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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

IEA Wind Task 26. Wind Technology, Cost and Performance Trends in Denmark, Germany, Ireland, Norway, the European Union, and the United States. 2007 - 2012

This report builds from a similar previous analysis (Schwabe et al., 2011) exploring the differences in cost of wind energy in 2008 among countries participating in IEA Wind Task 26 at that time. The levelized cost of energy (LCOE) is a widely recognized metric for understanding how technology, capital investment, operations, and financing impact the life-cycle cost of building and operating a wind plant. Schwabe et al. (2011) apply a spreadsheet-based cash flow model developed by the Energy Research Centre of the Netherlands (ECN) to estimate LCOE. This model is a detailed, discounted cash flow model used to represent the various cost structures in each of the participating countries from the perspective of a financial investor in a domestic wind energy project. This model is used for the present analysis as well, and comparisons are made for those countries who contributed to both reports, Denmark, Germany, and the United States.

17 WIND ENERGY↗
DOE OSTI2015

GADRAS Isotope ID User's Manual for Analysis of Gamma-Ray Measurements and API for Linux and Android

Isotope identification algorithms that are contained in the Gamma Detector Response and Analysis Software (GADRAS) can be used for real-time stationary measurement and search applications on platforms operating under Linux or Android operating systems. Since the background radiation can vary considerably due to variations in naturally-occurring radioactive materials (NORM), spectral algorithms can be substantially more sensitive to threat materials than search algorithms based strictly on count rate. Specific isotopes or interest can be designated for the search algorithm, which permits suppression of alarms for non-threatening sources, such as such as medical radionuclides. The same isotope identification algorithms that are used for search applications can also be used to process static measurements. The isotope identification algorithms follow the same protocols as those used by the Windows version of GADRAS, so files that are created under the Windows interface can be copied directly to processors on fielded sensors. The analysis algorithms contain provisions for gain adjustment and energy linearization, which enables direct processing of spectra as they are recorded by multichannel analyzers. Gain compensation is performed by utilizing photo-peaks in background spectra. Incorporation of this energy calibration tasks into the analysis algorithm also eliminates one of the more difficult challenges associated with development of radiation detection equipment.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Parallel Reconstruction of Three Dimensional Magnetohydrodynamic Equilibria in Plasma Confinement Devices

Fast, accurate three dimensional reconstructions of plasma equilibria, crucial for physics interpretation of fusion data generated within confinement devices like stellarators/ tokamaks, are computationally very expensive and routinely require days, even weeks, to complete using serial approaches. Here, we present a parallel implementation of the three dimensional plasma reconstruction code, V3FIT. A formal analysis to identify the performance bottlenecks and scalability limits of this new parallel implementation, which combines both task and data parallelism, is presented. The theoretical findings are supported by empirical performance results on several thousands of processor cores of a Cray XC30 supercomputer. Parallel V3FIT is shown to deliver over 40X speedup, enabling fusion scientists to carry out three dimensional plasma equilibrium reconstructions at unprecedented scales in only a few hours (instead of in days/weeks) for the first time.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

ExaWorks software development kit: a robust and scalable collection of interoperable workflows technologies

Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different types of tasks (e.g., simulation, analysis, and learning) that need to be mapped, scheduled, and launched on different computing. That requires a software stack that enables users to code their workflows and automate resource management and workflow execution. Currently, there are many workflow technologies with diverse levels of robustness and capabilities, and users face difficult choices of software that can effectively and efficiently support their use cases on HPC machines, especially when considering the latest exascale platforms. We contributed to addressing this issue by developing the ExaWorks Software Development Kit (SDK). The SDK is a curated collection of workflow technologies engineered following current best practices and specifically designed to work on HPC platforms. We present our experience with (1) curating those technologies, (2) integrating them to provide users with new capabilities, (3) developing a continuous integration platform to test the SDK on DOE HPC platforms, (4) designing a dashboard to publish the results of those tests, and (5) devising an innovative documentation platform to help users to use those technologies. Our experience details the requirements and the best practices needed to curate workflow technologies, and it also serves as a blueprint for the capabilities and services that DOE will have to offer to support a variety of scientific heterogeneous workflows on the newly available exascale HPC platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

MATBOX (Microstructure Analysis Toolbox) [SWR-20-76]

MATBOX is a MATLAB application for performing various microstructure-related tasks including microstructure numerical generation, image filtering and microstructure segmentation, microstructure characterization, result three-dimensional visualization and result correlation, and microstructure meshing. MATBOX was originally developed to analyze electrode microstructures for lithium ion batteries; however, the algorithms provided by the toolbox are widely applicable to other heterogeneous materials. The toolbox provides a user-friendly experience thanks to a Graphic-User Interface.

