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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Proxy Applications for Converged Workloads: DMC LDRD Initiative

Modern scientific applications are complicated and require coordination of several components. Proxy application driven software-hardware co-design plays a vital role in driving innovation among the developments of applications, software infrastructure and hardware architecture. Proxy applications are self-contained and simplified codes that are intended to model the performance-critical computations within applications. Applications executing on modern High Performance Computing (HPC) systems are susceptible to network congestion, insufficient memory bandwidth within and across compute nodes, and inadvertent loss of performance due to bugs and unoptimized programming models. Modern numerical simulations and machine learning models play a critical role in studying physical phenomenon under myriad uncertainties. Such applications often exhibit irregular computation and memory accesses at specific regions of the application code, which can contribute to various performance bottlenecks at scale. To mitigate such issues and prepare the next generation hardware for a variety of computation and data movement contingencies, a well-known practice is to consider "proxy" applications as representative motifs for various classes of scientific applications. While there is disagreement in the HPC community on the mechanisms of construction of the proxy applications, there is a strong consensus on their positive impact in co-design. Proxy Applications for Converged Workloads (PACER) is about facilitating software-hardware co-design through proxy applications with the goal of improving the performance of converged science workflows on heterogeneous systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

MATBOX, an Open-Source Microstructure Analysis Toolbox for Meshing, Generation, Segmentation, and Characterization of 3D Heterogenous Volumes

Battery performance is strongly correlated with electrode microstructural properties. To account for its impact, lithium-ion battery (LIB) models either abstract the microstructural heterogeneity of composite electrodes using effective macroscopic properties (macro- or meso- scale models) or directly solve the system of equations on the microstructure geometry or mesh (microstructure-scale models). Therefore, to be adequate, both families of models require information from the microstructure geometry, which can be provided by the numerical tool presented in this work. MATBOX is a MATLAB open-source application [1] developed by NREL for performing various microstructure-related tasks including microstructure numerical generation, image filtering and microstructure segmentation, microstructure characterization and correlation, visualization, and microstructure meshing. MATBOX was originally developed for the analysis of LIB electrode microstructures; however, the algorithms provided by the toolbox are widely applicable to other heterogeneous materials. The toolbox provides a user-friendly experience thanks to a Graphical-User Interface, requires no coding by the user, and is well documented. This presentation will illustrate various MATBOX features for the characterization of a LIB electrode, including a fully automated Representative Volume Element (RVE) analysis, the numerical generation of complex 'virtual' microstructure, including dual-layer electrodes and carbon-binder additive phase, and the meshing of a complex NMC/graphite full cell microstructure suitable for 3D finite-element modeling. Other modules (segmentation, visualization, and correlation) will be briefly presented. Thanks to its modular, open-source approach, MATBOX can easily incorporate third-party algorithms to eventually build a standard in the field that will benefit the whole scientific community. Effective diffusion coefficient [2], additive phase numerical generation [3], and meshing [4] third-party algorithms have been already integrated in the toolbox with more to come.

DIRECT ENERGY CONVERSION,MATHEMATICS AND COMPUTING↗
DOE OSTI2020

National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions

A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts’ opinion on the development and application of multi-omic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular and functional aberrations within the lesion and within individual cells. Additionally, single-cell proteomic data will be essential for the establishment of new tools that with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included: • The best way/s to analyze single-cells from fresh and preserved tissue • Detection and analysis of secreted molecules and from single cells, especially from a tissue slice • Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment • Multi-omic integration of data to support and inform proteomic measurements • Subcellular organelles – identifying abnormal structure, function, distribution and location within individual premalignant and malignant cells • How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post translational modifications (PTM) • The depth of coverage measured concurrently using single-cell techniques • Quantitation - absolute or semiquantitative? • Single methodology or multiplexed combinations? • Application of analytical methods for identification of biologically significant subsets • Data visualization of N-dimensional datasets • How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME) • Associations between intrinsic cellular processes and extrinsic stimuli • How to predict cellular responses to stress inducing stimuli • Identification of new markers for prediction of progression from precursor, benign and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells • Identification of new targets for immunoprevention or immunotherapy – identification of neoantigens and surfactome of individual cells within a lesion

