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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
DOE OSTI2023

Large Ensemble Particle Filter for Spatial Climate Reconstructions Using a Linear Inverse Model

Proxy records that document the last 2000 years of climate provide evidence for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) Particle filter (PF) that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP-class GCMs. We examine in a perfect-model framework the skill of the various LIMs to forecast the dynamics of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR PF. Our results show that the LIMs allow for skillful ensemble forecasts at 1-year lead-time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM-emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR PF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Low-symmetry vacancy-related spin qubit in hexagonal boron nitride

Abstract Point defect qubits in semiconductors have demonstrated their outstanding capabilities for high spatial resolution sensing generating broad multidisciplinary interest. Hexagonal boron nitride (hBN) hosting point defect qubits have recently opened up new horizons for quantum sensing by implementing sensing foils. The sensitivity of point defect sensors in hBN is currently limited by the linewidth of the magnetic resonance signal, which is broadened due to strong hyperfine couplings. Here, we report on a vacancy-related spin qubit with an inherently low symmetry configuration, the VB2 center, giving rise to a reduced magnetic resonance linewidth at zero magnetic fields. The VB2 center is also equipped with a classical memory that can be utilized for storing population information. Using scanning transmission electron microscopy imaging, we confirm the existence of the VB2 configuration in free-standing monolayer hBN.

Chemistry↗
DOE OSTI2023

GAHLS: an optimized graph analytics based high level synthesis framework

The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Current-induced switching of thin film α - Fe 2 O 3 devices imaged using a scanning single-spin microscope

Electrical switching of Néel order in an antiferromagnetic insulator is desirable as a basis for memory applications. Unlike electrically driven switching of ferromagnetic order via spin-orbit torques, electrical switching of antiferromagnetic order remains poorly understood. Here we investigate the low-field magnetic properties of 30-nm-thick, c-axis-oriented α-Fe 2 ⁢O 3 Hall devices using a diamond nitrogen-vacancy center scanning microscope. Using the canted moment of α-Fe 2⁢ O 3 as a magnetic handle on its Néel vector, we apply a saturating in-plane magnetic field to create a known initial state before letting the state relax in low field for magnetic imaging. Here, we repeat this procedure for different in-plane orientations of the initialization field. We find that the magnetic field images are characterized by stronger magnetic textures for fields along [$\overline{11}$20] and 11$\overline{2}$0, suggesting that despite the expected 3-fold magnetocrystalline anisotropy, our α-Fe 2 ⁢O 3 thin films have an overall in-plane uniaxial anisotropy. We also study current-induced switching of the magnetic order in α-Fe 2 ⁢O 3 . We find that the fraction of the device that switches depends on the current pulse duration, amplitude, and direction relative to the initialization field.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Imaging Domains In Magnetic Garnets By Use Of TSMFM

Tunneling-stabilized magnetic-force microscopy (TSMFM) demonstrated to yield images of magnetic domains in low-coercivity magnetic garnets with perpendicular anisotropy. Ability to generate images of domain walls and minute vertical Bloch lines aids study of vertical-Bloch-line magnetic memory devices that contain garnets. TSMFM provides desired resolution because its resolution not limited by diffraction.

Katti, Romney R.↗
NASA NTRS1999

Dynamic Load Balancing for Adaptive Computations on Distributed-Memory Machines

Dynamic load balancing is central to adaptive mesh-based computations on large-scale parallel computers. The principal investigator has investigated various issues on the dynamic load balancing problem under NASA JOVE and JAG rants. The major accomplishments of the project are two graph partitioning algorithms and a load balancing framework. The S-HARP dynamic graph partitioner is known to be the fastest among the known dynamic graph partitioners to date. It can partition a graph of over 100,000 vertices in 0.25 seconds on a 64- processor Cray T3E distributed-memory multiprocessor while maintaining the scalability of over 16-fold speedup. Other known and widely used dynamic graph partitioners take over a second or two while giving low scalability of a few fold speedup on 64 processors. These results have been published in journals and peer-reviewed flagship conferences.

Source record↗
DOE OSTI2016

GPU Acceleration of History-Based Multigroup Monte Carlo

Accurate solutions of the radiation transport equation play an important role in the analysis of many nuclear systems. Because there is no need to discretize the problem, Monte Carlo methods offer the most accurate possible solutions to the transport equation. However, significant computational effort is required to reduce the stochastic noise inherent to Monte Carlo methods. Current trends in high performance computing are moving towards vectorized, single instruction multiple data (SIMD), architectures such as Intel Xeon Phis and graphics processing units (GPUs) due to their favorable ratio of performance to power consumption. The challenge of adapting Monte Carlo transport to vectorized computing architectures was first addressed by Brown with an algorithm known as event-based Monte Carlo. In this approach, rather than simulating individual particle histories from birth to death, groups of particles are processed one event at a time. This allows each event batch to perform the same operations in lockstep, enabling use of the vector hardware. Although modern vectorized hardware is more versatile than the early vector machines, reducing so-called thread divergence is nonetheless still an important consideration for algorithm design. Many of the algorithms proposed so far for Monte Carlo transport on advanced architectures have focused on variants of event-based transport. Some evidence, however, suggests that low-level thread divergence, resulting from short-lived branching statements, may not be detrimental to overall performance on GPUs as long as the effect on achievable memory bandwidth is considered. This paper characterizes the performance of a traditional history-based Monte Carlo algorithm on GPUs. We show that with only minor modifications to traditional algorithms, significant performance gains relative to CPUs are possible. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Harvesting Mechanical and Thermal Energy by Combining ZnO Nanowires and NiTi Shape Memory Alloy

