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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A tutorial on the micro-trench technique for incident ion angle, material erosion, and impurity deposition measurements at plasma-facing surfaces

The micro-trench method is a new technique to measure polar and azimuthal incident ion angles, material erosion, and impurity deposition at plasma-facing surfaces, and has been applied in the DIII-D divertor. This article gives a tutorial of the micro-trench technique consisting of six steps: (1) micro-trench fabrication, (2) pre-exposure measurement of the fabricated micro-trench geometry, (3) tracer material deposition, (4) plasma exposure, (5) post-exposure observation of the tracer material, and (6) post-exposure measurement of the micro-trench geometry. Two criteria need to be satisfied to apply the micro-trench method successfully: (i) uniform impurity deposition on the micro-trench floor, and (ii) erosion dominated by the physical sputtering induced by the impinging ions. Furthermore, when those two criteria are satisfied, post-exposure analysis of the impurity deposition patterns on the micro-trench floor may be used to determine the polar and azimuthal incident ion directions (mean values of the ion angle directions), erosion rate, and impurity deposition rate during plasma exposure without computational interpretation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Search for soft unclustered energy patterns produced in association with a W or Z boson in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A search for a Higgs boson produced in association with a W or Z boson and decaying via a soft unclustered energy pattern (SUEP) is presented. The analysis is based on proton-proton collision data corresponding to an integrated luminosity of 138 fb −1 collected between 2016 and 2018 at the LHC. Final states with a leptonic W or Z boson decay associated with a high multiplicity of low-momentum charged particles are explored for the first time. The results show no significant excess over the standard model background expectation. Limits are set on the production cross section of a Higgs boson that decays to a SUEP, for a range of parameters of the SUEP model. Material is provided to facilitate further interpretation of the results.

CMS↗
DOE OSTI2021

Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning

We propose a new machine learning-based method for nowcasting earthquakes to image the time-dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross-correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper, we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near-future seismic hazards.

58 GEOSCIENCES↗
DOE OSTI2024

Symmetric and asymmetric DNA N6-adenine methylation regulates different biological responses in Mucorales

DNA N6-adenine methylation (6mA) has recently gained importance as an epigenetic modification in eukaryotes. Its function in lineages with high levels, such as early-diverging fungi (EDF), is of particular interest. Here, we investigated the biological significance and evolutionary implications of 6mA in EDF, which exhibit divergent evolutionary patterns in 6mA usage. The analysis of two Mucorales species displaying extreme 6mA usage reveals that species with high 6mA levels show symmetric methylation enriched in highly expressed genes. In contrast, species with low 6mA levels show mostly asymmetric 6mA. Interestingly, transcriptomic regulation throughout development and in response to environmental cues is associated with changes in the 6mA landscape. Furthermore, we identify an EDF-specific methyltransferase, likely originated from endosymbiotic bacteria, as responsible for asymmetric methylation, while an MTA-70 methylation complex performs symmetric methylation. The distinct phenotypes observed in the corresponding mutants reinforced the critical role of both types of 6mA in EDF.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

What Is in Umbilicaria pustulata? A Metagenomic Approach to Reconstruct the Holo-Genome of a Lichen

Lichens are valuable models in symbiosis research and promising sources of biosynthetic genes for biotechnological applications. Most lichenized fungi grow slowly, resist aposymbiotic cultivation, and are poor candidates for experimentation. Obtaining contiguous, high-quality genomes for such symbiotic communities is technically challenging. Here, we present the first assembly of a lichen holo-genome from metagenomic whole-genome shotgun data comprising both PacBio long reads and Illumina short reads. The nuclear genomes of the two primary components of the lichen symbiosis—the fungus Umbilicaria pustulata (33 Mb) and the green alga Trebouxia sp. (53 Mb)—were assembled at contiguities comparable to single-species assemblies. The analysis of the read coverage pattern revealed a relative abundance of fungal to algal nuclei of ~20:1. Gap-free, circular sequences for all organellar genomes were obtained. The bacterial community is dominated by Acidobacteriaceae and encompasses strains closely related to bacteria isolated from other lichens. Gene set analyses showed no evidence of horizontal gene transfer from algae or bacteria into the fungal genome. Our data suggest a lineage-specific loss of a putative gibberellin-20-oxidase in the fungus, a gene fusion in the fungal mitochondrion, and a relocation of an algal chloroplast gene to the algal nucleus. Major technical obstacles during reconstruction of the holo-genome were coverage differences among individual genomes surpassing three orders of magnitude. Moreover, we show that GC-rich inverted repeats paired with nonrandom sequencing error in PacBio data can result in missing gene predictions. This likely poses a general problem for genome assemblies based on long reads.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Conceptual Architecture for a Quantum-HPC Middleware

