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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Understanding of the Elemental Diffusion Behavior in Concentrated Solid Solution Alloys

As one of the core effects on the high-temperature structural stability, the so-called “sluggish diffusion effect” in high-entropy alloy (HEA) has attracted much attention. Experimental investigations on the diffusion kinetics have been carried out in a few HEA systems, such as Al-Co-Cr-Fe-Ni and Co-Cr-Fe-Mn-Ni. However, the mechanisms behind this effect remain unclear. To better understand the diffusion kinetics of the HEAs, a combined computational/experimental approach is employed in the current study. In the present work, a self-consistent atomic mobility database is developed for the face-centered cubic (fcc) phase of the Co-Cr-Fe-Mn-Ni quinary system. The simulated diffusion coefficients and concentration profiles using this database can well describe the experimental data both from this work and the literatures. The validated mobility database is then used to calculate the tracer diffusion coefficients of Ni in the subsystems of the Co-Cr-Fe-Mn-Ni system with equiatomic ratios. The comparisons of these calculated diffusion coefficients reveal that the diffusion of Ni is not inevitably more sluggish with increasing number of components in the subsystem even with homologous temperature. Taking advantage of computational thermodynamics, the diffusivities of alloying elements with composition and/or temperature are also calculated. Furthermore, these calculations provide us an overall picture of the diffusion kinetics within the Co-Cr-Fe-Mn-Ni system.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Petascale supercomputing to accelerate the design of high-temperature alloys

Recent progress in high-performance computing and data informatics has opened up numerous opportunities to aid the design of advanced materials. Herein, we demonstrate a computational workflow that includes rapid population of high-fidelity materials datasets via petascale computing and subsequent analyses with modern data science techniques. We use a first-principles approach based on density functional theory to derive the segregation energies of 34 microalloying elements at the coherent and semi-coherent interfaces between the aluminium matrix and the θ '-Al 2 Cu precipitate, which requires several hundred supercell calculations. We also perform extensive correlation analyses to identify materials descriptors that affect the segregation behaviour of solutes at the interfaces. Finally, we show an example of leveraging machine learning techniques to predict segregation energies without performing computationally expensive physics-based simulations. As a result, the approach demonstrated in the present work can be applied to any high-temperature alloy system for which key materials data can be obtained using high-performance computing.

36 MATERIALS SCIENCE↗
DOE OSTI2016

TU-FG-201-04: Computer Vision in Autonomous Quality Assurance of Linear Accelerators

Purpose: Routine quality assurance (QA) of linear accelerators represents a critical and costly element of a radiation oncology center. Recently, a system was developed to autonomously perform routine quality assurance on linear accelerators. The purpose of this work is to extend this system and contribute computer vision techniques for obtaining quantitative measurements for a monthly multi-leaf collimator (MLC) QA test specified by TG-142, namely leaf position accuracy, and demonstrate extensibility for additional routines. Methods: Grayscale images of a picket fence delivery on a radioluminescent phosphor coated phantom are captured using a CMOS camera. Collected images are processed to correct for camera distortions, rotation and alignment, reduce noise, and enhance contrast. The location of each MLC leaf is determined through logistic fitting and a priori modeling based on knowledge of the delivered beams. Using the data collected and the criteria from TG-142, a decision is made on whether or not the leaf position accuracy of the MLC passes or fails. Results: The locations of all MLC leaf edges are found for three different picket fence images in a picket fence routine to 0.1mm/1pixel precision. The program to correct for image alignment and determination of leaf positions requires a runtime of 21– 25 seconds for a single picket, and 44 – 46 seconds for a group of three pickets on a standard workstation CPU, 2.2 GHz Intel Core i7. Conclusion: MLC leaf edges were successfully found using techniques in computer vision. With the addition of computer vision techniques to the previously described autonomous QA system, the system is able to quickly perform complete QA routines with minimal human contribution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Optimizing Management of Persistent Data Structures in High-Performance Analytics

Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.

