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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Effect of electron-nuclear spin interactions for electron-spin qubits localized in InGaAs self-assembled quantum dots

The effect of electron-nuclear spin interactions on qubit operations is investigated for a qubit represented by the spin of an electron localized in an InGaAs self-assembled quantum dot. The localized electron wave function is evaluated within the atomistic tight-binding model. The electron Zeeman splitting induced by the electron-nuclear spin interaction is estimated in the presence of an inhomogeneous environment characterized by a random nuclear spin configuration, by the dot-size distribution, alloy disorder, and interface disorder. Due to these inhomogeneities, the electron Zeeman splitting varies from one qubit to another by the order of 10(-6), 10(-6), 10(-7), and 10(-9) eV, respectively. Such fluctuations cause errors in exchange operations due to the inequality of the Zeeman splitting between two qubits. However, the error can be made lower than the quantum error threshold if an exchange energy larger than 10(-4) eV is used for the operation. This result shows that the electron-nuclear spin interaction does not hinder quantum-dot based quantum computer architectures from being scalable even in the presence of inhomogeneous environments.

Whaley, K. Birgitta↗
NASA NTRSDate not supplied

Impact of NASA’s Entry Systems Modeling Project on Planetary Mission Design

Planetary missions continue to grow larger and more complex. Furthermore, the current focus on human exploration of the Moon and Mars, as well as Mars Sample Return(MSR), place increas-ingly stringent requirements on the reliability of the entry, descent, and landing (EDL) system that ensures the safe delivery of payload or crew to their destination. Planetary EDL is an area in which mission designers are critically reliant on modeling and simulation to demonstrate the reliability of the system, as there are no ground facilities that are able to fully test these systems in a flight-relevant environment. NASA’s state-of-the-art modeling and simulation capability must continually evolve to meet the needs of the next generation of planetary EDL. To accomplish this aim, NASA’s Entry Systems Modeling (ESM) Project was formed in 2013and is funded bythe Space Technology Mission Directorate(STMD) and Science Mission Directorate (SMD). ESM is the Agency’s only cross-cutting effort for advancing entry systems modeling and simulation capabilities across a range of technical disciplines and Solar System destinations. ESM is a portfolio project covering a variety of mid-TRL research efforts within four core EDL-related areas of investment: (1) Thermal protection material modeling, (2) Shock layer kinetics and radiation, (3) Aerosciences, and (4) Guidance, navigation, and con-trol. The material modeling group creates detailed material response modelsof thermal protection systems (TPS)from the micro to macro scale, and at the fun-damental and engineering levels. Shock layer kinetics and radiation focuses on radiative heating of space-craft, quantum chemistry and benchmark experiments for validation.Aerosciences is a broad research area that impacts many aspects of entry systems, including parachutes, aerodynamics, and turbulent heating augmentation due to TPS roughness.The guidance, navigation,and control effort under ESM is expanding the capabilities of NASA’s main flight mechanics tool, POST2, for use on high-performance computing architectures and to generalize interoperability with external applications for more detailed end-to-end simula-tion.In addition, several focusedresearch topics have been approvedto augment ESM’s core portfolio. These include efforts for deep post-flight analysis of Mars 2020/MEDLI2 flight data; development ofTPS failure models; improvement of hypersonic wakeflow models; and a recently concluded effort to provide material response models for NuSil-coated PICA heat-shield material. This presentation will discuss each of these investment areas and demonstrate via real mission examples how advances to the state-of-the-art enabled by ESM are directly impacting the missions of today and tomorrow, including InSight, Mars 2020, Mars Sample Return, Orion, and Dragonfly.

Entry Systems Modeling↗
NASA NTRSDate not supplied

Test Results for Autonomous Assembly of Modular Space Structures

This paper presents test results for autonomous assembly of a modular space structure. A Universal Robots UR10e manipulator with a custom end-effector, known as the Grapple Tool, was used to join TriTruss structural modules. The TriTrusses and their assembly configuration were developed for an in-space assembled telescope conceptual design. The tests presented here were performed with a 1:2.8 dimensionally scaled version of the Tri-Trusses. Robot trajectories were generated autonomously using a sample-based motion planner and an a-priori worksite model. The test results verify the assembly concept of operations as well as the software requirements for the Grapple Tool. While the Grapple Tool used in the tests was designed for the 1:2.8 scale Tri-Trusses, it has the same actuators, software, and computation architecture as the version designed for the full scale structure. The test operations consist of picking up two TriTrusses from their stowed locations and joining them to a third TriTruss mounted on a test stand and joining them to each other. This constitutes the first three modules of the in-space assembled telescope conceptual design. The paper presents the robot behavioral architecture, the generated trajectories commanded, the actual trajectories of the robot, and actuator performance of the Grapple Tool.