In the expanding world of small scale energy harvesting, the ability to combine thermal and mechanical harvesting is growing ever more important. Here, we demonstrate the feasibility of using ZnO nanowires to harvest both mechanical and low-quality thermal energy in simple, scalable devices. These devices were fabricated on kapton films and used ZnO nanowires with the same growth direction to assure alignment of the piezoelectric potentials of all of the wires. Mechanical harvesting from these devices was demonstrated using a periodic application of force, modeling the motion of the human body. Tapping the device from the top of the device with a wood stick, for example yielded an Open Circuit Voltage (OCV) of 0.2 - 4 V, which is in an ideal range for device applications. In order to demonstrate thermal harvesting from low quality heat sources, a commercially available Nitinol (Ni-Ti alloy) foil was attached to the nanowire piezoelectric device to create a compound thermoelectric. When bent at room temperature and then heated to 50°C, the Nitinol foil was restored to its original flat shape, which yielded an output voltage of nearly 1 V from the ZnO nanowire device.

36 MATERIALS SCIENCE↗
DOE OSTI2014

LastingNVCache: A Technique for Improving the Lifetime of Non-volatile Caches

Use of NVM (Non-volatile memory) devices such as ReRAM (resistive RAM) and STT-RAM (spin transfer torque RAM) for designing on-chip caches holds the promise of providing a high-density, low-leakage alternative to SRAM. However, low write endurance of NVMs, along with the write-variation introduced by existing cache management schemes may significantly limit the lifetime of NVM caches. We present LastingNVCache, a technique for improving lifetime of NVM caches by mitigating the intra-set write variation. LastingNVCache works on the key idea that by periodically flushing a frequently-written data-item, the next time the block can be made to load into a cold block in the set. Through this, the future writes to that data-item can be redirected from a hot block to a cold block, which leads to improvement in the cache lifetime. Microarchitectural simulations have shown that LastingNVCache provides 6.36X, 9.79X, and 10.94X improvement in lifetime for single, dual and quad-core systems. Also, its implementation overhead is small and it outperforms a recently proposed technique for improving lifetime of NVM caches.

device lifetime↗
DOE OSTI2021

Effect of substitutional doping and disorder on the phase stability, magnetism, and half-metallicity of Heusler alloys

Spintronics is the fast growing field that will play a key role in optimizing power consumption, memory, and processing capabilities of nanoelectronic devices. Heusler alloys are potential candidates for application in spintronics due to their room temperature (RT) half-metallicity, high Curie temperature, low lattice mismatch with most substrates, and strong control on electronic density of states at Fermi level. In this work, we investigate the effect of substitutional doping and disorder on the half-metallicity, phase stability, and magnetism of Heusler alloys using density functional theory methods. Our study shows that electronic and magnetic properties of half/full-Heusler alloys can be tuned by changing electron-count through controlled variation of chemical compositions of alloying elements. We provide a detailed discussion on the effect of substitutional doping and disorder on the tunability of half-metallic nature of Co 2 MnX and NiMnX based Heusler alloys, where X represents group 13–16 and period 3–6 elements of the periodic table. Based on the idea of electron count and disorder, we predicted a possible existence of thermodynamically stable half-metallic multicomponent bismuthides, for example, (CuNi 3 )Mn 4 Bi 4 and (ZnNi 7 )Mn 8 Bi 8 , through substitution doping at Ni site by specific Cu and Zn composition in half-Heusler NiMnBi. We believe that the design guide based on electron-counts presented for half-metals will play a key role in electronic-structure engineering of novel Heusler alloys for spintronic application, which will accelerate the development and synthesis of novel materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Excited-state electronic structure of molecules using many-body Green’s functions: Quasiparticles and electron–hole excitations with VOTCA-XTP