Computer science↗
DOE OSTI2017

Implementation of new PRODIAG algorithm and simulation-based acceptance test - 219

This paper presents the improvement of an automated-reasoning computer program for nuclear power plant diagnosis, namely PRODIAG. PRODIAG, first developed at the Argonne National Laboratory, uses confluence-based reasoning to perform fault diagnosis in engineering systems that can be described by the conservation laws for mass, energy, and momentum. Therefore, unlike other plant configuration-dependent rule-based diagnosis algorithms, the code is generally applicable to a wide variety of plant configurations by simply replacing the plant instrumentation diagram (PID) database. The need for improvement was raised when the original PRODIAG failed to provide the requisite degree of extensibility and maintainability for new applications. These shortcomings have been addressed through a new code architecture that uses an objected-oriented logic-programming language in place of the original Prolog language and uses an open-source automated reasoning engine in place of the original hardwired rule-evaluation sequence. This paper presents the architecture and implementation details of the code transition. Verification and validation studies were performed for the new PRODIAG, which involved re-running the distinct fault cases defined at the time the code was originally developed. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1995

Visualization Techniques in Space and Atmospheric Sciences

Unprecedented volumes of data will be generated by research programs that investigate the Earth as a system and the origin of the universe, which will in turn require analysis and interpretation that will lead to meaningful scientific insight. Providing a widely distributed research community with the ability to access, manipulate, analyze, and visualize these complex, multidimensional data sets depends on a wide range of computer science and technology topics. Data storage and compression, data base management, computational methods and algorithms, artificial intelligence, telecommunications, and high-resolution display are just a few of the topics addressed. A unifying theme throughout the papers with regards to advanced data handling and visualization is the need for interactivity, speed, user-friendliness, and extensibility.

Szuszczewicz, E. P.↗
DOE OSTI2022

On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Computational workflows that combine molecular dynamics (MD) simulations and emerging data-centric (DC) methods can accelerate the screening and analysis of solvent systems experimentally and computationally. Here, MD simulations provide atomic positions and velocities of reactant, solvent, and catalyst materials that can be manipulated into data representations that in turn can be used by DC techniques to conduct predictive modeling, feature extraction, and experimental design. For liquid-phase catalytic applications, emerging DC techniques such as Convolutional and Graph Neural Networks (CNN/GNN), Topological Data Analysis (TDA), and Active Learning (AL) can leverage MD and experimental data to quickly predict solvent effects on reaction outcomes. For instance, in recent studies, 3D solvent environments obtained with MD have been exploited by CNNs to predict experimental reaction rates for homogeneous acid-catalyzed lignocellulosic processes. In this perspective, we discuss basic principles of DC methods and how these can be combined with MD to enable high-throughput screening of solvent selection for diverse catalysis applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2008

