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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Measurement and memory in the periodically driven complex Ginzburg-Landau equation

In the present work we illustrate that classical but nonlinear systems may possess features reminiscent of quantum ones, such as memory, upon suitable external perturbation. As our prototypical example, we use the two-dimensional complex Ginzburg-Landau equation in its vortex glass regime. Here, we impose an external drive as a perturbation mimicking a quantum measurement protocol, with a given “measurement rate” (the rate of repetition of the drive) and “mixing rate” (characterized by the intensity of the drive). Using a variety of measures, we find that the system may or may not retain its coherence, statistically retrieving its original glass state, depending on the strength and periodicity of the perturbing field. The corresponding parametric regimes and the associated energy cascade mechanisms involving the dynamics of vortex waveforms and domain boundaries are discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Ultrafast Electron Correlations and Memory Effects at Work: Femtosecond Demagnetization in Ni

Experimental observations of the ultrafast (less than 50 fs) demagnetization of Ni have so far defied theoretical explanations particularly since its spin-flipping time is much less than that resulting from spin-orbit and electron-lattice interactions. Through the application of an approach that benefits from spin-flip time-dependent density-functional theory and dynamical mean-field theory, we show that proper inclusion of electron correlations and memory (time dependence of electron-electron interaction) effects leads to demagnetization at the femtosecond scale, in good agreement with experimental observations. Furthermore, our calculations reveal that this ultrafast demagnetization results mainly from spin-flip transitions from occupied to unoccupied orbitals implying a dynamical reduction of exchange splitting. In this work, these conclusions are found to be valid for a wide range of laser pulse amplitudes. They also pave the way for ab initio investigations of ultrafast charge and spin dynamics in a variety of quantum materials in which electron correlations may play a definitive role.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Origins of magnetic memory and strong exchange bias bordering magnetic compensation in mixed-lanthanide systems

Here, the unexpected physical phenomena resulting from the seemingly inconsequential substitutions of chemically similar lanthanide elements in the Pr 1-x Gd x ScGe system are exploited to further the understanding of rare-earth magnetism and inform materials design. By directly probing magnetic moments of crystallographically indistinguishable Pr and Gd we solve the puzzles of how an unusual magnetic memory and strong exchange bias emerge at specific, easily predictable chemistries. Both effects are rooted in a robust antiparallel arrangement of large 4f magnetic moments of light and heavy lanthanides. This enables precise control of nearly zero net magnetization either opposed to, or aligned with, the external magnetic field that persists over a wide range of temperatures and fields. Further, spontaneous perturbations in the random distribution of lanthanide ions makes strong exchange bias possible in bulk single-phase compounds bordering magnetic compensation, consequently expanding the materials base beyond artificial magnetic multilayers and broadening the range of potential applications of the phenomenon.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Fast Sideband Control of a Multimode Cavity Memory with Weak Dispersive Coupling to a Transmon

Mitigating ancilla-mediated error channels is a critical challenge in controlling high-quality superconducting cavities using circuit quantum electrodynamics (cQED). We address this by weakening the dispersive coupling while demonstrating fast, high-fidelity multimode control through transmon-mediated sideband interactions. We implement transmon-cavity SWAP gates with speeds up to 30 times larger than the bare dispersive coupling. Combined with transmon rotations, this enables universal state preparation in a single mode, though achieving unitary gates and extending control to multiple modes remains a challenge. In this work, we overcome this limitation by introducing two control strategies: (i) a shelving technique that stores populations in sideband-transparent states, and (ii) a method that exploits the dispersive shift to implement photon-number-selective transmon-cavity SWAP gates. We use these protocols to prepare Fock and binomial code states across any of the ten modes of a multimode cavity with millisecond coherence times—serving as a multimode quantum memory. We demonstrate unitaries that encode and decode an arbitrary qubit state from the transmon into corresponding vacuum and Fock state superpositions, as well as entangled NOON states of cavity mode pairs—a scheme extendable to arbitrary multimode Fock encodings in the cavity modes. Furthermore, we implement a new binomial encoding gate that converts arbitrary transmon superpositions into binomial code states in any cavity mode at a rate exceeding the dispersive shifts in our system, achieving an average post-selected state fidelity of 96.3% in a 4 μ⁢s gate time. By using precalibrated transmon and sideband pulses, our work demonstrates multimode control with significantly reduced calibration overhead, enabling efficient unitary operations using sideband interactions in multimode cQED systems.

