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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. Furthermore, one experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Observation of multiple nodal lines in SmSbTe

Having been a ground for various topological fermionic phases, the family of ZrSiS-type 111 materials has been under experimental and theoretical investigations. Within this family of materials, the subfamily LnSbTe (Ln = lanthanide elements) is gaining interest in recent times as the strong correlation effects and magnetism arising from the 4f electrons of the lanthanides can provide an important platform to study the link between topology, magnetism, and correlation. In this Letter, we report the systematic study of the electronic structure of SmSbTe—a member of the Ln SbTe subfamily—by utilizing angle-resolved photoemission spectroscopy in conjunction with first-principles calculations, transport, and magnetic measurements. Our experimental results identify multiple Dirac nodes forming the nodal lines along the Γ–X and Z–R directions in the bulk Brillouin zone (BZ) as predicted by our theoretical calculations. Additionally, a surface Dirac-like state is also observed at the X point of the surface BZ. Our study highlights SmSbTe as a promising candidate to understand the topological electronic structure of LnSbTe materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Artificial Intelligence and Computer Vision for Cost-Effective Benthic Habitat Characterizations

Machine learning applied to computer vision and pattern recognition is a type of artificial intelligence that has advanced rapidly in the last 10 to 15 years, spurred forward by breakthroughs in deep convolutional neural networks. These state-of-the-art methods are poised to become widely used in environmental monitoring applications as a result of the increasing abundance of data available from different imaging platforms (e.g., fixed-point cameras, drone surveys, high-resolution satellite data) that can be analyzed to observe, model, and understand environmental conditions. Computer vision and pattern recognition (CVPR) tools advance our ability to use imagery and camera-based tools in cost-effective ways for environmental monitoring. Although these techniques offer great potential, some challenges remain, such as the need for large sets of labeled images for model training and validation and optimized hardware and software to ensure that the models can be trained effectively and in a reasonable amount of time. Here, we have overcome many of these rate-limiting challenges by using a diverse image library built across multiple projects coupled with staff expertise and onsite computing resources. We are working toward a fully automated SPI processing system and also are moving to develop CVPR analytical tools for other imaging platforms and data sets.

02 PETROLEUM↗
DOE OSTI2024

Phosphonate functionalized magnetic mesoporous silica for rare earth element recovery from citrate assisted solid waste extracts

The growing high demand of rare earth elements (REEs) has prompted extensive research on REE recovery from waste streams. This study reports the synthesis and evaluation of phosphonate-functionalized magnetic mesoporous silica (MMS-PP) for REE recovery from the acidic extracts of solid wastes. MMS-PP was synthesized using surfactant template and post-synthesis methods, and tested using simulated and real extraction solutions from citrate-assisted REE extracts from municipal solid waste incineration (MSWI) ash. The organic-inorganic hybrid MMS-PP was evaluated for La recovery from 50 mM simulated citrate extract, with the adsorption capacity of 13.5 mg/g compared to ∼2.5 mg/g for non-functionalized MMS. The MMS-PP material exhibited fast adsorption within 10 min, good La selectivity against competing Na + and Ca 2+ , moderate selectivity against Al 3+ and Fe 3+ , high recyclability over multiple adsorption-desorption cycles, and equivalent efficiency for La, Ce, Nd, and Y recovery. Furthermore, the MMS-PP material also demonstrated 70–95% REE recovery from real citrate extracts of MSWI ash, and the spent MMS-PP material can be regenerated and reused for multiple cycles. Functionalized mesoporous materials in combination with organic-ligand assisted extraction can potentially provide a green, effective, and tunable solid-liquid separation platform for REE recovery from complex solid waste streams.

REE recovery↗
DOE OSTI2022

Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

Abstract Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including National Aeronautics and Space Administration’s Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low‐stature plants and very fine‐scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long‐term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi‐scale spectroscopy, from lab studies to satellites that enable frequent and continuous long‐term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Auxetic two-dimensional transition metal selenides and halides

Abstract Auxetic two-dimensional (2D) materials provide a promising platform for biomedicine, sensors, and many other applications at the nanoscale. In this work, utilizing a hypothesis-based data-driven approache, we identify multiple materials with remarkable in-plane auxetic behavior in a family of buckled monolayer 2D materials. These materials are transition metal selenides and transition metal halides with the stoichiometry MX (M = V, Cr, Mn, Fe, Co, Cu, Zn, Ag, and X = Se, Cl, Br, I). First-principles calculations reveal that the desirable auxetic behavior of these 2D compounds originates from the interplay between the buckled 2D structure and the weak metal–metal interaction determined by their electronic structures. We observe that the Poisson’s ratio is sensitive to magnetic order and the amount of uniaxial stress applied. A transition from positive Poisson’s ratio (PPR) to negative Poisson’s ratio (NPR) for a subgroup of MX compounds under large uniaxial stress is predicted. The work provides a guideline for the future design of 2D auxetic materials at the nanoscale.

