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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Utah FORGE 2-2446: Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity - Workshop Presentation

This is a presentation on the Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity project by Lawrence Livermore National Laboratory, presented by Dr. Matteo Cusini. The project's objective was to employ a combination of high-fidelity simulations and true-triaxial block fracturing tests at high temperature to explore the intricate relationship between in-situ stress and hydraulic fracture patterns and better characterize the in-situ stress at Utah FORGE. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Utah FORGE 5-2419: Seismicity-Permeability Relationships Probed via Nonlinear Acoustic Imaging - Workshop Presentation

This is a presentation on the Seismicity-Permeability Relationships Probed via Nonlinear Acoustic Imaging project by Pennsylvania State University, presented by Derek Elsworth. The project's objectives were to explore controls and acoustic signatures of aseismic through seismic evolution of friction-stability-permeability relationships on fractures in shear-reactivation, link this reactivation to key features of the pre-existing stress state, and upscale these indexes to reservoir scale to drive successful reservoir stimulation. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Predicting battery capacity from impedance at varying temperature and state of charge using machine learning

Prediction of battery health from electrochemical impedance spectroscopy (EIS) data can enable rapid measurement of battery state in real-world applications without using additional sensors or time-consuming performance measurements. However, deconvoluting the effect of capacity, state of charge, and temperature on EIS response is complicated analytically. Here, various machine-learning models, such as linear, Gaussian process, random forest, and artificial neural network regression, are utilized to predict capacity from EIS using hundreds of capacity, direct current (DC) resistance, and EIS measurements recorded under varying conditions of health, temperature, and state of charge (SOC). Several feature extraction and selection methods from traditional electrochemical analysis and statistical modeling are explored using machine-learning pipelines. EIS data from just two frequencies can accurately predict capacity, and interrogation shows that the optimal set of frequencies is not usually intuitive. Best results are achieved with an ensemble model, which predicts battery capacity with a mean absolute error of 1.9% on data from unobserved cells.

25 ENERGY STORAGE↗
DOE OSTI2022

Resonant three-wave interactions with linear source/sink terms: Exploring the parameter space

Here, it is shown that the resonance three wave interactions in the presence of linear source/sink terms exhibit a wide variety of time-evolution dynamics, such as exponential growth, limit cycles, and strange attractors. The particular features of the dynamics are determined primarily by the values of linear growth rates, forming a 2D parameter space. An exploration of this parameter space is the main focus of this present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS 2 through Machine‐Learning Models

Abstract 2D transition metal dichalcogenides (TMDCs) with intense and tunable photoluminescence (PL) have opened up new opportunities for optoelectronic and photonic applications such as light‐emitting diodes, photodetectors, and single‐photon emitters. Among the standard characterization tools for 2D materials, Raman spectroscopy stands out as a fast and non‐destructive technique capable of probing material's crystallinity and perturbations such as doping and strain. However, a comprehensive understanding of the correlation between photoluminescence and Raman spectra in monolayer MoS 2 remains elusive due to its highly nonlinear nature. Here, the connections between PL signatures and Raman modes are systematically explored, providing comprehensive insights into the physical mechanisms correlating PL and Raman features. This study's analysis further disentangles the strain and doping contributions from the Raman spectra through machine‐learning models. First, a dense convolutional network (DenseNet) to predict PL maps by spatial Raman maps is deployed. Moreover, a gradient boosted trees model (XGBoost) with Shapley additive explanation (SHAP) to bridge the impact of individual Raman features in PL features is applied. Last, a support vector machine (SVM) to project PL features on Raman frequencies is adopted. This work may serve as a methodology for applying machine learning to characterizations of 2D materials.

Lu, Ang‐Yu↗
DOE OSTI2020

Build Orientation Dependent Microstructure in Polymer Laser Sintering: Relationship to Part Performance and Evolution with Aging

