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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Kinematic analysis of in situ measurement during chemical mechanical planarization process

Chemical mechanical planarization (CMP) is the most widely used planarization technique in semiconductor manufacturing presently. With the aid of in situ measurement technology, CMP tools can achieve good performance and stable productivity. However, the in situ measurement has remained unexplored from a kinematic standpoint. The available related resources for the kinematic analysis are very limited due to the complexity and technical secret. In this paper, a comprehensive kinematic analysis of in situ measurement is provided, including the analysis model, the measurement trajectory, and the measurement time of each zone of wafer surface during the practical CMP process. In addition, a lot of numerical calculations are performed to study the influences of main parameters on the measurement trajectory and the measurement velocity variation of the probe during the measurement process. All the efforts are expected to improve the in situ measurement system and promote the advancement in CMP control system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Nanofluid of graphene-based amphiphilic Janus nanosheets for tertiary or enhanced oil recovery: High performance at low concentration

Significance Improving crude oil recovery by 1% worldwide would result in a huge amount of crude oil resources becoming available. However, the economic and environmental concerns are too serious to ignore when chemical methods (surfactants or polymers flooding, etc.) are used for an average 10–20% enhancement for tertiary oil recovery. Simple nanofluid (containing only nanoparticles) flooding at low concentration (0.01 wt % or less) is a promising alternative, but the efficiency is below 5% in a saline environment (2 wt % or higher NaCl content). We report a simple nanofluid of graphene-based Janus amphiphilic nanosheets for enhanced oil recovery with efficiency of about 15.2%, comparable to chemical methods, which is both economically and environmentally beneficial to the petroleum industry.

Luo, Dan↗
DOE OSTI2018

Uncertainty Quantification Study of CTF for the OECD/NEA LWR Uncertainty Analysis in Modeling Benchmark

This work describes the results of a quantitative uncertainty analysis of the thermal-hydraulic subchannel code for nuclear engineering applications, Coolant Boiling in Rod Arrays-Three Field (COBRA-TF). CTF is used, which is a version of COBRA-TF developed in cooperation between the Consortium for Advanced Simulation of Light Water Reactors and North Carolina State University. Four steady-state cases from Phase II Exercise 3 of the Organisation for Economic Co-operation and Development/Nuclear Energy Agency Light Water Reactor Uncertainty Analysis in Modeling (UAM) Benchmark are analyzed using the statistical analysis tool, Design Analysis Kit for Optimization and Terascale Applications (Dakota). The input parameters include boundary condition, geometry, and modeling uncertainties, which are selected using a sensitivity study and then defined based on expert judgment. Here, a forward uncertainty quantification method with Latin hypercube sampling (LHS) is used, where the sample size is based on available computational resources.

42 ENGINEERING↗
DOE OSTI2021

A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

The global nature of the ITER project along with its projected ~ petabyte per day data generation presents a unique challenge, but also an opportunity for the fusion community to rethink, optimize, and enhance our scientific discovery process. Recognizing this, collaborative research with computational scientists was undertaken over the past several years to create a framework for large-scale data movement across wide-area networks (WANs), to enable global near-real time analysis of fusion data. This would broaden the available computational resources for analysis/simulation, and increase the number of researchers actively participating in experiments. An official demonstration of this framework for fast, large data transfer and real-time analysis was carried out between the KSTAR tokamak in Daejeon, Korea and PPPL in Princeton, USA. Streaming large data transfer, with near real-time movie creation and analysis of the KSTAR Electron Cyclotron Emission imaging (ECEI) data, was performed using the I/O framework ADIOS, and comparisons made at PPPL with simulation results from the XGC1 code. These demonstrations were made possible utilizing an optimized network configuration at PPPL, which achieved over 8.8 Gbps (88% utilization) in throughput tests from NFRI to PPPL. This demonstration showed the feasibility for large-scale data analysis of KSTAR data, and provides a nascent framework to enable use of globally distributed computational and personnel resources in pursuit of scientific knowledge from the ITER experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