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Potential of the Julia Programming Language for High Energy Physics Computing

Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

NIRP Core Software Suite v.5.1

SAND2023-06610O The Nuclear Incident Response Program (NIRP) Core Software Suite is a set of code that supports multiple applications. It includes miscellaneous base code for data objects, mathematic equations, and user interface components that are used for developing large applications, including those used for data management. Among the types of applications: • Analyst Manager is used for managing contact information, which is also often included in reports and identifies the owners of calculations. • Radionuclide Viewer provides access to the Dose Coefficient File Package (DCFPAK) radiological data, and it complements the Mixture Manager tool. • Mixture Manager creates and manages radionuclides mixtures that are commonly used in other applications. • High Explosive Manager manages explosives and properties. • Chart Viewer is used for data charts such as meteorology charts and other charts specific to data needs. • Unit Converter converts numeric values to/from different units. This package includes object-oriented base code such as data objects describing the date, time, and location of an event; weather conditions; data types that support predefined ranges of values; a unit library, and others. The package also includes mathematic equations such as determining the distance between two points; erf functions; factorial functions; incidence fractions of probit curves; and others. Other features are customized user interface components for text fields; unit selectors (combo boxes); menus; panels; bug-tracking; and automated checks for software updates. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Collins, Ian↗
DOE OSTI2023

Advances in imaging, scattering, spectroscopy, and machine learning-aided approaches for multiscale characterization of cementitious systems

Recent progress in methods used in the multiscale characterization of cementitious systems is reviewed, focusing on advances in imaging, scattering, and spectroscopy. The review includes relevant applications and developments in machine learning and other data analytics approaches to enhance characterization. Developments in imaging using light and electron microscopy as well as x-ray (i.e., from synchrotron) methods are summarized. Updates include scanning electron microscopy (SEM), transmission electron microscopy (TEM), tomography, and holography. A critical overview of spectroscopy (e.g., MAS NMR, Raman) and scattering (e.g., neutron, x-ray, synchrotron x-ray) methods is provided, and the intersection of these with imaging is developed (e.g., Raman imaging). Additionally, the paper summarizes recent developments in and implementations of state-of-the-art machine-learning algorithms and data analytics methods for automated, systematic, and/or quantitative analyses of image data sets. The review considers but is not limited to the application of these methods for investigating the hydration and microstructure development of cement phases, low-carbon-footprint cements (e.g., limestone calcined clay cements, LC3), environmental interactions (e.g., ASR) and model systems. Finally, the present work provides a critical presentation of advances in characterization methods that link together the composition and multiscale structure of cementitious materials.

42 ENGINEERING↗
DOE OSTI2026

Development of thermal joints for conduction-cooling applications

Abstract In support of the development of conduction-cooled superconducting radiofrequency (SRF) niobium cavities for use in continuous-wave linear accelerators, an experimental study of thermal contact resistance was performed on bolted joints using high-purity niobium, aluminium, and copper along with Apiezon N grease and indium foil as interfacial materials. The geometry of each joint investigated aims at replicating the design adopted in conduction-cooled SRF cavities currently under development at Jefferson Lab. The materials’ thermal conductivity and the joints’ thermal resistance were measured in the temperature range of 3.5 - 10 K. The results showed that the low thermal contact resistance of 2-4 K·cm 2 /W at 4.3 K achieved with Al-In-Nb and Al-Apiezon N-Cu joints should allow the operation of conduction-cooled SRF cavities at a targeted accelerating gradient of ∼10 MV/m.