Laser sintering (LS) is widely used to produce functional polymeric parts; however, the resulting parts are often limited by their porous structure, and performance of the part may be strongly anisotropic. Relating the structural features of parts to build process conditions or powder feedstock has been explored previously. In contrast, little is known regarding how the unique internal structures of LS parts evolve and relate to their performance later in their life cycle, for example, after use in a real-world operating environment. In this study, a tightly controlled LS build process and measurement campaign by standard X-ray computed tomography (XCT), supported by high-resolution synchrotron XCT, was used to benchmark the internal microstructure (e.g. porosity) as a function of four different build orientations in printed polyamide-12 (PA-12) parts. The initial performance of the parts after printing was characterized by tensile testing and dynamic mechanical analysis (DMA). Arrays of PA-12 parts in different build orientations were exposed to steam (under pressure) and air across multiple temperature and time points for the purposes of accelerated aging through oxidative and hydrolytic breakdown. Exposing parts to steam dramatically altered the internal microstructure and functional properties such as the glass transition temperature, tensile properties, and damping behavior. Notably, post-aging XCT revealed large microstructural changes after only 40 h of steam treatment relative to the as-printed specimens. Furthermore, pore reorganization and changes in crystallinity occurred regardless of whether the steam temperature was aggressive enough to induce a significant loss in mechanical properties. The build orientation dependence on the aging rate was minimal with the relative trend in performance persisting across most metrics among the different orientations even after aging.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The quantum Hall effect in the absence of disorder

Abstract It is widely held that disorder is essential to the existence of a finite interval of magnetic field in which the Hall conductance is quantized, i.e., for the existence of “plateaus” in the quantum Hall effect. Here, we show that the existence of a quasi-particle Wigner crystal (QPWC) results in the persistence of plateaus of finite extent even in the limit of vanishing disorder. Several experimentally detectable features that characterize the behavior in the zero disorder limit are also explored.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structural chirality and related properties in periodic inorganic solids: review and perspectives

Abstract Chirality refers to the asymmetry of objects that cannot be superimposed on their mirror image. It is a concept that exists in various scientific fields and has profound consequences. Although these are perhaps most widely recognized within biology, chemistry, and pharmacology, recent advances in chiral phonons, topological systems, crystal enantiomorphic materials, and magneto-chiral materials have brought this topic to the forefront of condensed matter physics research. Our review discusses the symmetry requirements and the features associated with structural chirality in inorganic materials. This allows us to explore the nature of phase transitions in these systems, the coupling between order parameters, and their impact on the material’s physical properties. We highlight essential contributions to the field, particularly recent progress in the study of chiral phonons, altermagnetism, magnetochirality between others. Despite the rarity of naturally occurring inorganic chiral crystals, this review also highlights a significant knowledge gap, presenting challenges and opportunities for structural chirality mostly at the fundamental level, e.g. chiral displacive phase transitions, possibilities of tuning and switching structural chirality by external means (electric, magnetic, or strain fields), whether chirality could be an independent order parameter, and whether structural chirality could be quantified, etc. Beyond simply summarizing this field of research, this review aims to inspire further research in materials science by addressing future challenges, encouraging the exploration of chirality beyond traditional boundaries, and seeking the development of innovative materials with superior or new properties.

Bousquet, Eric (ORCID:0000000292903463)↗
DOE OSTI2024

Chapter 2: What Is Life?

The question “What is life?” has existed since the beginning of recorded history. However, the scientific and philosophical contexts of this question have changed and been refined as advancements in technology have revealed both fine details and broad connections in the network of life on Earth. Understanding the framework of the question “What is life?” is central to formulating other questions such as “Where else could life be?” and “How do we search for life elsewhere?” While many of these questions are addressed throughout the Astrobiology Primer 3.0, here this chapter gives historical context for defining life, highlights conceptual characteristics shared by all life on Earth as well as key features used to describe it, discusses why it matters for astrobiology, and explores both challenges and opportunities for finding an informative operational definition.

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

Beyond the hubble sequence – exploring galaxy morphology with unsupervised machine learning

We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantized variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (1) consideration of the clustering performance simultaneously when learning features from images; (2) allowing for various distance thresholds within the HC algorithm; (3) using the galaxy orientation to determine the number of clusters. This set-up provides 27 clusters created with this unsupervised learning that we show are well separated based on galaxy shape and structure (e.g. Sérsic index, concentration, asymmetry, Gini coefficient). These resulting clusters also correlate well with physical properties such as the colour–magnitude diagram, and span the range of scaling relations such as mass versus size amongst the different machine-defined clusters. When we merge these multiple clusters into two large preliminary clusters to provide a binary classification, an accuracy of $\sim 87{{\ \rm per\ cent}}$ is reached using an imbalanced data set, matching real galaxy distributions, which includes 22.7 per cent early-type galaxies and 77.3 per cent late-type galaxies. Comparing the given clusters with classic Hubble types (ellipticals, lenticulars, early spirals, late spirals, and irregulars), we show that there is an intrinsic vagueness in visual classification systems, in particular galaxies with transitional features such as lenticulars and early spirals. Based on this, the main result in this work is not how well our unsupervised method matches visual classifications and physical properties, but that the method provides an independent classification that may be more physically meaningful than any visually based ones.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Impedance Response Influenced by Variability in the Random Distribution of Physical Properties of Coated Materials in Two-Dimensional Space