Abstract-Quantum computing promises potential for science and industry by solving certain computationally complex problems faster than classical computers. Quantum computing systems evolved from coupled to classical computing nodes (HPC). With the increasing monolithic systems towards modular architectures comprising multiple quantum processing units (QPUs) scale, middleware systems that facilitate the efficient coupling of quantum-classical computing are becoming critical. Through an in-depth analysis of quantum applications, integration patterns and systems, we identified a gap in understanding Quantum HPC middleware systems. We present a conceptual middleware to facilitate reasoning about quantum-classical integration and serve as the basis for a future middleware system. An essential contribution of this paper lies in leveraging well-established high performance computing abstractions for managing workloads, tasks, and resources to integrate quantum computing into HPC systems seamlessly

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Electronic surface reconstruction of TiO 2 nanocrystals revealed by resonant inelastic x-ray scattering

The identification of lattice multiphases in TiO 2 nanocrystals is studied by high resolution transmission electron microscope and electron diffraction patterns. Based on the spectroscopic analysis using soft x-ray absorption and resonant inelastic soft x-ray scattering, it is believed that the oxygen vacancies at the interface exhibit structural distortion of the TiO$^{8-}_6$ cluster around the defect site as for the multiphase lattice. We elucidate that the extra 3d electrons nearby induce the inelastic scattering features with the excitation energy dependence owing to different energy relaxation processes, a characteristic of the electron-phonon coupling or the nature of the electron-hole pair at the intermediate state. The manifold dd excitations driven by the strong interaction between Ti-3d and O-2p electrons are noticeably rich, coexisting on both Ti and O sites. Finally, this sophisticated experiment can advance the perspective of nanocomposite TiO 2 for various interactions of surface Ti 3+ in applications of future devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

ML for microbiomes

The software provides machine learning analysis and visualization to detect patterns in microbiome data, including topic modeling, probabilistic graphical modeling, conventional machine learning methods, and deep learning. The software is written in python and R, it uses some python and R libraries as well as big open-source libraries like sklearn, networkX, pytorch (python), pgmpy (python), and bnlearn (R). It also has a script to use for MALLET and DTM (open-source packages for topic modeling, written in Java).

Kim, Anastasiia↗
DOE OSTI2024

Machine learning tools for epigenetics

The software provides machine learning analysis and visualization to detect patterns in epigenetic data, including conventional machine learning and statistical methods, and open-source packages like pyBigWig (https://github.com/deeptools/pyBigWig) for data processing. The software is written in python, it uses some python libraries.

Kim, Anastasiia↗
DOE OSTI2023

Tail Dependence as a Measure of Teleconnected Warm and Cold Extremes of North American Wintertime Temperatures

Current models for spatial extremes are concerned with the joint upper (or lower) tail of the distribution at two or more locations. Such models cannot account for teleconnection patterns of 2-m surface air temperature ( T 2m ) in North America, where very low temperatures in the contiguous United States may coincide with very high temperatures in Alaska in the wintertime. This dependence between warm and cold extremes motivates the need for a model with opposite-tail dependence in spatial extremes. This work develops a statistical modeling framework that has flexible behavior in all four pairings of high and low extremes at pairs of locations. In particular, we use a mixture of rotations of common Archimedean copulas to capture various combinations of four-corner tail dependence. We study teleconnected T 2m extremes using ERA5 of daily average 2-m temperature during the boreal winter. Further, the estimated mixture model quantifies the strength of opposite-tail dependence between warm temperatures in Alaska and cold temperatures in the midlatitudes of North America, as well as the reverse pattern. These dependence patterns are shown to correspond to blocked and zonal patterns of midtropospheric flow. This analysis extends the classical notion of correlation-based teleconnections to considering dependence in higher quantiles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
DOE OSTI2022

The His-tag as a decoy modulating preferred orientation in cryoEM

The His-tag is a widely used affinity tag that facilitates purification by means of affinity chromatography of recombinant proteins for functional and structural studies. We show here that His-tag presence affects how coproheme decarboxylase interacts with the air-water interface during grid preparation for cryoEM. Depending on His-tag presence or absence, we observe significant changes in patterns of preferred orientation. Our analysis of particle orientations suggests that His-tag presence can mask the hydrophobic and hydrophilic patches on a protein’s surface that mediate the interactions with the air-water interface, while the hydrophobic linker between a His-tag and the coding sequence of the protein may enhance other interactions with the air-water interface. Our observations suggest that tagging, including rational design of the linkers between an affinity tag and a protein of interest, offer a promising approach to modulating interactions with the air-water interface.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Effect of Collimation on Diffraction Signal-to-Background Ratios at a Neutron Diffractometer