2d materials↗
DOE OSTI2022

Determination of Multimode Motional Quantum States in a Trapped Ion System

Trapped atomic ions are a versatile platform for studying interactions between spins and bosons by coupling the internal states of the ions to their motion. Measurement of complex motional states with multiple modes is challenging, because all motional state populations can only be measured indirectly through the spin state of ions. Here we present a general method to determine the Fock state distributions and to reconstruct the density matrix of an arbitrary multimode motional state. Further, we experimentally verify the method using different entangled states of multiple radial modes in a five-ion chain. This method can be extended to any system with Jaynes-Cummings-type interactions.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗
DOE OSTI2025

G2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms Using Multi-Omics Data

Multi-omics data offers rich insights into complex traits across organisms, yet integrating and analyzing these datasets for phenotype prediction and marker discovery remains challenging. Researchers need accessible tools that combine deep learning, hyperparameter optimization, visualization, and downstream analysis in a unified web platform. To address this, we developed G2PDeep-v2, a web-based platform powered by deep learning for phenotype prediction and marker discovery from multi-omics data across a wide range of organisms, including humans and plants. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Diverse electronic landscape of the kagome metal YbTi 3 Bi 4

Kagome lattices have emerged as an ideal platform for exploring exotic quantum phenomena in materials. Here, we report the discovery of Ti-based kagome metal YbTi 3 Bi 4 which we characterize using angle-resolved photoemission spectroscopy (ARPES) and magneto-transport, in combination with density functional theory calculations. Our ARPES results reveal the complex fermiology of YbTi 3 Bi 4 and provide spectroscopic evidence of four flat bands. Our measurements also show the presence of multiple van Hove singularities originating from Ti 3d orbitals and a linearly-dispersing gapped Dirac-like bulk state at the $\overline{\text {K}}$ point in accord with our theoretical calculations. Our study establishes YbTi 3 Bi 4 as a platform for exploring exotic phases in the wider LnTi 3 Bi 4 (Ln = lanthanide) family of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par, Alpaka, and OpenMP/OpenACC, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.

Kwok, Ka Hei Martin↗
DOE OSTI2025

A scanning resonator for probing quantum coherent devices

Superconducting resonators with high quality factors are extremely sensitive detectors of the complex impedance of materials and devices coupled to them. This capability has been used to measure losses in multiple different materials and, in the case of circuit quantum electrodynamics (circuit QED), has been used to measure the coherent evolution of multiple different types of qubits. Here, we report on the implementation of a scanning resonator for probing quantum coherent devices. Our scanning setup enables tunable coherent coupling to systems of interest without the need for fabricating on-chip superconducting resonators. We measure the internal quality factor of our resonator sensor in the single-photon regime to be >104 and demonstrate capacitive imaging using our sensor with zeptoFarad sensitivity and micron spatial resolution at milliKelvin temperatures. We then use our setup to characterize the energy spectrum and coherence times of multiple transmon qubits with no on-chip readout circuitry. Our experiments introduce a new tool for using circuit QED to measure existing and proposed qubit platforms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

NucMesh: nuclear reactor geometry creation and mesh generation module in NEMoSys

NucMesh is a parameterized geometry and mesh generator for nuclear reactors developed within the Nuclear Energy Modeling System NEMoSys at Illinois Rocstar. NEMoSys is a platform developed for mesh generation, adaptive refinement, and solution verification. NucMesh is implemented to be generalized and extensible with a robust computer aided design engine and multiple mesh generation algorithms for unstructured triangular, quad-dominant, and structured quadrilateral meshing. In this paper, we present the geometric and meshing features of NucMesh. Geometrically objects are constructed bottom-to-top and overlaps are addressed automatically. A sophisticated object tracking algorithm prevents data from being lost for segmented objects. We discuss the primitive objects of circle and polygons that constitute the module and show how they are used with example inputs. Arrays of primitives and arrays of arrays are utilized to build large assemblies of objects. The concept of saved objects is discussed to demonstrate how repetitive objects can be reused easily and augmented in place. Three dimensional meshes can be obtained through mesh extrusion where all materials and side sets are extended to three dimensions. We show that side sets can be defined nearly anywhere within the geometry and can then be applied to the mesh. Finally, example reactor meshes are demonstrated for the Idaho National Laboratory Advanced Test Reactor and Los Alamos National Lab Empire reactor, both of which use control drums that NucMesh handles easily. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par and Alpaka, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.

