels, such as uranium oxide, and moderator materials, such as graphite, become friable, which requires characterization techniques that can accommodate irregularly shaped fragments. Photothermal radiometry, a good candidate for this application, involves locally heating a sample by using a laser and measuring the temperature field by monitoring blackbody radiation. The interaction volume for this study, less than a millimeter, is carefully chosen to sample a statistically significant number of large-scale structural features, such as pores and gas filled bubbles, and is small enough that the sample fragments can be treated as a thermal half-space. The thermal diffusivity standards considered in this study cover a range of thermal diffusivities representative of both fresh and spent nuclear fuels. We also consider a sample having a porous microstructure representative of large-scale structures found in materials irradiated to high doses. Our measurement methodology circumvents complex thermal wave models that address optical diffraction, nonlinear transfer function associated with blackbody radiation, and finite sample size effects. Consequently, the large measurement uncertainty associated with modeling these effects can be avoided. While the emphasis here is on nuclear fuels and materials, this measurement approach is well suited to measure thermal transport in a variety of technologically important materials associated with advanced synthesis techniques. Examples range from small, exotic single crystals grown using hydrothermal growth techniques to additively manufactured components having complex geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Cryogen-free modular scanning tunneling microscope operating at 4-K in high magnetic field on a compact ultra-high vacuum platform

One of the daunting challenges in modern low temperature scanning tunneling microscopy (STM) is the difficulty of combining atomic resolution with cryogen-free cooling. Further functionality needs, such as ultra-high vacuum (UHV), high magnetic field (HF), and compatibility with μm-sized samples, pose additional challenges to an already ambitious build. We present the design, construction, and performance of a cryogen-free, UHV, low temperature, and high magnetic field system for modular STM operation. An internal vibration isolator reduces vibrations in this system, allowing for atomic resolution STM imaging while maintaining a low base temperature of ∼4 K and magnetic fields up to 9 T. Samples and tips can be conditioned in situ utilizing a heating stage, an ion sputtering gun, an e-beam evaporator, a tip treater, and sample exfoliation. In situ sample and tip exchange and alignment are performed in a connected UHV room temperature stage with optical access. Multisite operation without breaking vacuum is enabled by a unique quick-connect STM head design. A novel low-profile vertical transfer mechanism permits transferring the STM between room temperature and the low temperature cryostat.

Instruments & Instrumentation↗
DOE OSTI2021

Modeling freight mode choice using machine learning classifiers: a comparative study using Commodity Flow Survey (CFS) data

This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naïve Bayes, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random Forest, Boosting and Bagging, along with the classical Multinomial Logit model. US 2012 Commodity Flow Survey data are used as the primary data source; we augment it with spatial attributes from secondary data sources. The performance of the classifiers is compared based on prediction accuracy results. The current research also examines the role of sample size and training-testing data split ratios on the predictive ability of the various approaches. In addition, the importance of variables is estimated to determine how the variables influence freight mode choice. The results show that the tree-based ensemble classifiers perform the best. Specifically, Random Forest produces the most accurate predictions, closely followed by Boosting and Bagging. With regard to variable importance, shipment characteristics, such as shipment distance, industry classification of the shipper and shipment size, are the most significant factors for freight mode choice decisions.

42 ENGINEERING↗
DOE OSTI2026

A Proof of the Asymptotic Variance of Path Length Estimators for Single-Collision Monte Carlo Source Iteration in the Thick Diffusion Limit

Here, we prove a theorem relating the variance of path length estimators for single-collision Monte Carlo source iteration to a parameter that becomes infinitesimally small in an important physical regime arising in radiative transfer. In our usage, “single-collision Monte Carlo source iteration” refers to Monte Carlo Boltzmann transport methods in which each Monte Carlo particle history includes no more than a single collision, and the physics of multiple scattering is modeled by lagging the scattering source term and iterating until this term converges. Our theorem can be used to construct variance reduction techniques which improve the order of the estimator variance. This enables calculations that would otherwise require impractically large sample sizes to achieve practical estimator uncertainties. We believe this is the first postulation of a theorem relating estimator variance to a limiting case parameter for single-collision Monte Carlo source iteration, and the first proof of such a theorem. We illustrate the theorem’s value with an example in which the authors of a transport method used the theorem to design a variance reduction technique that improved the uncertainty of their solution by a factor of about 500 for a proxy problem from radiative transfer that contains both optically-thick and optically-thin material.

Mathematics and Computing↗
DOE OSTI2020

A small-volume, high-throughput approach for surface tension and viscosity measurements of liquid fuels

An ongoing effort looking at bioblendstocks to function as drop-in replacements or blending components for gasoline has identified a large number of candidate fuels. This work documents an approach for rapid screening of candidate fuels using relatively small sample sizes ($\mathcal{O}(\mu\,{\rm l})$) that targets two key physical properties of liquid fuels—surface tension and viscosity. The approach utilizes shape oscillation dynamics of single droplets generated by a piezo-electric device and their decay over time. Strobed imaging of the oscillation process is used along with image processing, edge detection, and data analysis to capture the decay of the oscillation over time. The time constant of the decay process along with oscillation frequency are then used to estimate viscosity and surface tension using a theory for small amplitude droplet oscillations. Measurements are obtained for primary reference fuels (isooctane and n-heptane) as well as candidate fuels from four bio-derived functional groups of interest. Measurement results for surface tension and viscosity are correlated with literature data as well as measurements from standard reference instruments. The measurement results show that the droplet oscillation based approach is capable of reproducing surface tension and viscosity values for the tested fuels within deviations of 7% and 13% respectively from literature data. Results are obtained using an average of 5 µl per fuel within about 20 s, thus demonstrating a small-volume, high-throughput approach that can be used for screening of candidate bioblendstock fuels.