Usseglio Viretta, Francois↗
DOE OSTI2022

Syracuse University Industrial Assessment Center (Final Report)

Syracuse University Industrial Assessment Center (SU-IAC) project is focused on the two primary objectives: (i) educate undergraduate and graduate students in engineering and associated disciplines on the concepts of energy sustainability and to provide hands-on training by performing energy assessment at industrial facilities to become future energy efficiency experts, and (ii) offer energy assessments to small and mid-sized manufacturing companies in SU-IAC 2 the greater New York State region, perform analytical engineering work in support of assessment recommendations, and to report the results of those analyses to the client companies, to DOE, and to Field office personnel Through this project period, SU-IAC successfully trained 77 engineering students who were undergraduate or graduate students at Syracuse University College of Engineering, or at SU- IAC’s satellite center at Clarkson University. Each of these students received comprehensive ‘in-class’ technical training, safety training, and hands-on field training. The students performed various tasks related to energy assessment at the manufacturing facilities, including billing analysis, energy use reduction recommendations, and report preparation. Through this project period, a total of 34 students have completed all requirements of the SU-IAC program and have received the US DoE Certificate on Energy Efficiency, with the remaining students continuing at various stages of field work or training. During this project period, due to Covid-related measures in New York State, Syracuse University campus and most of the manufacturing facilities were completely closed or were under severe restrictions for access, for an extended period from March 2020 through April 2021. The closures affected our project execution, with the project Sponsor offering structured relief to carry-out a modified project during the affected budget periods. During this project period, SU-IAC student teams, led by its Director, completed a total of 77 energy assessments at small- and medium-scale manufacturing facilities located in New York State. For these facilities, SU-IAC prepared and presented details for 569 assessment recommendations (ARs). The facilities implemented a total of 258 of these ARs, realizing annual average energy savings in the range of $\$ $3,300 to $\$ $55,000 per facility. Cumulatively, these facilities have reduced their “first year after implementation” energy use by a total of 4.37 million kwh, and fuel use by a total of 27.7 billion BTU. SU-IAC project has successfully met the two primary objectives (and the associated sub objectives) and helped the manufacturing facilities in New York State achieve quantitative energy use reductions as a result of this project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Exometabolomic Analysis of Cross-Feeding Metabolites

Microbial consortia have the potential to perform complex, industrially important tasks. The design of microbial consortia requires knowledge of the substrate preferences and metabolic outputs of each member, to allow understanding of potential interactions such as competition and beneficial metabolic exchange. Here, we used exometabolite profiling to follow the resource processing by a microbial co-culture of two biotechnologically relevant microbes, the bacterial cellulose degrader Cellulomonas fimi , and the oleaginous yeast Yarrowia lipolytica . We characterized the substrate preferences of the two strains on compounds typically found in lignocellulose hydrolysates. This allowed prediction that specific sugars resulting from hemicellulose polysaccharide degradation by C. fimi may serve as a cross-feeding metabolites to Y. lipolytica in co-culture. We also showed that products of ionic liquid-treated switchgrass lignocellulose degradation by C. fimi were channeled to Y. lipolytica in a co-culture. Additionally, we observed metabolites, such as shikimic acid accumulating in the co-culture supernatants, suggesting the potential for producing interesting co-products. Insights gained from characterizing the exometabolite profiles of individual and co-cultures of the two strains can help to refine this interaction, and guide strategies for making this an industrially viable co-culture to produce valuable products from lignocellulose material.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Characterizing the Performance of Executing Many-tasks on Summit

Many scientific workloads are comprised of many tasks, where each task is an independent simulation or analysis of data. The execution of millions of tasks on heterogeneous HPC platforms requires scalable dynamic resource management and multi-level scheduling. RADICAL-Pilot (RP) -- an implementation of the Pilot abstraction, addresses these challenges and serves as an effective runtime system to execute workloads comprised of many tasks. In this paper, we characterize the performance of executing many tasks using RP when interfaced with JSM and PRRTE on Summit: RP is responsible for resource management and task scheduling on acquired resource; JSM or PRRTE enact the placement of launching of scheduled tasks. Our experiments provide lower bounds on the performance of RP when integrated with JSM and PRRTE. Specifically, for workloads comprised of homogeneous single-core, 15 minutes-long tasks we find that: PRRTE scales better than JSM for > O(1000) tasks; PRRTE overheads are negligible; and PRRTE supports optimizations that lower the impact of overheads and enable resource utilization of 63% when executing O(16K), 1-core tasks over 404 compute nodes.