High diffraction signal-to-background ratios (SBRs), the ratios of diffraction peak integrated intensity over its background intensity, are desired for a neutron diffractometer to acquire good statistics for diffraction pattern measurements and subsequent data analysis. For a given detector, while the diffraction peak signals primarily depend on the characteristics of neutron beam and sample coherent scattering, the background largely originates from the sample incoherent scattering and the scattering from the instrument space. In this work, we investigated the effect of collimation on neutron diffraction SBRs of Si powder measurements using one high-angle area detector bank coupled with six different collimation configurations in a large and complex instrument space at the engineering materials diffractometer VULCAN, SNS, ORNL. The results revealed that the diffraction SBRs can be significantly improved by a proper coarse collimator that leaves no gap between the detector and the collimator, and the improvement of SBRs by a fine radial collimator was remarkable with a proper coarse collimator in place but not distinguishable without one. It was also found that the diffraction SBRs were not effectively improved by adding neutron absorbing element boron to the fine radial collimator body, which indicates that either the absorption of secondary scattered neutrons by the added boron is insignificant or the collimator base material (resin and ABS) alone attenuates background scattering sufficiently. These findings could serve as a useful reference for diffractometer developers and/or operators to optimize their collimation to achieve higher diffraction SBRs.

Yu, Dunji↗
DOE OSTI2020

Northern Hemisphere Winter Air Temperature Patterns and Their Associated Atmospheric and Ocean Conditions

The Northern Hemisphere (NH) has experienced winter Arctic warming and continental cooling in recent decades, but the dominant patterns in winter surface air temperature (SAT) are not well understood. Here, a self-organizing map (SOM) analysis is performed to identify the leading patterns in winter daily SAT fields from 1979 to 2018, and their associated atmospheric and ocean conditions are also examined. Three distinct winter SAT patterns with two phases of nearly opposite signs and a time scale of 7–12 days are found: one pattern exhibits concurrent SAT anomalies of the same sign over North America (NA) and northern Eurasia, while the other two patterns show SAT anomalies of opposite signs between, respectively, NA and the Bering Sea, and the Kara Sea and East Asia (EA). Winter SAT variations may arise from changes in the SOM frequencies. Specifically, the observed increasing trends of winter cold extremes over NA, central Eurasia, and EA during 1998–2013 can be understood as a result of the increasing occurrences of some specific SAT patterns. These SOMs are closely related to poleward advection of midlatitude warm air and equatorward movements of polar cold airmass. These meridional displacements of cold and warm airmasses cause concurrent anomalies over different regions not only in SAT but also in water vapor and surface downward longwave radiation. Anomalous sea surface temperatures in the tropical Pacific, midlatitude North Pacific, and North Atlantic and anomalous Arctic sea ice concentrations also concur to support and maintain the anomalous atmospheric circulation that causes the SAT anomalies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Explainable Machine Learning for Functional Data

Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).

Elastic Shape Analysis↗
DOE OSTI2020

Preferred Void Orientation in Uniaxially Pressed PBX 9502

Ultra- and small-angle neutron scattering techniques were used to study the morphology of voids in directions parallel and perpendicular to the compaction plane of a uniaxially pressed cylinder of PBX 9502. Two-dimensional scattering patterns from samples cut parallel to the compaction plane had circular equal-intensity contours, implying that along this view the voids are circular or randomly oriented. Identical measurements of samples cut perpendicular to the compaction plane produced elliptical equal-intensity contours, indicating preferred orientation of voids. Analysis of the two dimensional scattering patterns confirmed that the voids are non-spherical with an average aspect ratio of 1.20, flattened so that the larger dimensions of the voids lie within the compaction plane. Model analysis of the combined ultra- and small-angle neutron scattering curves revealed that the PBX 9502 porosity is interconnected, in the form of aggregates comprised of non-spherical, primary voids. The volume dimension was found to be the same in both directions, indicating that the aggregate is randomly oriented throughout the sample, and that it is the primary voids that are preferentially oriented. Furthermore, void interfaces with the surrounding matrix were found to be rough or polydisperse, with a common roughness dimension or polydispersity in all directions.

42 ENGINEERING↗
DOE OSTI2024

Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids

Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.

97 MATHEMATICS AND COMPUTING↗