PATHWAYS OF LARGE-SCALE MAGNETIC COUPLINGS BETWEEN SOLAR CORONAL EVENTS

The high-cadence, comprehensive view of the solar corona by SDO/AIA shows many events that are widely separated in space while occurring close together in time. In some cases, sets of coronal events are evidently causally related, while in many other instances indirect evidence can be found. We present case studies to highlight a variety of coupling processes involved in coronal events. We find that physical linkages between events do occur, but concur with earlier studies that these couplings appear to be crucial to understanding the initiation of major eruptive or explosive phenomena relatively infrequently. We note that the post-eruption reconfiguration timescale of the large-scale corona, estimated from the extreme-ultraviolet afterglow, is on average longer than the mean time between coronal mass ejections (CMEs), so that many CMEs originate from a corona that is still adjusting from a previous event. We argue that the coronal field is intrinsically global: current systems build up over days to months, the relaxation after eruptions continues over many hours, and evolving connections easily span much of a hemisphere. This needs to be reflected in our modeling of the connections from the solar surface into the heliosphere to properly model the solar wind, its perturbations, and the generation and propagation of solar energetic particles. However, the large-scale field cannot be constructed reliably by currently available observational resources. We assess the potential of high-quality observations from beyond Earth's perspective and advanced global modeling to understand the couplings between coronal events in the context of CMEs and solar energetic particle events.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

TRANSITION FROM KINETIC TO MHD BEHAVIOR IN A COLLISIONLESS PLASMA

The study of kinetic effects in heliospheric plasmas requires representation of dynamics at sub-proton scales, but in most cases the system is driven by magnetohydrodynamic (MHD) activity at larger scales. The latter requirement challenges available computational resources, which raises the question of how large such a system must be to exhibit MHD traits at large scales while kinetic behavior is accurately represented at small scales. Here we study this implied transition from kinetic to MHD-like behavior using particle-in-cell (PIC) simulations, initialized using an Orszag–Tang Vortex. The PIC code treats protons, as well as electrons, kinetically, and we address the question of interest by examining several different indicators of MHD-like behavior.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Next Generation Workload Management System For Big Data on Heterogeneous Distributed Computing

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS and ALICE are the largest collaborations ever assembled in the sciences and are at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, both experiments rely on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System (WMS) for managing the workflow for all data processing on hundreds of data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. The scale is demonstrated by the following numbers: PanDA manages O(10 2 ) sites, O(10 5 ) cores, O(10 8 ) jobs per year, O(10 3 ) users, and ATLAS data volume is O(10 17 ) bytes. In 2013 we started an ambitious program to expand PanDA to all available computing resources, including opportunistic use of commercial and academic clouds and Leadership Computing Facilities (LCF). The project titled 'Next Generation Workload Management and Analysis System for Big Data' (BigPanDA) is funded by DOE ASCR and HEP. Extending PanDA to clouds and LCF presents new challenges in managing heterogeneity and supporting workflow. The BigPanDA project is underway to setup and tailor PanDA at the Oak Ridge Leadership Computing Facility (OLCF) and at the National Research Center "Kurchatov Institute" together with ALICE distributed computing and ORNL computing professionals. Our approach to integration of HPC platforms at the OLCF and elsewhere is to reuse, as much as possible, existing components of the PanDA system. Finally, we will present our current accomplishments with running the PanDA WMS at OLCF and other supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

The OSG Open Facility: an on-ramp for opportunistic scientific computing

The Open Science Grid (OSG) is a large, robust computing grid that started primarily as a collection of sites associated with large HEP experiments such as ATLAS, CDF, CMS, and DZero, but has evolved in recent years to a much larger user and resource platform. In addition to meeting the US LHC community’s computational needs, the OSG continues to be one of the largest providers of distributed high-throughput computing (DHTC) to researchers from a wide variety of disciplines via the OSG Open Facility. The Open Facility consists of OSG resources that are available opportunistically to users other than resource owners and their collaborators. In the past two years, the Open Facility has doubled its annual throughput to over 200 million wall hours. More than half of these resources are used by over 100 individual researchers from over 60 institutions in fields such as biology, medicine, math, economics, and many others. Over 10% of these individual users utilized in excess of 1 million computational hours each in the past year. The largest source of these cycles is temporary unused capacity at institutions affiliated with US LHC computational sites. An increasing fraction, however, comes from university HPC clusters and large national infrastructure supercomputers offering unused capacity. Such expansions have allowed the OSG to provide ample computational resources to both individual researchers and small groups as well as sizable international science collaborations such as LIGO, AMS, IceCube, and sPHENIX. Opening up access to the Fermilab FabrIc for Frontier Experiments (FIFE) project has also allowed experiments such as mu2e and NOvA to make substantial use of Open Facility resources, the former with over 40 million wall hours in a year. We present how this expansion was accomplished as well as future plans for keeping the OSG Open Facility at the forefront of enabling scientific research by way of DHTC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Vertex finding in neutrino-nucleus interaction: a model architecture comparison

We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Relics of interspecific hybridization retained in the genome of a drought-adapted peanut cultivar

