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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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Motivation: Non-congregational shelters are a place where COVID-19 patients can quarantine away from their household until recovered. NM wants to establish non-congregational shelters for COVID-19 patients and first responders; Where? How many rooms? Create a decision support tool to assess which counties have the most vulnerable population to COVID-19; Socioeconomic status, community practices.
The DOE Office of Nuclear Energy (DOE-NE) repository research and development program seeks to provide a sound technical basis for multiple viable disposal options, increase confidence in the robustness of generic disposal concepts, and develop science and engineering tools needed to support disposal concept implementation. Sandia, Los Alamos, and Lawrence Berkeley National labs are conducting research into salt, which includes the Brine Availability Test in Salt (BATS) field test at the Waste Isolation Pilot Plant (WIPP), a DOE Office of Environmental Management facility. BATS is leveraging the existing infrastructure associated with the WIPP to advance science informing generic disposal concepts. BATS is an ongoing heated borehole experiment being conducted in the WIPP underground. The goal of this experiment is to reduce the uncertainty associated with spent nuclear fuel disposition in geologic salt formations. The BATS experiments is designed to accomplish this by increasing our understanding of brine migration in salt, assessing damaged zones from mining and drilling, simulating a post-closure environment, confirming salt properties, and providing data for model validation. Phased testing for generic salt repository R&D was proposed in 2015. Subsequently, borehole thermal testing was developed as a first step in this process. This document summarizes the work conducted on the BATS experiment during fiscal year 2020 with an emphasis on the contributions by Los Alamos National Lab.
Coherent manipulation of quantum states is at the core of quantum information science (QIS). Many state-of-the-art quantum systems rely on microwave fields for quantum operations. As such, the microwave electromagnetic fields serve as the ideal "quantum bus" to integrate different types of QIS systems into a hybrid quantum system. Superconducting metamaterials are artificial materials consisting of arrays of superconducting resonant microstructures with sizes much smaller than the microwave wavelengths of interest. Superconducting metamaterials are a strong candidate medium for the microwave quantum bus, because the effective impedance, field distributions, and frequency response can all be controlled by engineering the microstructures, electrical bias, and magnetic flux while maintaining extremely low loss. In this project, we investigate the fundamental unit of a superconducting metamaterial - a resonator with physical dimensions much smaller than the microwave wavelengths - using NbTiN as the working superconductor, whose high operating temperatures and magnetic fields are desirable attributes for compatibility with a wide variety of quantum systems. We first studied the properties of sputtered NbTiN thin films by correlating the film thickness with the normal state resistivity, superconducting transition temperature, and resonances of transmission line resonators made from these films. We developed a process flow and designed a coplanar waveguide platform for studying small resonators. The platform significantly shortens the turnaround times of the resonator fabrication and testing cycles. Several resonators with different designs were fabricated and tested at 4 Kelvin. Resonances were observed in some resonator testers. Potential paths for improvements and future directions are discussed.
The SPOKE graph [2, 6] is a sparse decorated semantic graph representing a collection of knowledge collected in many scientific databases from the fields of healthcare, biochemistry, chemistry, biology, et cetera. This knowledge graph is stored as a relational dataset decorated with metadata on each constituent vertex and edge. Formally, the graph is G(V, E, D), where V is a set of n vertices V := {1, ..., n} and edges of the form (i, j) ϵ E for i, j ϵ V, and table D that for any item in V υ E stores unstructured data such as vertex/edge type, nature of a relationship, et cetera. D(i) = {data involving vertex i ϵ V}, and D(i, j) = {data involving edge (i, j) ϵ E}. Here, we treat the graph as undirected in the sense that a direct relationship for (i, j) causes a (possibly opposite) reverse direct relationship for (j, i). The SPOKE graph G(V, E, D) is formed by processing a collection of relational datasets from medicine, chemistry, and biology, connecting many entities. Here, we analyze an instance from 2019, Spoke-20190707, where a graph file contains 6.16M edges and associated metadata and a vertex file contains 2.15M vertices and the associated metadata. There are 12 different types of vertex entities; all edge types used are implicit (see §2). There is other metadata in D on edges and vertices, but we just use the topology and the vertex labels in this report. SPOKE is growing as more knowledge is gained and more datasets are added. SPOKE is likely to grow 10x during the next phase of this project, and we therefore would like to consider topoligical analysis techniques that are scalable to several orders of magnitude larger than the current dataset (say >1B edges).