Huang, Jordan [Rutgers Univ., Piscataway, NJ (Unit↗
DOE OSTI2018

Scalable Distributed Memory Community Detection Using Vite

Graph clustering, popularly known as community detection, is a fundamental graph operation used in several applications of relevance for network analysis and cybersecurity. Community detection is an operation of partitioning a network into an arbitrary number of “communities” such that each com munity represents a tightly-knit group of nodes with relatively sparser connections to the rest of the nodes in the network. The need to compute clustering on large-scale networks necessitates the development of efficient parallel algorithms capable of exploiting modern parallel architectures. However, due to their irregular and inherently sequential nature, many of the current algorithms for community detection are challenging to parallelize. In response to the 2018 Graph Challenge, we present Vite which is a distributed-memory parallel community detection method. Vite is a parallelization of the algorithmic template provided by a widely-used serial method (Louvain). Alongside, it uses several heuristics aimed at significantly improving parallelism and performance, while preserving output quality. Using the inputs from the 2018 Graph Challenge (static and streaming), we demonstrate superior performance and high quality solutions based on four parallelization heuristics.

community detection↗
DOE OSTI2018

Performance Issues of SYRK Implementations in Shared Memory Environments for Edge Cases

The symmetric rank-k update (SYRK) is a level-3 BLAS routine commonly used by many Data Mining/Machine Learning(DM/ML) algorithms such as regression, dimensionality reduction algorithms like PCA, matrix factorization and k-mean clustering. This paper presents a comprehensive analysis of the SYRK routine under popular dense linear algebra libraries such as OpenBLAS, Intel MKL, and BLIS particularly focusing on edge cases of dense matrices (thin or fat shapes) that are common in DM/ML applications. Our work identifies some performance issues of the SYRK routine in multi-threaded shared memory environments for edge cases and discuss matrix dependent modifications for performance improvement.

Hossain, Md Mosharaf↗
DOE OSTI2018

Improving Quality of Observational Streaming Medical Data by Using Long Short-Term Memory Networks (LSTMs)

We present an exploration of the encoder-decoder structured Long Short-Term Memory Network (LSTM) as a detector of the anomalous missing observations in streaming medical data by using the difference between the LSTM-reconstructed and observed values as the anomaly detector. We experiment with time-series data from bedside monitoring devices from the available Medical Information Mart for Intensive Care Database (MIMIC). Our results show that not only encoder-decoder LSTM approach works well for detecting the difference between anomalous and normal missing observations in streaming medical data, but also has an imputation potential for the missing data.

Bowie, Michael B.↗
DOE OSTI2016

Parallel k-means++ for Multiple Shared-Memory Architectures

In recent years k-means++ has become a popular initialization technique for improved k-means clustering. To date, most of the work done to improve its performance has involved parallelizing algorithms that are only approximations of k-means++. In this paper we present a parallelization of the exact k-means++ algorithm, with a proof of its correctness. We develop implementations for three distinct shared-memory architectures: multicore CPU, high performance GPU, and the massively multithreaded Cray XMT platform. We demonstrate the scalability of the algorithm on each platform. In addition we present a visual approach for showing which platform performed k-means++ the fastest for varying data sizes.

data clustering↗
DOE OSTI2021

Design Space Exploration of Ferroelectric Tunnel Junction Toward Crossbar Memories

We perform a simulation-based analysis on the potential of emerging ferroelectric tunnel junctions (FTJs) as a memory device for crossbar arrays. Though FTJs are promising due to their low power switching characteristics compared to other emerging technologies, the greatest challenge for FTJs is the tradeoff between integration density and read performance. Our analysis highlights the need to co-optimize the ferroelectric thickness of the FTJ and read/write voltages to achieve proper functionality at large array sizes. Our analysis shows that FTJ-based crossbar achieves 93% higher sense margin at isoread power of 116 nW (per bit), but this FTJ design comes at a cost of 9.28× higher write power at isowrite time of 250 ns. In response, we study the potential tradeoffs of design points outside the feasible region to understand what device characteristics are desired to overcome such challenges.