Peanut (Arachis hypogaea L.) is a globally important oil and food crop frequently grown in arid, semi-arid, or dryland environments. Improving drought tolerance is a key goal for peanut crop improvement efforts. Here, we present the genome assembly and gene model annotation for “Line8,” a peanut genotype bred from drought-tolerant cultivars. Our assembly and annotation are the most contiguous and complete peanut genome resources currently available. The high contiguity of the Line8 assembly allowed us to explore structural variation both between peanut genotypes and subgenomes. We detect several large inversions between Line8 and other peanut genome assemblies, and there is a trend for the inversions between more genetically diverged genotypes to have higher gene content. We also relate patterns of subgenome exchange to structural variation between Line8 homeologous chromosomes. Unexpectedly, we discover that Line8 harbors an introgression from A.cardenasii, a diploid peanut relative and important donor of disease resistance alleles to peanut breeding populations. The fully resolved sequences of both haplotypes in this introgression provide the first in situ characterization of A.cardenasii candidate alleles that can be leveraged for future targeted improvement efforts. The completeness of our genome will support peanut biotechnology and broader research into the evolution of hybridization and polyploidy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Whole-Genome Comparisons of Ergot Fungi Reveals the Divergence and Evolution of Species within the Genus Claviceps Are the Result of Varying Mechanisms Driving Genome Evolution and Host Range Expansion

The genus Claviceps has been known for centuries as an economically important fungal genus for pharmacology and agricultural research. Only recently have researchers begun to unravel the evolutionary history of the genus, with origins in South America and classification of four distinct sections through ecological, morphological, and metabolic features (Claviceps sects. Citrinae, Paspalorum, Pusillae, and Claviceps). The first three sections are additionally characterized by narrow host range, whereas section Claviceps is considered evolutionarily more successful and adaptable as it has the largest host range and biogeographical distribution. However, the reasons for this success and adaptability remain unclear. Our study elucidates factors influencing adaptability by sequencing and annotating 50 Claviceps genomes, representing 21 species, for a comprehensive comparison of genome architecture and plasticity in relation to host range potential. Our results show the trajectory from specialized genomes (sects. Citrinae and Paspalorum) toward adaptive genomes (sects. Pusillae and Claviceps) through colocalization of transposable elements around predicted effectors and a putative loss of repeat-induced point mutation resulting in unconstrained tandem gene duplication coinciding with increased host range potential and speciation. Alterations of genomic architecture and plasticity can substantially influence and shape the evolutionary trajectory of fungal pathogens and their adaptability. Furthermore, our study provides a large increase in available genomic resources to propel future studies of Claviceps in pharmacology and agricultural research, as well as, research into deeper understanding of the evolution of adaptable plant pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

NP-MRD: the Natural Products Magnetic Resonance Database

The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the ‘gold standard’ for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom

Abstract Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

ASSESSMENT OF 90SR AND 137CS PENETRATION INTO REINFORCED CONCRETE (EXTENT OF 'DEEPENING') UNDER NATURAL ATMOSPHERIC CONDITIONS

When assessing the feasibility of remediation following the detonation of a radiological dispersion device or improvised nuclear device in a large city, several issues should be considered including the levels and characteristics of the radioactive contamination, the availability of resources required for decontamination, and the planned future use of the city's structures and buildings. Currently, little is known about radionuclide penetration into construction materials in an urban environment. Knowledge in this area would be useful when considering costs of a thorough decontamination of buildings, artificial structures, and roads in an affected urban environment. Pripyat, a city substantially contaminated by the Chernobyl Nuclear Power Plant accident in April 1986, may provide some answers. The main objective of this study was to assess the depth of {sup 90}Sr and {sup 137}Cs penetration into reinforced concrete structures in a highly contaminated urban environment under natural weather conditions. Thirteen reinforced concrete core samples were obtained from external surfaces of a contaminated building in Pripyat. The concrete cores were drilled to obtain sample layers of 0-5, 5-10, 10-15, 15-20, 20-30, 30-40, and 40-50 mm. Both {sup 90}Sr and {sup 137}Cs were detected in the entire 0-50 mm profile of the reinforced cores sampled. In most of the cores, over 90% of the total {sup 137}Cs inventory and 70% of the total {sup 90}Sr inventory was found in the first 0-5 mm layer of the reinforced concrete. {sup 90}Sr had penetrated markedly deeper into the reinforced concrete structures than {sup 137}Cs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

High-resolution coupled physics solvers for analysing fine-scale nuclear reactor design problems