Heterogeneous physical characteristics of a system featuring a single-layer film on a metallic surface have been explored via its impedance response. The Nyquist plot showed a distorted semicircle, indicative of the system's unique distribution characteristics. Utilizing a copula-based probability method, a two-dimensional deterministic impedance model was successfully integrated, accounting for spatial physical properties such as permittivity and electrical conductivity. This strategy enabled in-depth exploration and mechanistic quantification of a broad spectrum of properties. A quantitative understanding of impedance signal alterations, characterized by normally or log-normally correlated variables, was achieved through the variation in aspect ratio and characteristic frequency of the impedance spectra. Log-normally distributed electrical properties provided a superior representation of the distorted impedance spectra. As coefficient of variation (CV) values fluctuated, the aspect ratio and characteristic frequency showed heightened sensitivity to log-normal permittivity compared to log-normal electrical conductivity. Notably, a marked positive linear correlation between electrical properties resulted in an impedance response that approximated perfect semicircular spectra. The variability in the electrical properties' distribution was demonstrated by considering the correlation coefficient between electrical conductivity and the z-direction position. Furthermore, the highest aspect ratio of the impedance spectra was observed when the electrical conductivity was randomly distributed across the z-direction space.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Physics-driven Explosions of Stripped High-mass Stars: Synthetic Light Curves and Spectra of Stripped-envelope Supernovae with Broad Light Curves

Stripped-envelope supernovae (SESNe) represent a significant fraction of core-collapse supernovae, arising from massive stars that have shed their hydrogen and, in some cases, helium envelopes. The origins and explosion mechanisms of SESNe remain a topic of active investigation. In this work, we employ radiative-transfer simulations to model the light curves and spectra of a set of explosions of single, solar-metallicity, massive Wolf–Rayet stars with ejecta masses ranging from 4 to 11 M ⊙ , which were computed from a turbulence-aided and neutrino-driven explosion mechanism. We analyze these synthetic observables to explore the impact of varying ejecta mass and helium content on observable features. We find that the light curve shape of these progenitors with high ejecta masses is consistent with observed SESNe with broad light curves but not the peak luminosities. The commonly used analytic formula based on rising bolometric light curves overestimates the ejecta mass of these high-initial-mass progenitor explosions by a factor of up to 2.6. In contrast, the calibrated method by Haynie et al., which relies on late-time decay tails, reduces uncertainties to an average of 20% within the calibrated ejecta mass range. Spectroscopically, the He I 1.083 μm line remains prominent even in models with as little as 0.02 M ⊙ of helium. However, the strength of the optical He I lines is not directly proportional to the helium mass but instead depends on a complex interplay of factors such as the 56 Ni distribution, composition, and radiation field. Thus, producing realistic helium features requires detailed radiative transfer simulations for each new hydrodynamic model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Nonlinear dimension reduction for surrogate modeling using gradient information

We introduce a method for the nonlinear dimension reduction of a high-dimensional function $u:{\mathbb{R}}^d\rightarrow{\mathbb{R}}$, $d\gg 1$. Our objective is to identify a nonlinear feature map $g:{\mathbb{R}}^d\rightarrow{\mathbb{R}}^m$, with a prescribed intermediate dimension $m\ll d$, so that $u$ can be well approximated by $f\circ g$ for some profile function $f:{\mathbb{R}}^m\rightarrow{\mathbb{R}}$. We propose to build the feature map by aligning the Jacobian $\nabla g$ with the gradient $\nabla u$, and we theoretically analyze the properties of the resulting $g$. Once $g$ is built, we construct $f$ by solving a gradient-enhanced least squares problem. Our practical algorithm uses a sample $\{{\textbf{x}}^{(i)},u({\textbf{x}}^{(i)}),\nabla u({\textbf{x}}^{(i)})\}_{i=1}^N$ and builds both $g$ and $f$ on adaptive downward-closed polynomial spaces, using cross validation to avoid overfitting. We numerically evaluate the performance of our algorithm across different benchmarks, and explore the impact of the intermediate dimension $m$. We show that building a nonlinear feature map $g$ can permit more accurate approximation of $u$ than a linear $g$, for the same input data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