The goal of this project was to unite powerful analysis techniques in high-energy physics (HEP) with cutting-edge advances in quantum information science (QIS). Almost every event at the Large Hadron Collider (LHC) involves jets, collimated sprays of particles that are copiously produced in proton-proton collisions. Current LHC algorithms to identify and classify jets are constrained by the limits of classical computation. Quantum algorithms could fundamentally change how collider data is analyzed, by speeding up existing classical algorithms and by enabling new quantum jet representations. By exploiting the capabilities of quantum computation, this research confronted the challenge of data analysis in collider physics.
Focal Area 3: Explainable AI Our confidence in the projections made by Earth System Models (ESMs) depends on understanding them to be, in some important respects, faithful representations of the Earth system. Here we present an “explainable Artificial Intelligence (AI)” method that allows us to uncover the dynamical structure of the observed and modeled Earth system, discover hidden links across wide spatiotemporal scales, target model development efforts at poorly-represented dynamics, and optimize observed or modeled data collection to maximize predictive information. Science Challenge: Dynamical system science for the Earth system poses unique challenges given the large degree of internal climate variability. Thus, tools that help us understand how ESMs succeed and fail at representing these dynamics are crucial, particularly in relation to the observed system. Furthermore, the computational and memory constraints on ESM data output motivate in situ analysis of ESM dynamics, including automatic detection of dynamical shifts. Also, of key importance are procedures that leverage ESMs to optimize observational campaigns for improving process representation, reducing structural uncertainty and improving model skill.
At Lawrence Livermore National Laboratory (LLNL), we operate and maintain the Quantum Device and Integration Testbed (QuDIT) facility, a small quantum testbed that supports about 10 active research teams (including our own) and over 50 internal and external collaborators. This testbed is designed to give remote white box access to users for research, training, and outreach. A guiding principle behind the development of our testbed infrastructure, software and user interfaces is to empower users to perform experiments at the cutting edge of quantum information science at any level of abstraction, from materials studies, device physics and control and characterization techniques to algorithm development and quantum operating system design. Our testbed targets a multilevel quantum system (qudit) to expand the accessible Hilbert space of a simple-to-manufacture quantum device and focuses on quantum simulation, typically implemented through custom gates designed with quantum optimal control methods, rather than on a universal computing framework with a fixed gate set. We leverage the Lab’s high-performance computing (HPC) program and related expertise to simulate quantum systems, develop hybrid algorithms, and generate gates optimized for given simulations. Additionally, we have adopted a co-design philosophy from the HPC community in designing new hardware, so that the systems we develop are optimized for the specific physics simulations we plan to use them for.
This report provides an analysis of engineering and geologic data collected from the Hydraulic Fracturing Test Site #1 (HTSF-1) project in the southern Midland Basin, Reagan County, Texas. The site is being studied as part of the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative at the National Energy Technology Laboratory (NETL). The data collected is intended to provide a basis of understanding of the site and to construct simulation models. This report provides a summary analysis of the engineering and geologic data collected from the project to provide a basis of understanding of the site and to construct simulation models. The report examines various possible correlations in engineering properties based on natural fracture data from the program, which consisted of 11 horizontal wells, one vertical well, and one slant well. The horizontal wells are in two horizons: 1) Upper and 2) Middle Wolfcamp formations. Program data include: fracture frequency and fracture orientation data from four core runs in a slant well; fracture spacing and orientation from a vertical pilot well; laboratory triaxial testing and mineralogical determinations; and porosity results from magnetic resonance analyses from various wells, together with observations based on the data collection. In addition, available references were reviewed on the site for additional insights. As the focus of the report is on the natural system, hydraulic fracture data from the site were not examined in detail in this report. Data variability is the chief observation in examination of the database. Fracture frequency in the slant well can range from sections with values as high as five fractures per ft to sections up to 100+ ft in length with no natural observed fractures. Fracture spacing across is typically less than 10 ft, but can range up to hundreds of feet. Apparent fracturing shows the trends in two predominate orientations, E-SW and WNW-ESE, but minor variations exist. The rock units vary across the site from siliceous mudstones to calcareous mudstones, showing a general layering with depth. The laboratory properties such as strength and modulus show no apparent trend with depth, but appear to correlate with rock mineralogy with high strength and modulus values where calcium content is high. In addition, an attempt to examine variability and mineralogy on a larger scale was made using a color-coded system based on gamma ray measurements. A staged colored approach was adopted, presuming that lower gamma ray values indicate higher value of calcium content (blue scale) and that higher gamma ray values indicate higher clay mineral content (orange scale). As provided in report appendices, the system correlated well with visual examination of the slant core and the petrofabric analyses of the vertical pilot well. The results showed large variability in mineral content along the horizontal plane across the site. The change in mineral content was also rapid, on a scale less than that of the average hydraulic fracture stage length of about 180 ft.