NASA Tech Briefs, December 2008

Topics covered include: Crew Activity Analyzer; Distributing Data to Hand-Held Devices in a Wireless Network; Reducing Surface Clutter in Cloud Profiling Radar Data; MODIS Atmospheric Data Handler; Multibeam Altimeter Navigation Update Using Faceted Shape Model; Spaceborne Hybrid-FPGA System for Processing FTIR Data; FPGA Coprocessor for Accelerated Classification of Images; SiC JFET Transistor Circuit Model for Extreme Temperature Range; TDR Using Autocorrelation and Varying-Duration Pulses; Update on Development of SiC Multi-Chip Power Modules; Radio Ranging System for Guidance of Approaching Spacecraft; Electromagnetically Clean Solar Arrays; Improved Short-Circuit Protection for Power Cells in Series; Electromagnetically Clean Solar Arrays; Logic Gates Made of N-Channel JFETs and Epitaxial Resistors; Improved Short-Circuit Protection for Power Cells in Series; Communication Limits Due to Photon-Detector Jitter; System for Removing Pollutants from Incinerator Exhaust; Sealing and External Sterilization of a Sample Container; Converting EOS Data from HDF-EOS to netCDF; HDF-EOS 2 and HDF-EOS 5 Compatibility Library; HDF-EOS Web Server; HDF-EOS 5 Validator; XML DTD and Schemas for HDF-EOS; Converting from XML to HDF-EOS; Simulating Attitudes and Trajectories of Multiple Spacecraft; Specialized Color Function for Display of Signed Data; Delivering Alert Messages to Members of a Work Force; Delivering Images for Mars Rover Science Planning; Oxide Fiber Cathode Materials for Rechargeable Lithium Cells; Electrocatalytic Reduction of Carbon Dioxide to Methane; Heterogeneous Superconducting Low-Noise Sensing Coils; Progress toward Making Epoxy/Carbon-Nanotube Composites; Predicting Properties of Unidirectional-Nanofiber Composites; Deployable Crew Quarters; Nonventing, Regenerable, Lightweight Heat Absorber; Miniature High-Force, Long-Stroke SMA Linear Actuators; "Bootstrap" Configuration for Multistage Pulse-Tube Coolers; Reducing Liquid Loss during Ullage Venting in Microgravity; Ka-Band Transponder for Deep-Space Radio Science; Replication of Space-Shuttle Computers in FPGAs and ASICs; Demisable Reaction-Wheel Assembly; Spatial and Temporal Low-Dimensional Models for Fluid Flow; Advanced Land Imager Assessment System; Range Imaging without Moving Parts.

Source record↗
NASA NTRSDate not supplied

Impact of Emerging Computing Architectures and Opportunities for Process Systems Engineering Applications

Moore’s “law” was the observation that the number of transistors in an integrated circuit doubled approximately every two years. This trend has distinctly failed to hold in recent years. The death of Moore’s law has left researchers and practitioners in the computational sciences searching for technologies to provide the speedups formerly supported by Moore’s law. Previously overlooked chip architectures and other computing technologies are now receiving more development resources. Critically, these technologies are gaining more mature software support, opening their adoption by researchers in algorithms and applications. In this article, we review some of these computing technologies, their relationship with various algorithms and applications, and their potential benefits (or pitfalls). We close with recommendations for future work by the process systems engineering community specifically.

Emerging hardware↗
DOE OSTI2026

Identifying Opportunities at the Interface of Chemistry and Quantum Information Science (Final Technical Report)

This project convened a National Academies committee to identify opportunities and research priorities at the interface of chemistry and quantum information science (QIS). The work culminated in a consensus study report that (1) articulates three fundamental research areas to advance QIS (design and synthesis of molecular qubits; measurement and control of molecular quantum systems; and experimental and computational scaling of qubit design and function), and (2) underscores the importance of cross-disciplinary collaboration, access to facilities and instrumentation, FAIR-aligned data infrastructure, and workforce development initiatives to sustain U.S. leadership in QIS. The report and all other material associated with this project can be downloaded on the project webpage: https://www.nationalacademies.org/projects/DELS-BCST-21-01 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Extending the Publish/Subscribe Abstraction for High-Performance I/O and Data Management at Extreme Scale

The Adaptable I/O System (ADIOS) represents the culmination of substantial investment in Scientific Data Management, and it has demonstrated success for several important extreme-scale science cases. However, looking towards the exascale and beyond, we see the development of yet more stringent data management requirements that require new abstractions. Therefore, there is an opportunity to attempt to connect the traditional realms of HPC I/O optimization with the Database / Data Management community. As such, in this paper we offer some specific examples from our ongoing work in managing data structures, services, and performance at the extreme scale for scientific computing. Using the publish/subscribe model afforded by ADIOS, we demonstrate a set of services that connect data format, metadata, queries, data reduction, and high-performance delivery. The resulting publish/subscribe framework facilitates connection to on-line workflow systems to enable the dynamic capabilities that will be required for exascale science.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