09 BIOMASS FUELS↗
DOE OSTI2022

Region-based convolutional neural network for wind turbine wake characterization from scanning lidars

A convolutional neural network is applied to lidar scan images from three experimental campaigns to identify and characterize wind turbine wakes. Initially developed as a proof-of-concept model and applied to a single data set in complex terrain, the model is now improved and generalized and applied to two other unique lidar data sets, one located near an escarpment and one located offshore. The model, initially developed using lidar scans collected in predominantly westerly flow, exhibits sensitivity to wind flow direction. The model is thus successfully generalized through implementing a standard rotation process to scan images before input into the convolutional neural network to ensure the flow is westerly. The sample size of lidar scans used to train the model is increased, and along with the generalization process, these changes to the model are shown to enhance accuracy and robustness when characterizing dissipating and asymmetric wakes. Applied to the offshore data set in which nearly 20 wind turbine wakes are included per scan, the improved model exhibits a 95% success rate in characterizing wakes and a 74% success rate in characterizing dissipating wake fragments. The improved model is shown to generalize well to the two new data sets, although an increase in wake characterization accuracy is offset by an increase in model sensitivity and false positive wake identifications.

17 WIND ENERGY↗
DOE OSTI2024

Exploring dielectric properties in atomistic models of amorphous boron nitride

Abstract We report a theoretical study of dielectric properties of models of amorphous Boron Nitride, using interatomic potentials generated by machine learning. We first perform first-principles simulations on small (about 100 atoms in the periodic cell) sample sizes to explore the emergence of mid-gap states and its correlation with structural features. Next, by using a simplified tight-binding electronic model, we analyse the dielectric functions for complex three dimensional models (containing about 10.000 atoms) embedding varying concentrations of sp 1 , sp 2 and sp 3 bonds between B and N atoms. Within the limits of these methodologies, the resulting value of the zero-frequency dielectric constant is shown to be influenced by the population density of such mid-gap states and their localization characteristics. We observe nontrivial correlations between the structure-induced electronic fluctuations and the resulting dielectric constant values. Our findings are however just a first step in the quest of accessing fully accurate dielectric properties of as-grown amorphous BN of relevance for interconnect technologies and beyond.

Materials Science↗
DOE OSTI2023

A robust estimator of mutual information for deep learning interpretability

Abstract We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning (DL) models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced ‘Jimmie’), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established MI estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train DL models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available in this GitHub repository.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Information-incorporated gene network construction with FDR control

Abstract Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗
DOE OSTI2022

Automatic information extraction from childhood cancer pathology reports

The International Classification of Childhood Cancer (ICCC) facilitates the effective classification of a heterogeneous group of cancers in the important pediatric population. However, there has been no development of machine learning models for the ICCC classification. We developed deep learning-based information extraction models from cancer pathology reports based on the ICD-O-3 coding standard. In this article, we describe extending the models to perform ICCC classification. We developed 2 models, ICD-O-3 classification and ICCC recoding (Model 1) and direct ICCC classification (Model 2), and 4 scenarios subject to the training sample size. We evaluated these models with a corpus consisting of 29206 reports with age at diagnosis between 0 and 19 from 6 state cancer registries. Our findings suggest that the direct ICCC classification (Model 2) is substantially better than reusing the ICD-O-3 classification model (Model 1). Applying the uncertainty quantification mechanism to assess the confidence of the algorithm in assigning a code demonstrated that the model achieved a micro-F1 score of 0.987 while abstaining (not sufficiently confident to assign a code) on only 14.8% of ambiguous pathology reports. Our experimental results suggest that the machine learning-based automatic information extraction from childhood cancer pathology reports in the ICCC is a reliable means of supplementing human annotators at state cancer registries by reading and abstracting the majority of the childhood cancer pathology reports accurately and reliably.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Connecting radio emission to AGN wind properties with broad absorption line quasars

ABSTRACT Broad absorption line quasars (BALQSOs) show strong signatures of powerful outflows, with the potential to alter the cosmic history of their host galaxies. These signatures are only seen in ∼10 per cent of optically selected quasars, although the fraction significantly increases in IR and radio selected samples. A proven physical explanation for this observed fraction has yet to be found, along with a determination of why this fraction increases at radio wavelengths. We present the largest sample of radio matched BALQSOs using the LOFAR Two-metre Sky Survey Data Release 2 and employ it to investigate radio properties of BALQSOs. Within the DR2 footprint, there are 3537 BALQSOs from Sloan Digital Sky Survey DR12 with continuum signal-to-noise ≥5. We find radio-detections for 1108 BALQSOs, with an important subpopulation of 120 LoBALs, an unprecedented sample size for radio matched BALQSOs given the sky coverage to date. BALQSOs are a radio-quiet population that show an increase of ×1.50 radio-detection fraction compared to non-BALQSOs. LoBALs show an increase of ×2.22 that of non-BALQSO quasars. We show that this detection fraction correlates with wind-strength, reddening, and C iv emission properties of BALQSOs and that these features may be connected, although no single property can fully explain the enhanced radio detection fraction. We create composite spectra for subclasses of BALQSOs based on wind strength and colour, finding differences in the absorption profiles of radio-detected and radio-undetected sources, particularly for LoBALs. Overall, we favour a wind-ISM interaction explanation for the increased radio-detection fraction of BALQSOs.

79 ASTRONOMY AND ASTROPHYSICS↗