The U.S. Department of Energy Solar Decathlon (DOE/SD) is a collegiate competition, comprising 10 contests, that challenges student teams to design and build highly efficient and innovative buildings powered by renewable energy. The winners will be those teams that best blend architectural and engineering excellence with innovation, market potential, building efficiency, and smart energy production. The DOE/SD is comprised of two Challenges--Design Challenge (annual, in April of each year at NREL in Golden, Colo.) and Build Challenge (biennial, held next in June/July 2020 in Washington D.C.). NREL provides competition management for DOE/SD. For over 35 years, EEBA has provided the most trusted resources for building science information and education in the construction industry. EEBA delivers turn-key educational resources and events designed to transform residential construction practices through high performance design, marketing, materials, and technologies. NREL and EEBA establish this CRADA to enable the success of the DOE/SD program in managing sponsorship funds and creating a professional education program. EEBA acted as an Education Partner to DOE/SD, which included: 1) accepting and dispersing sponsorship funds for DOE/SD; and 2) providing professional development content.
Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.
We aim to use machine learning to teach a computer to decompose models into meshable sub-shapes.
Abstract not provided.
Sandia has a multiplatform, multiapplication quantum information science program. The QIS program is built leveraging Sandia’s strengths in microelectronics fabrication, nanotechnology, and computational modeling, and complements and strengthens Sandia’s overall mission.
Ultra-low loss microwave resonant cavities are the backbone of quantum information science (QIS) and axion/dark matter detection (ADMX), but one of the challenges in developing 3D cavity systems has been finding a tuning mechanism that does not drastically reduce the cavity quality factor (Q) but still provides a large operating bandwidth. To alleviate this limitation, we proposed to fabricate a high-Q, 3D microwave cavity with an integrated hybrid mechanical/electrical tuning scheme to achieve both coarse and rapid fine tuning. We implemented two tuning schemes: one using a shift in kinetic inductance and one utilizing mechanical motion. We found that the kinetic inductance method did not yield any tunability in the cavity mode, but the mechanical adjustment successfully moved the cavity resonance. The quality factors obtained were lower than originally proposed, but there is potential to improve the design on further iterations.
In the last 23 years, three mosquito-borne diseases have been introduced to the Americas and have quickly spread in a matter of months.
The driving goal is to investigate the feasibility of a message-based data transport for SPDK. All of SPDK’s existing data transport protocols are “connection-based”, requiring a target to hold state for each connected client / initiator. At extreme scale, juggling these connections from a large number of initiators creates a performance bottleneck. Under a message-based paradigm, we can bypass this behavior, as all initiators share a global communication channel, held open for the entire run. In theory, this approach should minimize connection management overhead at extreme scale.
Quantum Information Science (QIS) is an emerging technology being pursued by fundamental science research groups worldwide, as well as commercial companies and government programs. There are a variety of QIS disciplines, including quantum computing, quantum sensing and quantum encryption. Some of the commodities needed for a robust quantum laboratory are particular to quantum phenomenon, but in general the equipment needed is similar to that needed for a typical high - technology lab (e.g. oscilloscopes, lasers, vacuum chambers, etc.). This study focuses on identifying commodities manufactured worldwide that would be needed for a robust quantum lab. The authors' own knowledge of needed equipment and primary vendors was used as a starting point, follow ed by extensive internet searching and utilization of buyer's guides to create a large spreadsheet of most of the components needed, the company offering the components, and country of manufacture. With this extensive spreadsheet, stakeholders can identify commodities that would be needed for a quantum lab oratory and potentially identify market choke points.
This project has investigated the magnetic and electric properties of 2D materials, with emphasis on the (anti-)ferromagnetic properties of 2D crystalline magnets and a new concept for stacking engineering of 2D ferroelectricity. The new basic physics fundamental knowledge obtained through this research may have important implications for future technologies based on magnetism and ferroelectricity, for example spintronics, quantum information science, or neuromorphic computing.