Matteo, Turilli↗
DOE OSTI2019

Plasma-Based Ambient Desorption/Ionization Mass Spectrometry for the Analysis of Liquid Crystals Employed in Display Devices

Liquid-crystal displays (LCDs) are the most frequently used display technology worldwide these days. Due to the rather complex manufacturing process and purity requirements for the chemicals used, quality control and display failure analysis are important analytical tasks. Currently, the state-of-the-art techniques (e.g., high-performance liquid chromatography (HPLC), gas chromatography (GC) coupled to mass spectrometry (MS), time-of-flight secondary ion mass spectrometry (TOF-SIMS), or high-resolution microscopy) are costly and time-consuming. Hence, a new pathway to precisely analyze liquid-crystalline materials and LCDs in their native state is reported. A new approach for direct analysis via plasma-based ambient desorption/ionization mass spectrometry (ADI-MS) offers an inexpensive and faster alternative. In this study, direct analysis in real time (DART), the low-temperature plasma (LTP) probe, and flowing atmospheric-pressure afterglow (FAPA) ADI sources coupled to high-resolution mass spectrometry (HR-MS) are compared based on their capabilities and performance for liquid-crystal analysis. These sources enable direct analyte desorption from a sample surface at ambient conditions and ionize the vaporized analyte molecules in a subsequent step. Primarily, the ionization capabilities of the three ADI sources are compared for individual liquid-crystal standards, mixtures of liquid crystals (LCs), and complex liquid crystal/additive mixtures applied in commercially available LCDs. Furthermore, direct surface analysis from a glass substrate is also performed with ADI-MS to compare their applicability to this type of sample matrix. .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Enhancing Electron Microscopy Image Classification Using Data Augmentation

Manual labeling for machine learning tasks such as image classification is tedious and labor-intensive; as a result, scientific datasets suitable for deep learning applications are scarce and limited. While data augmentation techniques have shown promise for extending image datasets, very little work has been done to understand the impact of combining multiple augmentation methods sequentially or the limits of their effectiveness when combined. Our work addresses this gap by examining how standard and combinatorial data augmentation affects the performance of machine learning models when trained on small datasets for label classification tasks. For our analysis, we generate single, double and quadruple-augmented datasets for a microscopy image classification task using six standard augmentation methods, and compare the resultant improvements observed in binary classification accuracy with three standard image classification models (DenseNet169, MobileNetV2, ResNet101V2). Our experiments show a non-monotonic relationship between the number of simultaneous augmentation methods and classification accuracy, indicating that there is a trade-off between the degree of augmentation and the model performance. These findings suggest that the optimal number of augmentation methods will vary by domain and use case. We also find that the order in which augmentation methods are applied to a limited dataset matters when combining augmentation schemes, with our use case showing performance differences up to 2.6% when the augmentation order is reversed for double-augmented datasets. Our work offers insights to the limits of data augmentation when working on image classification tasks with limited datasets.

Welsman, Jordan A↗
DOE OSTI2025

Exxon Mobil-NETL Testing of DAC Materials

This joint initiative is aimed at extending our understanding of real DAC testing conditions at the NETL DAC testbed facility. Of particular focus will be: • Small Scale testing of powdered and formulated materials (supplied by ExxonMobil) to evaluate various performance metrics under DAC process cycles • Pilot scale testing of formulated materials at larger scales ExxonMobil will work with NETL to shake down equipment, validate testing methods, and define best practices for data analysis. Three tasks are proposed: 1. Validation of multi-cycle test data on powdered and formulated materials to benchmark various performance metrics of these materials. 2. Steam regeneration of powdered samples at small scale to define baseline performance under commercially relevant conditions. 3. Pilot scale testing of larger formulated materials to test commercially relevant samples under actual process cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Performance, Cost, and Financial Parameters of Geothermal District Heating Systems for Market Penetration Modeling under Various Scenarios