NEWTS State-Level Database Dashboard

The National Energy Water Treatment and Speciation (NEWTS) State-Level Database Dashboard enables quick visualization and exploration of energy-related wastewater data collected from state environmental agencies and research projects across the United States. The dashboard features a subset of the NEWTS Integrated Dataset (version 1.0), which was built to provide stakeholders with a unified and standardized database to support wastewater treatment and critical minerals exploration. The full NEWTS Integrated Dataset and citations for the original data resources can be found here: https://edx.netl.doe.gov/dataset/newts-integrated-dataset-version-1-0

abandoned mine drainage↗
DOE OSTI2020

Double X/Peanut structures in barred galaxies – insights from an N -body simulation

ABSTRACT Boxy, peanut-, or X-shaped ‘bulges’ are observed in a large fraction of barred galaxies viewed in, or close to, edge-on projection, as well as in the Milky Way. They are the product of dynamical instabilities occurring in stellar bars, which cause the latter to buckle and thicken vertically. Recent studies have found nearby galaxies that harbour two such features arising at different radial scales, in a nested configuration. In this paper, we explore the formation of such double peanuts, using a collisionless N-body simulation of a pure disc evolving in isolation within a live dark matter halo, which we analyse in a completely analogous way to observations of real galaxies. In the simulation, we find a stable double configuration consisting of two X/peanut structures associated with the same galactic bar – rotating with the same pattern speed – but with different morphology, formation time, and evolution. The inner, conventional peanut-shaped structure forms early via the buckling of the bar, and experiences little evolution once it stabilizes. This feature is consistent in terms of size, strength, and morphology, with peanut structures observed in nearby galaxies. The outer structure, however, displays a strong X, or ‘bow-tie’, morphology. It forms just after the inner peanut, and gradually extends in time (within 1–1.5 Gyr) to almost the end of the bar, a radial scale where ansae occur. We conclude that, although both structures form, and are dynamically coupled to, the same bar, they are supported by inherently different mechanisms.

Ciambur, Bogdan C.↗
DOE OSTI2021

Scalable Comparative Visualization of Ensembles of Call Graphs

Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and application parameters, and are interested in detecting and understanding bottlenecks in different executions. They often collect hierarchical performance profiles represented as call graphs, which combine performance metrics with their execution contexts. The crucial task of exploring multiple call graphs together is tedious and challenging because of the many structural differences in the execution contexts and significant variability in the collected performance metrics (e.g., execution runtime). In this paper, we present Ensemble CallFlow to support the exploration of ensembles of call graphs using new types of visualizations, analysis, graph operations, and features. We introduce ensemble-Sankey , a new visual design that combines the strengths of resource-flow (Sankey) and box-plot visualization techniques. Whereas the resource-flow visualization can easily and intuitively describe the graphical nature of the call graph, the box plots overlaid on the nodes of Sankey convey the performance variability within the ensemble. Our interactive visual interface provides linked views to help explore ensembles of call graphs, e.g., by facilitating the analysis of structural differences, and identifying similar or distinct call graphs. Finally, we demonstrate the effectiveness and usefulness of our design through case studies on large-scale parallel codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Updated resources for exploring experimentally-determined PDB structures and Computed Structure Models at the RCSB Protein Data Bank

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, RCSB.org), the US Worldwide Protein Data Bank (wwPDB, wwPDB.org) data center for the global PDB archive, provides access to the PDB data via its RCSB.org research-focused web portal. We report substantial additions to the tools and visualization features available at RCSB.org, which now delivers more than 227000 experimentally determined atomic-level three-dimensional (3D) biostructures stored in the global PDB archive alongside more than 1 million Computed Structure Models (CSMs) of proteins (including models for human, model organisms, select human pathogens, crop plants and organisms important for addressing climate change). In addition to providing support for 3D structure motif searches with user-provided coordinates, new features highlighted herein include query results organized by redundancy-reduced Groups and summary pages that facilitate exploration of groups of similar proteins. Newly released programmatic tools are also described, as are enhanced training opportunities.

Burley, Stephen K.↗
DOE OSTI2020

Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images

Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this paper, we present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. It allows domain scientists to interactively explore this important type of scientific images in embedded spaces that are defined on the model prediction output, the actual labels, and the discovered feature space of neural networks. Users are allowed to flexibly select instance images, their clusters, and compare them regarding the specified visual representation of attributes. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes, which often affect the learning accuracy and effectiveness. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters. Case studies and scientists feedback demonstrate its functionalities and usefulness.

97 MATHEMATICS AND COMPUTING↗