Lewis, J. [Old Dominion University]↗
DOE OSTI2021

Interactive Web Application for Traffic Simulation Data Management and Visualization

As traffic simulation software becomes more effective for realistically simulating and analyzing traffic dynamics and vehicle interactions on the mesoscopic and microscopic level, the management, dissemination, and collaborative visualization of traffic simulation results produced by individual transportation planners presents a significant challenge. Existing online content management systems have a very limited capability in allowing users to query specific traffic simulation scenarios and geospatially visualize simulation results through shareable and interactive web interfaces. This paper presents a web-based application for promoting the archiving, sharing, and visualization of large-scale traffic simulation outputs. The application is developed to enhance cyber-physical controls, communications, and public education for collaborative transportation planning. Unique features of the web application include: (a) allowing users to upload their new traffic simulation scenarios (parameters and outputs), as well as search existing scenarios using easily accessible interfaces; (b) optimizing simulation output files with heterogeneous data formats and projected coordinate systems for web-based storage and management using a scalable and searchable data/metadata standard; (c) standardizing user-uploaded simulation outputs using web interfaces and data processing libraries with parallel computing capacity; and (d) providing shareable web visual interfaces for visualizing the traffic flow and signal information stored in simulation outputs (e.g., regional traffic patterns and individual vehicle interactions) and visually comparing multiple simulation outputs both spatially and temporally. Furthermore, the paper presents the conceptual design and implementation of this application, and demonstrates the application’s performance for sharing, comparing, and visualizing simulation outputs from VISSIM and SUMO, two commonly used traffic simulation software programs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Final Report DE-SC0020773

The DOE/NNSA sought development of innovative portable neutron/x-ray cameras for imaging and, when coupled with a portable source, also for radiography. This project focused on developing such a device that promises to have excellent resolution, while also providing neutron/gamma separation. We developed an Application Specific Integrated Circuit (ASIC) to be employed in conjunction with existing sensor technologies and modern assembly techniques. The concepts developed were a combination of new novel techniques and an innovative implementation of existing methods boosted by technological advances in ASIC performance.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal patterning

Abstract Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and limitations of published tools. While most models show human-level performance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental, or developmental variation. Other models can make predictions across greater phenotypic diversity and/or additional stomatal/epidermal traits, but require significantly greater time investment to generate ground-truth data. We discuss the challenges and opportunities presented by AI/ML-enabled computer vision analysis, and make recommendations for future work to advance accelerated stomatal phenotyping.

Plant Sciences↗
DOE OSTI2020

General Analysis of Data Collected from DOE Lidar Buoy Deployments Off Virginia and New Jersey

Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of offshore wind energy in the U.S. In general, each buoy is deployed for a year or more at a given location in order to capture at least a full annual cycle of weather conditions. The initial deployment for one buoy was off the coast of Virginia beginning in 2015, and the other buoy was first deployed off the coast of New Jersey beginning in 2016. Over the last two years, PNNL has had an opportunity to analyze the data collected during these first two deployments. This report describes a substantial analysis of data collected by the two lidar buoys operated off the coasts of Virginia and New Jersey. The centerpiece instrument for each buoy as deployed off Virginia and New Jersey is a lidar system, which is designed to measure the horizontal wind vector from approximately 40 m to 200 m above the sea surface with vertical resolution of 40 m. Since the initial deployments, the original lidars have been replaced with more powerful Leosphere 866 v2 systems. The analyses in this report will apply to the original Vindicator systems. In addition to the wind profiles from the lidars, the buoys collect near-surface measurements of wind speed and direction, air temperature, relative humidity, barometric pressure, and solar irradiance. Oceanographic variables measured include the two-dimensional wave spectrum, water temperature and conductivity, and ocean current vectors to a depth of 90 m. An assessment of overall data recovery and a basic analysis of the data collected was provided in a previous report This report substantially extends that analysis. The various sections describe the development and application of an inertial measurement unit (IMU) data recovery scheme for the New Jersey deployment; a climatological analysis of winds at hub height and at the surface together with thermodynamic variables measured at the surface for the full deployment periods; an analysis of oceanographic observations describe sea state; the development of a refinement for NOAA’s WaveWatch III model to allow its application to near-shore areas; a basic climatology of ocean currents observed from the buoys; an analysis of observed winds in the framework of Monin-Obukhov Similarity Theory; and the development and evaluation of techniques to extract turbulence intensity and turbulence kinetic energy from the lidars. The analyses contained in this report provide a great deal of new information about offshore conditions on the U.S. East Coast. In addition, the experience gained will inform both configurations and analysis of data from future deployments of these lidar buoy systems.