Geothermal district heating (GDH) systems have limited penetration in the U.S., with an estimated installed capacity of only 100 MWth for a total of 21 sites. We see higher deployment in other regions, for example, in Europe with an installed capacity of more than 4,700 MWth for 257 GDH sites. The U.S. Department of Energy Geothermal Vision (GeoVision) Study is currently looking at the potential to increase the deployment in the U.S. and to understand the impact of this increased deployment. This paper reviews 31 performance, cost, and financial parameters as input for numerical simulations describing GDH system deployment in support of the GeoVision effort. The focus is on GDH systems using hydrothermal and Enhanced Geothermal System resources in the U.S.; ground-source heat pumps and heat-to-electricity conversion technology were excluded. Parameters investigated include 1) capital and operation and maintenance costs for both subsurface and surface equipment; 2) performance factors such as resource recovery factors, well flow rates, and system efficiencies; and 3) financial parameters such as inflation, interest, and tax rates. Current values as well as potential future improved values under various scenarios are presented. Sources of data considered include academic and popular literature, software tools such as GETEM and GEOPHIRES, industry interviews, and analysis conducted by other task forces for the GeoVision Study, e.g., on the drilling costs and reservoir performance.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗
DOE OSTI2022

Performance of an Astrophysical Radiation Hydrodynamics Code under Scalable Vector Extension Optimization

We present results of a performance study of an astrophysical radiation hydrodynamics code, V2D, on the Arm-based A64FX processor developed by Fujitsu. The code solves sparse linear systems, a task for which the A64FX architecture should be well suited. Here, we performed the performance analysis study on Ookami, an Apollo 80 platform utilizing the A64FX processor. We explored several compilers and performance anal-ysis packages and found the code did not perform as expected under scalable vector extension optimization, suggesting that a “deeper dive” into analyzing the code is worthwhile. However, a simple driver program that exercised basic sparse linear algebra routines used by V2D did show significant speedup with the use of the scalable vector extension optimization. We present the initial results from the study which used V2D on a relatively simple test problem that emphasized the repeated solution of sparse linear systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Smart, Connected Manufactured Housing Solutions through High-Performance Design. Final CRADA report

This report focuses on HVAC, domestic hot water, and miscellaneous electric loads via voluntary opportunities that may arise from partnerships with utilities, as well as future US Environmental Protection Agency ENERGY STAR and DOE Zero Energy Ready Manufactured Home programs. Phase I of this project has begun the technical dialogue toward developing an implementation plan among DOE’s Oak Ridge National Laboratory, Clayton Manufactured Homes, and US Department of Housing and Urban Development Code manufactured housing stakeholders. These activities have focused on delivering high-performance design through integration of technology. Project tasks include the following: Identifying baseline energy analysis resources opportunities from a variety of DOE and utility stakeholders; Developing a smart home and business solution by leveraging existing utility programs working with Smart Homes Partners resources such as ACE IoT Solutions, Google Nest, and Ecobee; Developing improved smarter ventilation systems with industry ventilation partners such as the Madison Group; Developing improved building science QA/QC testing equipment with manufacturers such as The Energy Conservatory, and supporting other feasible concepts vetted under DOE’s Advanced Buildings Collaborative with Slipstream, reinventing HVAC in manufactured housing; and, Developing smart home short- and long-term viable technical solutions in coordination with Clayton Manufactured Homes in new and/or revitalized community scales for future Phase II prototype demonstrations, which may include design (and perhaps construction) of single-section homes targeting rental property developers and multi-section homes targeting low- to middle-income affordable housing community developers Given the ongoing US Department of Energy (DOE) rulemaking activities, baseline energy analysis assessments of envelope prescriptive and Uo (i.e., the overall thermal energy efficiency of the home in British thermal units per square foot of exterior heat loss/gain surfaces) measures were removed from the scope of Phase I of this project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

ECAR-7210 Rev 0 Steady-state Thermo-mechanical Analysis of the MARVEL Fuel Bundle

This document reports the steady-state thermal-hydraulic and static mechanical analyses of the MARVEL micro-reactor core. The objective of this work is to investigate whether fuel-to-fuel contact could occur due to the thermal deformation of the fuel cladding during the normal operation and to assess the resulting impact on the peak cladding temperatures (PCT) if contact occurs. The scope of the work consists of three computational tasks. First, a conjugate heat transfer analysis using computational fluid dynamics (CFD) model was performed to evaluate the PCTs during normal operating conditions. Second, a finite element analysis (FEA) using the cladding temperature field obtained from the CFD analysis was performed to evaluate the thermal deformation of cladding and determine the occurrence of fuel-to-fuel contact. Finally, a CFD analysis of FEA-informed fuel-to-fuel gap (represented by a conservative 0.05 mm uniform gap) was performed to evaluate whether the PCT exceeds the safety criteria.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