test↗
NASA NTRSDate not supplied

Test Results for Autonomous Assembly of Modular Space Structures

This paper presents test results for autonomous assembly of a modular space structure. A Universal Robots UR10e manipulator with a custom end-effector, known as the Grapple Tool, was used to join TriTruss structural modules. The TriTrusses and their assembly configuration were developed for an in-space assembled telescope conceptual design. The tests presented here were performed with a 1:2.8 dimensionally scaled version of the Tri-Trusses. Robot trajectories were generated autonomously using a sample-based motion planner and an a-priori worksite model. The test results verify the assembly concept of operations as well as the software requirements for the Grapple Tool. While the Grapple Tool used in the tests was designed for the 1:2.8 scale Tri-Trusses, it has the same actuators, software, and computation architecture as the version designed for the full scale structure. The test operations consist of picking up two TriTrusses from their stowed locations and joining them to a third TriTruss mounted on a test stand and joining them to each other. This constitutes the first three modules of the in-space assembled telescope conceptual design. The paper presents the robot behavioral architecture, the generated trajectories commanded, the actual trajectories of the robot, and actuator performance of the Grapple Tool.

test↗
NASA NTRSDate not supplied

Cathode Modeling of Solid-State Batteries

The search for safe, reliable, and compact high-capacity energy storage devices has led to increased interest in all-solid-state battery research. The use of solid electrolytes provides enhanced safety and durability due to their reduced flammability and increased mechanical strength compared to organic liquid electrolytes. Still, the use of solid electrolytes remains challenging. Computational modeling plays a substantial role in addressing these challenges. A particle dynamics electromechanical model for simulating electrochemical processes in a solid-state battery cathode will be presented. The model presents cathode microstructure at the particle level as a mixture of ionically conductive solid electrolyte particles, electrically conductive carbon additives, and cathodic reactant particles. After densification, the particle connectivity is analyzed to reconstruct the complex electric network connecting reactant particles with an anodic and cathodic current collectors through the electrolyte and carbon particles. The Kirchhoff’s matrix equation describing this electric network, is solved to obtain values of various critical parameters, such as the overall conductivity of the cathode for lithium ions and electrons, cathodic reactant material utilization, and the distribution of the electric current and voltages within the cathode. In addition, by representing the reactant particles as electrolyte or galvanic microcells governed by the Butler-Volmer electrochemical equation, the overall performance of battery cells during charge or discharge processes, respectively, can be predicted for a given cathodic powder composition. The presented model, executed on a high-performance computing architecture, essentially provides a valuable guidance in designing and developing future solid-state batteries.

solid-state battery↗
NASA NTRS2023

The 50-Year Landsat Collection 2 Archive

The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the U.S. Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.

Christopher J Crawford↗
DOE OSTI2015

Progress in Fast, Accurate Multi-scale Climate Simulations

We present a survey of physical and computational techniques that have the potential to con- tribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allow more complete representations of climate features at the global scale. At the same time, part- nerships with computer science teams have focused on taking advantage of evolving computer architectures, such as many-core processors and GPUs, so that these approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.

climate modeling↗
DOE OSTI2015

Open Research Challenges with Big Data - A Data-Scientist s Perspective

In this paper, we discuss data-driven discovery challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical data mining and machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are data mining algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstanding challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security, healthcare and manufacturing to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.

Big data↗
DOE OSTI2014

MiniAero

MiniAero is an MPl+X proxy app for gas dynamics problems. MiniAero can be used as a representative limited gas dynamics application to assess future computer architectures and programming models

Ken Franko, Micah Howard↗
DOE OSTI2015

Processor Emulator with Benchmark Applications

A processor emulator and a suite of benchmark applications have been developed to assist in characterizing the performance of data-centric workloads on current and future computer architectures. Some of the applications have been collected from other open source projects. For more details on the emulator and an example of its usage, see reference [1].

Lloyd, G. Scott↗
DOE OSTI2014

Machine Learning in the Big Data Era: Are We There Yet?

In this paper, we discuss the machine learning challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are machine learning algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstanding challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security and healthcare to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.

Big Data↗
DOE OSTI2014

A deterministic discrete ordinates transport proxy application

Kripke is a simple 3D deterministic discrete ordinates (Sn) particle transport code that maintains the computational load and communications pattern of a real transport code. It is intended to be a research tool to explore different data layouts, new programming paradigms and computer architectures.

Source record↗
DOE OSTI2014

OpenSoC Fabric

Recent advancements in technology scaling have shown a trend towards greater integration with large-scale chips containing thousands of processors connected to memories and other I/O devices using non-trivial network topologies. Software simulation proves insufficient to study the tradeoffs in such complex systems due to slow execution time, whereas hardware RTL development is too time-consuming. We present OpenSoC Fabric, an on-chip network generation infrastructure which aims to provide a parameterizable and powerful on-chip network generator for evaluating future high performance computing architectures based on SoC technology. OpenSoC Fabric leverages a new hardware DSL, Chisel, which contains powerful abstractions provided by its base language, Scala, and generates both software (C++) and hardware (Verilog) models from a single code base. The OpenSoC Fabric2 infrastructure is modeled after existing state-of-the-art simulators, offers large and powerful collections of configuration options, and follows object-oriented design and functional programming to make functionality extension as easy as possible.