Here, we present the open-source VOTCA-XTP software for the calculation of the excited-state electronic structure of molecules using many-body Green’s function theory in the GW approximation with the Bethe–Salpeter equation (BSE). This work provides a summary of the underlying theory and discusses the details of its implementation based on Gaussian orbitals, including resolution-of-identity techniques and different approaches to the frequency integration of the self-energy or acceleration by offloading compute-intensive matrix operations using graphics processing units in a hybrid OpenMP/Cuda scheme. A distinctive feature of VOTCA–XTP is the capability to couple the calculation of electronic excitations to a classical polarizable environment on an atomistic level in a coupled quantum- and molecular-mechanics (QM/MM) scheme, where a complex morphology can be imported from Molecular Dynamics simulations. The capabilities and limitations of the GW–BSE implementation are illustrated with two examples. First, we study the dependence of optically active electron–hole excitations in a series of diketopyrrolopyrrole-based oligomers on molecular-architecture modifications and the number of repeat units. Second, we use the GW–BSE/MM setup to investigate the effect of polarization on localized and intermolecular charge-transfer excited states in morphologies of low-donor content rubrene–fullerene mixtures. These showcases demonstrate that our implementation currently allows us to treat systems with up to 2500 basis functions on regular shared-memory workstations, providing accurate descriptions of quasiparticle and coupled electron–hole excited states of various characters on an equal footing.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Extremely large nonsaturating magnetoresistance and ultrahigh mobility due to topological surface states in the metallic Bi 2 Te 3 topological insulator

Here, weak antilocalization (WAL) effects in Bi 2 Te 3 single crystals have been investigated at high and low bulk charge carrier concentrations. At low charge carrier density the WAL curves scale with the normal component of the magnetic field, demonstrating the dominance of topological surface states in magnetoconductivity. At high charge carrier density the WAL curves scale with neither the applied field nor its normal component, implying a mixture of bulk and surface conduction. WAL due to topological surface states shows no dependence on the nature (electrons or holes) of the bulk charge carriers. The observations of an extremely large, non-saturating magnetoresistance, and ultrahigh mobility in the samples with lower carrier density further support the presence of surface states. The physical parameters characterizing the WAL effects are calculated using the Hikami-Larkin-Nagaoka formula. At high charge carrier concentrations, there is a greater number of conduction channels and a decrease in the phase coherence length compared to low charge carrier concentrations. The extremely large magnetoresistance and high mobility of topological insulators have great technological value and can be exploited in magneto-electric sensors and memory devices.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Adaptive Impact-Driven Detection of Silent Data Corruption for HPC Applications

For exascale HPC applications, silent data corruption (SDC) is one of the most dangerous problems because there is no indication that there are errors during the execution. We propose an adaptive impact-driven method that can detect SDCs dynamically. The key contributions are threefold. (1) We carefully characterize 18 real-world HPC applications and discuss the runtime data features, as well as the impact of the SDCs on their execution results. (2) We propose an impact-driven detection model that does not blindly improve the prediction accuracy, but instead detects only influential SDCs to guarantee user-acceptable execution results. (3) Our solution can adapt to dynamic prediction errors based on local runtime data and can automatically tune detection ranges for guaranteeing low false alarms. Experiments show that our detector can detect 80-99.99% of SDCs with a false alarm rate less that 1% of iterations for most cases. The memory cost and detection overhead are reduced to 15% and 6.3%, respectively, for a large majority of applications.

Exascale HPC↗
DOE OSTI2023

DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets

Graph neural networks (GNNs) are a class of Deep Learning models used in designing atomistic materials for effective screening of large chemical spaces. To ensure robust prediction, GNN models must be trained on large volumes of atomistic data on leadership class supercomputers. Even with the advent of modern architectures that consist of multiple storage layers that include node-local NVMe devices in addition to device memory for caching large datasets, extreme-scale model training faces I/O challenges at scale.We present DDStore, an in-memory distributed data store designed for GNN training on large-scale graph data. DDStore provides a hierarchical, distributed, data caching technique that combines data chunking, replication, low-latency random access, and high throughput communication. DDStore achieves near-linear scaling for training a GNN model using up to 1000 GPUs on the Summit and Perlmutter supercomputers, and reaches up to a 6.15x reduction in GNN training time compared to state-of-the-art methodologies.

Choi, Jong Youl↗
NASA NTRS1980

On-board processing concepts for future satellite communications systems

The initial definition of on-board processing for an advanced satellite communications system to service domestic markets in the 1990's is discussed. An exemplar system with both RF on-board switching and demodulation/remodulation baseband processing is used to identify important issues related to system implementation, cost, and technology development. Analyses of spectrum-efficient modulation, coding, and system control techniques are summarized. Implementations for an RF switch and baseband processor are described. Among the major conclusions listed is the need for high gain satellites capable of handling tens of simultaneous beams for the efficient reuse of the 2.5 GHz 30/20 frequency band. Several scanning beams are recommended in addition to the fixed beams. Low power solid state 20 GHz GaAs FET power amplifiers in the 5W range and a general purpose digital baseband processor with gigahertz logic speeds and megabits of memory are also recommended.

Brandon, W. T.↗
NASA NTRS1980

Evolution of a standard microprocessor-based space computer

An existing in inventory computer hardware/software package (B-1 RFS/ECM) was repackaged and applied to multiple missile/space programs. Concurrent with the application efforts, low risk modifications were made to the computer from program to program to take advantage of newer, advanced technology and to meet increasingly more demanding requirements (computational and memory capabilities, longer life, and fault tolerant autonomy). It is concluded that microprocessors hold promise in a number of critical areas for future space computer applications. However, the benefits of the DoD VHSIC Program are required and the old proliferation problem must be revised.

Fernandez, M.↗