Kwok, Ka Martin↗
DOE OSTI2020

Open-Source Framework for Data Storage and Visualization of Real-Time Experiments

Digital real time simulators (DRTS) are increasingly being used for the evaluation of power hardware and controller hardware in the laboratory prior to field deployment. Although DRTS are capable of simulating large models in real-time, it is challenging to visualize the results of large models without overdrawing or confounding the viewer. This paper provides an open-source framework for users to visualize their DRTS-based hardware-in-the-loop (HIL) experimental results in real-time. This proposed framework can be used by experimental test beds that can push data through an internet protocol based network. The proposed framework includes three main components. First, it includes the DRTS that generates and pushes the data to a relay. Second, it includes an application that serves multiple purposes, from data storage, testing, and translation of the data to a publisher/subscriber protocol. Finally, it includes libraries and applications that can be used to visualize the data by subscribing to the relay. This framework is available in open source, and it is tested using the HIL platform developed for testing advanced distribution management systems.

advanced distribution management systems↗
DOE OSTI2020

Single-Step Conversion of Ethanol to n-Butene over Ag-ZrO2/SiO2 Catalysts

Ethanol is a promising platform molecule for production of a variety of fuels and chemicals. Of particular interest is producing middle distillate fuels (i.e., jet and diesel blendstock) from renewable ethanol feedstock. State-of-the-art alcohol-to-jet technology requires multiple process steps based on catalytic dehydration of ethanol to form ethylene, followed by sometimes a multi-step oligomerization, and then hydrotreatment and distillation. Here we report on a new catalytic route in which ethanol is directly converted to n-butene (1- and 2-butene mixtures) over Ag-ZrO2/SBA-16, thus offering the potential for a reduction in the number of required processing steps versus conventional alcohol-to-jet technology. This catalyst system provides the balanced metal and Lewis acid sites required to selectively facilitate a cascading sequence of reactions that includes dehydrogenation, aldol condensation, Meerwein–Ponndorf–Verley reduction, dehydration, and hydrogenation. High conversion and selectivity toward either n-butene or 1,3-butadiene is achieved by tuning the hydrogen feed partial pressure and other process/catalyst parameters. With sufficient hydrogen partial pressure 1,3-butadiene is completely and selectively hydrogenated to form n-butene. The reaction mechanism was elucidated through operando-nuclear magnetic resonance investigations coupled with reactivity measurements. Combined experimental-computational investigation reveals how changes in silver and zirconium composition and the silver oxidation state affects reactivity under controlled hydrogen partial pressures and after prolonged run times. Finally, catalyst effectiveness also was demonstrated when using wet ethanol feed, thus highlighting process flexibility in terms of feedstock purity requirements. This work was financially supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, and was performed at the Pacific Northwest National Laboratory (PNNL) under Contract No. DE-AC05-76RL01830 and the National Renewable Energy Laboratory under Contract No. DE-AC36- 08GO28308. Part of the work conducted by S. A. Akhade was performed under the auspices of the U.S. DOE at Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344. This work was partly supported through the PNNL-WSU Distinguished Graduate Research Program for ADW. NMR and XPS experiments were performed using EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research

Dagle, Vanessa↗
DOE OSTI2024

Intermediate Scale Test Platform Conceptual Design and Hybrid Resin Performance Evaluation Recommendations

Recent laboratory evaluations demonstrated that further testing of alternative hybrid resins is warranted for their potential use as part of the 200 West Area (200W) pump-and-treat (P&T) remedy. 200W P&T has been treating technetium (Tc) since 2012 and uranium (U) since 2015 via two separate ion exchange (IX) treatment units: a single treatment train (i.e., a three-vessel system) for the removal of uranium utilizing a gel strong base anion (SBA) exchange resin, Dowex 21K; and two parallel treatment trains for the removal technetium using a gel SBA resin, Purolite® A532E. While these resins have been removing these contaminants effectively, new hybrid resins can provide additional benefits of further optimizing the IX treatment unit capacities and addressing additional treatment needs (e.g., Cr(VI)) by removing multiple contaminants simultaneously and/or through specific design configurations. Some of these resins also demonstrated some level of removal for I-129 which would be significant for expansion of the P&T into the 200 East Area. This document provides design recommendations for an intermediate scale test platform (ISTP) to evaluate alternative hybrid IX resin performance under conditions approaching field scale and using 200W P&T process groundwater. Results from the ISTP will provide the technical basis required to inform alternative IX resin selection in future 200W P&T facility operations where the treatment of multiple contaminants of concern may be required.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

FORTE Measurements of Global Lightning Altitudes

While multiple lightning detection systems provide geographical locations of lightning events across the globe, robust lightning altitude measurements on a global scale have proven elusive. Space-based platforms have an advantageous viewing geometry for making these measurements, but prior studies with the Fast On-orbit Recording of Transient Events (FORTE) satellite were limited to a few thousand events. In this study, we apply the same technique for calculating source altitude from the previous efforts to a large catalog of hundreds of thousands of global FORTE in-cloud lightning events that were coincident with flashes geolocated by its lightning imager between 1997 and 2003. We use this new data set to document global variations in lightning altitude. As in previous studies, we find that FORTE primarily resolves sources from the upper (positive) charge layer at ~11 km altitude in normal thunderstorms. However, sources are also recorded from other charge layers in the storm and from leaders developing between layers. In particular, we note a pronounced increase in source altitude in the first 20 ms of FORTE flashes from the negative leader developing upward into the upper positive charge layer. Regions known for wintertime and/or stratiform lightning have increased contributions from low-altitude sources, while tropical regions particularly around Panama and the Maritime Continent have the greatest concentrations of high-altitude sources.

54 ENVIRONMENTAL SCIENCES↗