Source record↗
DOE OSTI2017

DARMA v. Beta 0.5

DARMA is a portability layer for asynchronous many-task (AMT) runtime systems. AMT runtime systems show promise to mitigate challenges imposed by next generation high performance computing architectures. However, current runtime system technologies are not production-ready. DARMA is a portability layer that seeks to insulate application developers from idiosyncrasies of individual runtime systems, thereby facilitating application-developer use of these technologies. DARMA comprises a frontend application programming interface (API) for application developers, a backend API for runtime system developers, and a translation that translates frontend API calls into backend API calls. Application developers use C++ abstractions to annotate both data and tasks in their code. The DARMA translation layer uses C++ template metaprogramming to capture data-task dependencies, and provides this information to a potential backend runtime system via a series of backend API calls.

Hollman, David↗
DOE OSTI2017

Limits on the Efficiency of Event-Based Algorithms for Monte Carlo Neutron Transport

The traditional form of parallelism in Monte Carlo particle transport simulations, wherein each individual particle history is considered a unit of work, does not lend itself well to data-level parallelism. Event-based algorithms, which were originally used for simulations on vector processors, may offer a path toward better utilizing data-level parallelism in modern computer architectures. In this study, a simple model is developed for estimating the efficiency of the event-based particle transport algorithm under two sets of assumptions. Data collected from simulations of four reactor problems using OpenMC was then used in conjunction with the models to calculate the speedup due to vectorization as a function of two parameters: the size of the particle bank and the vector width. When each event type is assumed to have constant execution time, the achievable speedup is directly related to the particle bank size. We observed that the bank size generally needs to be at least 20 times greater than vector size in order to achieve vector efficiency greater than 90%. When the execution times for events are allowed to vary, however, the vector speedup is also limited by differences in execution time for events being carried out in a single event-iteration. For some problems, this implies that vector effciencies over 50% may not be attainable. While there are many factors impacting performance of an event-based algorithm that are not captured by our model, it nevertheless provides insights into factors that may be limiting in a real implementation.

Romano, Paul K.↗
DOE OSTI2019

Parameter Determination of the Non-Local Granular Fluidity Model for Wood Chips by Comparison to Well-Defined Experimental Flow Systems

Unlike simple liquids and gases, the bulk flow and transport of granular materials remain poorly understood by physicists and pose many problems for engineers in designing and operating bulk-solids handling equipment. Discrete element methods (DEM) are currently considered the state-of-the-art for simulating the flow of granular materials, but DEM is limited in system size to about a few million particles due to high computational costs, even when run on current high-performance computing architectures. Continuum models are needed to simulate the flows of bulk solids in industrial-scale vessels and equipment, e.g., grain silos and coal-ash disposal piles. The focus of this work is the so-called nonlocal granular fluidity (NLGF) model that has been previously shown to reproduce observed nonlocal behaviors that arise when granular phenomena occur on length scales that are near the scale of the system geometry, e.g., the jamming of hopper outlets and the stop-height of piles on inclines [PNAS, 110(17), 6730-6735]. We have implemented the NLGF constitutive model in OpenFOAM CFD software, performed simulations of well-defined flow systems, and compared the results to experimental data. Experimental systems include a ring-shear tester, pile formation, flow on an incline, and discharge of a hopper. The tested material was loblolly pine wood chips that were milled and sieved to a size range of roughly 0.05-0.25 inches in diameter. The NLGF model parameters were tuned to achieve reasonable agreement between simulation and experimental results. Further, we evaluate the mapping between measurable material properties (bulk friction angle, particle-particle friction, compressibility, grain diameter, etc.) with NLGF model parameters, some of which have direct analogs (friction parameters and grain diameter) while others are constructs of the model (nonlocal amplitude and timescale of fluidity evolution).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Parallelizing the Unpacking and Clustering of Detector Data for Reconstruction of Charged Particle Tracks on Multi-core CPUs and Many-core GPUs

We present results from parallelizing the unpacking and clustering steps of the raw data from the silicon strip modules for reconstruction of charged particle tracks. Throughput is further improved by concurrently processing multiple events using nested OpenMP parallelism on CPU or CUDA streams on GPU. The new implementation along with earlier work in developing a parallelized and vectorized implementation of the combinatoric Kalman filter algorithm has enabled efficient global reconstruction of the entire event on modern computer architectures. We demonstrate the performance of the new implementation on Intel Xeon and NVIDIA GPU architectures.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