Jao, Nicholas↗
DOE OSTI2015

In-Memory Graph Databases for Web-Scale Data

RDF databases have emerged as one of the most relevant way for organizing, integrating, and managing expo- nentially growing, often heterogeneous, and not rigidly structured data for a variety of scientific and commercial fields. In this paper we discuss the solutions integrated in GEMS (Graph database Engine for Multithreaded Systems), a software framework for implementing RDF databases on commodity, distributed-memory high-performance clusters. Unlike the majority of current RDF databases, GEMS has been designed from the ground up to primarily employ graph-based methods. This is reflected in all the layers of its stack. The GEMS framework is composed of: a SPARQL-to-C++ compiler, a library of data structures and related methods to access and modify them, and a custom runtime providing lightweight software multithreading, network messages aggregation and a partitioned global address space. We provide an overview of the framework, detailing its component and how they have been closely designed and customized to address issues of graph methods applied to large-scale datasets on clusters. We discuss in details the principles that enable automatic translation of the queries (expressed in SPARQL, the query language of choice for RDF databases) to graph methods, and identify differences with respect to other RDF databases.

GEMS↗
DOE OSTI2020

Detection and Diagnosis of Data Integrity Attacks in Solar Farms Based on Multilayer Long Short-Term Memory Network

Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. Here this letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc–dc and dc–ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. As a comparison, we have evaluated classic data-driven methods, including K-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.

42 ENGINEERING↗
DOE OSTI2020

Atomic-Scale Imaging of Polarization Switching in an (Anti-)Ferroelectric Memory Material: Zirconia (ZrO2)

Direct, atomic-scale visualization of polarization switching in a functional, polycrystalline, binary oxide via in-situ high-resolution transmission electron microscopy (HRTEM) biasing is reported for the first time. Antiferroelectric (AFE) ZrO2 was used as the model system, which is important for commercial DRAMs and as emerging NVMs (through work-function engineering). We observed (1) clear shifting and coalescing of domains within a single grain, and (2) dramatic changes of the atomic arrangements and crystalline phases—both at voltages above the critical voltage measured for AFE switching. Similar synergistic in-situ structural-electrical characterization can pave the way to understand and engineer microscopic mechanisms for retention, fatigue, variability, sub-coercive switching and analog states in ferroelectric and AFE-based memory devices.

Lombardo, Sarah↗
DOE OSTI2022

Learning the Temporal Effect in Infrared Thermal Videos With Long Short-Term Memory for Quality Prediction in Resistance Spot Welding

With the advances of sensing technology, in-situ infrared thermal videos can be collected from Resistance Spot Welding (RSW) processes. Each video records the formulation process of a weld nugget. The nugget evolution creates a “temporal effect” across the frames, which can be leveraged for real-time, nondestructive evaluation (NDE) of the weld quality. Currently, quality prediction with imaging data mainly focuses on optical feature extraction with Convolutional Neural Network (CNN) but does not make the most of such temporal effect. In this study, pixels corresponding to critical locations on the weld nugget surface are extracted from a video to form multivariate time series (MTS). Multivariate Adaptive Regression Splines (MARS) is used in MTS processing to remove noisy signals related to uninformative frames. A Stacked Long Short-Term Memory (LSTM) model is developed to learn from the processed MTS and then predicts weld nugget size and thickness in real-time NDE. Results from a case study on RSW of Boron steel demonstrates the improvement in prediction accuracy and computational time with the proposed method, as compared to CNN-based weld quality prediction.

Guo, Shenghan↗
DOE OSTI2014

Performance improvement of gadolinium oxide resistive random access memory treated by hydrogen plasma immersion ion implantation

Characteristics improvement of gadolinium oxide (Gd{sub x}O{sub y}) resistive random access memories (RRAMs) treated by hydrogen plasma immersion ion implantation (PIII) was investigated. With the hydrogen PIII treatment, the Gd{sub x}O{sub y} RRAMs exhibited low set/reset voltages and a high resistance ratio, which were attributed to the enhanced movement of oxygen ions within the Gd{sub x}O{sub y} films and the increased Schottky barrier height at Pt/Gd{sub x}O{sub y} interface, respectively. The resistive switching mechanism of Gd{sub x}O{sub y} RRAMs was dominated by Schottky emission, as proved by the area dependence of the resistance in the low resistance state. After the hydrogen PIII treatment, a retention time of more than 10{sup 4} s was achieved at an elevated measurement temperature. In addition, a stable cycling endurance with the resistance ratio of more than three orders of magnitude of the Gd{sub x}O{sub y} RRAMs can be obtained.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Viable chemical approach for patterning nanoscale magnetoresistive random access memory