17 WIND ENERGY↗
DOE OSTI2022

Development of Large Scale Extrusion Deposition for Structural Applications

Large Scale Extrusion Deposition (LSED) is an evolving additive manufacturing (AM) technology that research, such as that taking place at Oak Ridge National Laboratory (ORNL) and companies like Local Motors, are continuing to utilize and develop new applications. A major LSED application of interest has been molds and tooling as it allows for much shorter production time and lower cost. However, interest has been expanding into using LSED for more structural applications due to more frequent use of high performing polymer composites as feedstock. The use of LSED for structural applications is of particular interest to Local Motors as it is currently being used to create commercially viable, energy efficient electric vehicles. LSED offers a unique opportunity when compared to traditional manufacturing methods as it can significantly reduce the number of components necessary, while decreasing embodied energy and carbon emissions. Even with these benefits however, it is important that LSED is properly understood from a structural aspect as this field has not been as heavily researched as tooling. To ensure a high level of safety and repeatability is an essential responsibility of a manufacturer whose products are structural in nature. As such it is important to understand the materials and LSED process to make structural objects that the manufacturer can be confident in. The main goals of this project were to: develop and investigate materials that are of interest for structural LSED applications, further develop and understand the current machines used in LSED and develop simulations tools of LSED and the mechanical properties of the created structure. Material development focused on composite materials that have high mechanical properties and are stable in a variety of environments. The materials were tested to determine their as printed mechanical and thermal properties as these are necessary for simulations. Once the materials were investigated thoroughly, it allowed for simulations to be performed to compare to experimental data with simulations. Materials were also vetted to determine candidates for multi-material printing. The machine development focused around the areas of process monitoring, non-destructive evaluation, and quality control. Finally, the goal of the simulations was to develop a realistic model of printed structures, including in-process simulation and dynamic simulation. The routes to get to some of these goals and the depth in which they were investigated changed throughout the project due to personnel changes and the COVID-19 pandemic. This project resulted in many valuable results such as the development of an nondestructive evaluation (NDE) technique for interlayer defects, proof of simulation for warpage in simple parts, the development and utilization of a profilometer to monitor a print for defects or inconsistencies, thorough investigation of a material used commercially for structural LSED applications and valuable experimental data on the applicability and advantages of multi-material crush structures versus their neat counterparts by creation and testing of samples.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Photothermal alternative to device fabrication using atomic precision advanced manufacturing techniques

The attachment of dopant precursor molecules to depassivated areas of hydrogen-terminated silicon templated with a scanning tunneling microscope (STM) has been used to create electronic devices with sub-nanometer precision, typically for quantum physics demonstrations, and to dope silicon past the solid-solubility limit, with potential applications in microelectronics and plasmonics. However, this process, which we call atomic precision advanced manufacturing (APAM), currently lacks the throughput required to develop sophisticated applications because there is no proven scalable hydrogen lithography pathway. Here, we demonstrate and characterize an APAM device workflow where STM lithography has been replaced with photolithography. An ultraviolet laser is shown to locally heat silicon controllably above the temperature required for hydrogen depassivation. STM images indicate a narrow range of laser energy density where hydrogen has been depassivated, and the surface remains well-ordered. A model for photothermal heating of silicon predicts a local temperature which is consistent with atomic-scale STM images of the photo-patterned regions. Finally, a simple device made by exposing photo-depassivated silicon to phosphine is found to have a carrier density and mobility similar to that produced by similar devices patterned by STM.