An integrated multi-physics simulation capability for the design and analysis of current and future nuclear reactor models is being investigated, to tightly couple neutron transport and thermal-hydraulics physics under the SHARP framework. Over several years, high-fidelity, validated mono-physics solvers with proven scalability on petascale architectures have been developed independently. Based on a unified component-based architecture, these existing codes can be coupled with a mesh-data backplane and a flexible coupling-strategy-based driver suite to produce a viable tool for analysts. The goal of the SHARP framework is to perform fully resolved coupled physics analysis of a reactor on heterogeneous geometry, in order to reduce the overall numerical uncertainty while leveraging available computational resources. Finally, the coupling methodology and software interfaces of the framework are presented, along with verification studies on two representative fast sodium-cooled reactor demonstration problems to prove the usability of the SHARP framework.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training

Distributed training increases the number of batches processed per iteration either by scaling-out (adding more nodes) or scaling-up (increasing the batch-size). However, the largest configuration does not necessarily yield the best performance. Horizontal scaling introduces additional communication overhead, while vertical scaling is constrained by computation cost and device memory limits. Thus, simply increasing the batch-size leads to diminishing returns: training time and cost decrease initially but eventually plateaus, creating a knee-point in the time/cost vs. batch-size pareto curve. The optimal batch-size therefore depends on the underlying model, data and available compute resources. Large batches also suffer from worse model quality due to the well-known “generalization gap”. In this paper, we present Tula, an online service that automatically optimizes time, cost, and convergence quality for large-batch training of convolutional models. It combines parallel-systems modeling with statistical performance prediction to identify the optimal batchsize. Tula predicts training time and cost within 7.5−14% error across multiple models, and achieves up to 20× overall speedup and improves test accuracy by ≈9% on average over standard large-batch training on various vision tasks, thus successfully mitigating the generalization gap and accelerating training at the same time.

Tyagi, Sahil [ORNL] (ORCID:0009000783144745)↗
DOE OSTI2024

Diaspora: Resilience-Enabling Services for Real-Time Distributed Workflows

The need for real-time processing to enable automated decision making and experimental steering has driven a shift from high-performance computing workflows on a centralized system to a distributed approach that integrates remote data sources, edge devices, and diverse compute facilities. Under this paradigm, data can be processed close to the source where it is generated, thus reducing latency and bandwidth usage. System resilience is thus a key challenge, requiring distributed workflows to survive component failures and to meet stringent quality-of-service requirements, which results in the need to mitigate anomalies such as congestion and low availability of resources. To address these challenges, we propose Diaspora, a unified resilience framework that is inspired by event-driven communication patterns used in public clouds. Specifically, we propose an event fabric that extends across sites, facilities, and computations to provide timely, reliable, and accurate information about data, application, and resource status. On top of the event fabric, we build resilience-enabling services that combine QoS-aware data streaming, resilient data views, resilient compute and data resources, and anomaly detection and prediction, all of which collectively enhance workflow resilience for these scientific cases.

Rao, Nageswara↗
DOE OSTI2012

Parameter Sweep and Optimization of Loosely Coupled Simulations Using the DAKOTA Toolkit

The increasing availability of large scale computing capabilities has accelerated the development of high-fidelity coupled simulations. Such simulations typically involve the integration of models that implement various aspects of the complex phenomena under investigation. Coupled simulations are playing an integral role in fields such as climate modeling, earth systems modeling, rocket simulations, computational chemistry, fusion research, and many other computational fields. Model coupling provides scientists with systematic ways to virtually explore the physical, mathematical, and computational aspects of the problem. Such exploration is rarely done using a single execution of a simulation, but rather by aggregating the results from many simulation runs that, together, serve to bring to light novel knowledge about the system under investigation. Furthermore, it is often the case (particularly in engineering disciplines) that the study of the underlying system takes the form of an optimization regime, where the control parameter space is explored to optimize an objective functions that captures system realizability, cost, performance, or a combination thereof. Novel and flexible frameworks that facilitate the integration of the disparate models into a holistic simulation are used to perform this research, while making efficient use of the available computational resources. In this paper, we describe the integration of the DAKOTA optimization and parameter sweep toolkit with the Integrated Plasma Simulator (IPS), a component-based framework for loosely coupled simulations. The integration allows DAKOTA to exploit the internal task and resource management of the IPS to dynamically instantiate simulation instances within a single IPS instance, allowing for greater control over the trade-off between efficiency of resource utilization and time to completion. We present a case study showing the use of the combined DAKOTA-IPS system to aid in the design of a lithium ion battery (LIB) cell, by studying a coupled system involving the electrochemistry and ion transport at the lower length scales and thermal energy transport at the device scales. The DAKOTA-IPS system provides a flexible tool for use in optimization and parameter sweep studies involving loosely coupled simulations that is suitable for use in situations where changes to the constituent components in the coupled simulation are impractical due to intellectual property or code heritage issues.

IPS CAEBAT Coupled Battery Simulations↗