A reactive ion etching process with alternating Cl{sub 2} and H{sub 2} exposures has been shown to chemically etch CoFe film that is an integral component in magnetoresistive random access memory (MRAM). Starting with systematic thermodynamic calculations assessing various chemistries and reaction pathways leading to the highest possible vapor pressure of the etch products reactions, the potential chemical combinations were verified by etch rate investigation and surface chemistry analysis in plasma treated CoFe films. An ∼20% enhancement in etch rate was observed with the alternating use of Cl{sub 2} and H{sub 2} plasmas, in comparison with the use of only Cl{sub 2} plasma. This chemical combination was effective in removing metal chloride layers, thus maintaining the desired magnetic properties of the CoFe films. Scanning electron microscopy equipped with energy-dispersive x-ray spectroscopy showed visually and spectroscopically that the metal chloride layers generated by Cl{sub 2} plasma were eliminated with H{sub 2} plasma to yield a clean etch profile. This work suggests that the selected chemistries can be used to etch magnetic metal alloys with a smooth etch profile and this general strategy can be applied to design chemically based etch processes to enable the fabrication of highly integrated nanoscale MRAM devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Thin liquid film as an optical nonlinear-nonlocal medium and memory element in integrated optofluidic reservoir computer

Understanding light–matter interaction lies at the core of our ability to harness physical effects and to translate them into new capabilities realized in modern integrated photonics platforms. Here, we present the design and characterization of optofluidic components in an integrated photonics platform and computationally predict a series of physical effects that rely on thermocapillary-driven interaction between waveguide modes and topography changes of optically thin liquid dielectric film. Our results indicate that this coupling introduces substantial self-induced phase change and transmittance change in a single channel waveguide, transmittance through the Bragg grating waveguide, and nonlocal interaction between adjacent waveguides. We then employ the self-induced effects together with the inherent built-in finite relaxation time of the liquid film, to demonstrate that the light-driven deformation can serve as a reservoir computer capable of performing digital and analog tasks, where the gas–liquid interface operates both as a nonlinear actuator and as an optical memory element.

42 ENGINEERING↗
DOE OSTI2020

CPU and memory efficient coherent mode decomposition for the partially coherent x-ray simulations

In this work, the method of the Coherent Mode Decomposition (CMD) is applied to numerical wave propagation calculations for partially-coherent X-rays, using the Fourier optics and compatible methods. Its CPU and memory efficiency is discussed in various cases of the wavefront at the source and the beam waist. With the absence of the quadratic phase terms, the required sampling density of the electric fields is effectively reduced. The problem size is thus moderate and the method is feasible to be implemented on a single-node CPU server. In other cases, the same argument holds with proper treatments of the quadratic phase terms. Tests on CMD and the modes propagation are done for the case of the Coherent Hard X-ray beamline of the National Synchrotron Light Source II, using the Synchrotron Radiation Workshop software. We observe a few hundred or less dominant decomposed modes that resemble the electric fields converge to the wavefront intensity at a high accuracy of over 99%.

36 MATERIALS SCIENCE↗
DOE OSTI2015

WE-A-207-01: Memorial Lecturer

The Medical Physics community lost one of its early pioneers in radiation oncology physics, Jacques Ovadia, who passed away in April of 2014 at the age of 90. Jacques received his Ph.D. in Nuclear Physics from the University of Illinois at Urbana in 1951. Subsequently, under the guidance of John Laughlin, he was introduced to the field of Medical Physics. When John moved to Memorial Sloan Kettering, Jacques followed him. There he gained clinical experience and expertise in the then cutting-edge field of high energy electron beam therapy. In 1956, Jacques joined Dr. Erich Uhlmann at Michael Reese Hospital in Chicago where one of the country’s first high energy medical linear accelerators had just been installed. During his 35 year tenure, Dr. Ovadia built a strong Medical Physics department that merged in 1984 with that of the University of Chicago. Jacques pioneered the use of high energy electron beams to treat deep seated tumors, multiple-field chest wall irradiation with variable electron energies, and even anticipated the current interest in high energy electron beam grid-therapy. At an early stage, he introduced a simulator, computerized treatment planning and in-house developed record and verify software. He retired in 1990 as Professor emeritus in Radiation and Cellular Biology at the University of Chicago. Dr. Ovadia was an early and strong supporter of AAPM. He was present at the Chicago ROMPS meeting where the decision was made to form an independent professional society for medical physics. He served as AAPM president in 1976. Jacques Ovadia is survived by his wife of 58 years, Florence, their daughter Corinne Graefe and son Marc Ovadia, MD, as well as four grandchildren and one great-grandchild. Jacques’ dynamic and ever enthusiastic personality inspired all who collaborated with him. He will be greatly missed.

60 APPLIED LIFE SCIENCES↗