42 ENGINEERING↗
DOE OSTI2023

Probing Nanocrystal Optical Properties in the Limit of Single Nanocrystals

The Bawendi lab has been at the forefront of the synthesis and characterization of the fundamental photo-physics of quantum confined systems for the past three decades. During this DoE project, our progress has been enabled by both our expertise in single nanocrystal (NC) photoluminescence (PL) spectroscopy as well as our extensive knowledge of the synthetic optimization of various NC semiconductors - all of this forming an efficient feedback loop between characterization and synthesis. Our research in this project has focused on the development and application of novel single emitter optical tools for the characterization of colloidal quantum dots (CQDs) which are semiconducting NCs with size-tunable quantum confined electronic structure. CQDs have had a significant commercial impact as display technologies. They are also commercially available as fluorescent biological markers. CQDs are being investigated as a potential material for photovoltaic or optical sensing applications and as emerging optical quantum materials. We have focused on the fundamental understanding of the optical properties of NCs through the development of novel spectroscopic methods at the single emitter level, including understanding how synthetic and environmental heterogeneities influence NC properties. A major advance in our research has been the development of novel photon resolved correlation spectroscopies that allow us to extract the physics of photo-excited single NCs with unprecedented time and spectral resolution from the photon stream of individual NCs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Progress and Challenges of Additive Manufacturing of Tungsten and Alloys as Plasma-Facing Materials

Tungsten (W) and W alloys are considered as primary candidates for plasma-facing components (PFCs) that must perform in severe environments in terms of temperature, neutron fluxes, plasma effects, and irradiation bombardment. These materials are notoriously difficult to produce using additive manufacturing (AM) methods due to issues inherent to these techniques. The progress on applying AM techniques to W-based PFC applications is reviewed and the technical issues in selected manufacturing methods are discussed in this review. Specifically, we focus on the recent development and applications of laser powder bed fusion (LPBF), electron beam melting (EBM), and direct energy deposition (DED) in W materials due to their abilities to preserve the properties of W as potential PFCs. Additionally, the existing literature on irradiation effects on W and W alloys is surveyed, with possible solutions to those issues therein addressed. Finally, the gaps in possible future research on additively manufactured W are identified and outlined.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An Adaptive-Mesh-Refinement Based Computational Tool for Simulating Catalysis at Mesoscale

In this work, we present a computational tool for mesoscale applications using open-source exascale- computing compatible adaptive-mesh-refinement (AMR) library, AMReX [2]. AMReX is software library that enables development of application solvers with block-structured Cartesian AMR. Our tool has capabilities to include realistic geometry representation, chemical species transport, reactions and thermodynamics that are critical for capturing mesoscale physics. A significant achievement is the ability of our solver to automatically import electron microscopy data in the form of a stereolithography (STL) or pixelated file format (mrc, tiff) without undergoing the tedious task of unstructured mesh generation. This feature allows for rapid simulation of catalyst particles with complex morphologies using an immersed-boundary formulation. The use of AMR allows for higher resolutions at catalyst surface interfaces, which in turn provides an accurate description of surface reactions and transport. Our solver uses a hybrid distributed and shared memory parallelism (OpenMP/GPU-based) with which strong scaling up to 10,000 processors for realistic catalyst particle simulations have been demonstrated.

BIOMASS FUELS,MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Slice-Write

SAND2022-1477 O Slice-Write is an extremely powerful toolpath generation technology for the extrusion 3D printing community. This application was developed as a standalone post-processor capable of interpreting and translating gcode output from Ultimaker Cura, an open-source slicing application for additive manufacturing, not designed or intended for direct ink write (DIW) printing. Slice-Write can convert output gcode from Cura into a format compatible with Aerotech-based motion control DIW printing platforms. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Roach, Devin↗