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At least 883 records · Page 49

All sources NASA NTRS DOE OSTI

DOE OSTI2015

A Processable Shape Memory Polymer System for Biomedical Applications

Polyurethane shape memory polymers (SMPs) with tunable thermomechanical properties and advanced processing capabilities are synthesized, characterized, and implemented in the design of a microactuator medical device prototype. The ability to manipulate glass transition temperature ( T g ) and crosslink density in low-molecular weight aliphatic thermoplastic polyurethane SMPs is demonstrated using a synthetic approach that employs UV catalyzed thiol-ene “click” reactions to achieve postpolymerization crosslinking. Polyurethanes containing varying C=C functionalization are synthesized, solution blended with polythiol crosslinking agents and photoinitiator and subjected to UV irradiation, and the effects of number of synthetic parameters on crosslink density are reported. Thermomechanical properties are highly tunable, including glass transitions tailorable between 30 and 105 °C and rubbery moduli tailorable between 0.4 and 20 MPa. This new SMP system exhibits high toughness for many formulations, especially in the case of low crosslink density materials, for which toughness exceeds 90 MJ m –3 at select straining temperatures. To demonstrate the advanced processing capability and synthetic versatility of this new SMP system, a laser-actuated SMP microgripper device for minimally invasive delivery of endovascular devices is fabricated, shown to exhibit an average gripping force of 1.43 ± 0.37 N and successfully deployed in an in vitro experimental setup under simulated physiological conditions.

42 ENGINEERING↗

DOE OSTI2026

Tunable Interfacial to Filamentary Resistive Switching Mechanism in Room-Temperature-Grown Amorphous YBa 2 Cu 3 O x with Excess Cu Addition

Resistive switching technologies have the potential not only to create large efficiency gains in computer memory but also to revolutionize emerging fields such as neuromorphic computing. In this paper, we report on novel resistive switching behavior in devices made from room-temperature-grown Cu-rich amorphous YBa 2 Cu 3 O x (YBCO) films, a material otherwise well-known as a high-temperature superconductor. In Nb:STO substrate/amorphous YBCO film (≈200 nm)/metallic Cu (15 nm)/metallic Pt (15 nm) devices, we demonstrate that the resistive switching can be tuned between mechanisms involving extended areas of the YBCO/electrode interface and a single-point filamentary mechanism simply by changing the Cu content of the deposition target and hence in the films. Changing the Cu content can also be used to optimize the properties of the devices further, with devices with an added 15 mol % of Cu in YBCO initially providing an on/off ratio >100, switching endurance potential >6500 cycles, and state retention >2 × 10 4 s, all at low switching fields of 0.3 MV/cm. The amalgam of promising resistive switching properties, fast growth (150 nm/min) at room temperature, and tuneability of the switching mechanism indicates the strong potential of this proof-of-concept amorphous system for future memory applications.

Cu↗

DOE OSTI2015

Electrical performance of phase change memory cells with Ge{sub 3}Sb{sub 2}Te{sub 6} deposited by molecular beam epitaxy

Here, we report on the electrical characterization of phase change memory cells containing a Ge{sub 3}Sb{sub 2}Te{sub 6} (GST) alloy grown in its crystalline form by Molecular Beam Epitaxy (MBE). It is found that the high temperature growth on the amorphous substrate results in a polycrystalline film exhibiting a rough surface with a grain size of approximately 80–150 nm. A detailed electrical characterization has been performed, including I-V characteristic curves, programming curves, set operation performance, crystallization activation at low temperature, and resistance drift, in order to determine the material related parameters. The results indicate very good alignment of the electrical parameters with the current state-of-the-art GST, deposited by physical vapor deposition. Such alignment enables a possible employment of the MBE deposition technique for chalcogenide materials in the phase change memory technology, thus leading to future studies of as-deposited crystalline chalcogenides as integrated in electrical vehicles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

AENET–LAMMPS and AENET–TINKER : Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet–TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet–LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Livermore Big Artificial Neural Network Toolkit

LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

Essen, BrianVan↗

DOE OSTI2018

Revision of DASHMM: Dynamic Adaptive System for Hierarchical Multipole Methods

We present version 1.2.0 of DASHMM, a general library implementing hierarchical multipole methods using the asynchronous multi-tasking HPX-5 runtime system. Compared with the previous release [10], this new version: (1) enables execution in both shared and distributed memory architectures; (2) extends DASHMM’s infrastructure to support advanced multipole methods [18]; and (3) provides built-in implementations of both the Yukawa [15] potential and Helmholtz [16] potential in the low frequency regime. These additions have not impacted the user interface, which remains simple and extensible.

DeBuhr, J.↗

NASA NTRS1994

Discrete sensitivity derivatives of the Navier-Stokes equations with a parallel Krylov solver

This paper solves an 'incremental' form of the sensitivity equations derived by differentiating the discretized thin-layer Navier Stokes equations with respect to certain design variables of interest. The equations are solved with a parallel, preconditioned Generalized Minimal RESidual (GMRES) solver on a distributed-memory architecture. The 'serial' sensitivity analysis code is parallelized by using the Single Program Multiple Data (SPMD) programming model, domain decomposition techniques, and message-passing tools. Sensitivity derivatives are computed for low and high Reynolds number flows over a NACA 1406 airfoil on a 32-processor Intel Hypercube, and found to be identical to those computed on a single-processor Cray Y-MP. It is estimated that the parallel sensitivity analysis code has to be run on 40-50 processors of the Intel Hypercube in order to match the single-processor processing time of a Cray Y-MP.

Ajmani, Kumud↗

NASA NTRS2007

Effect of Shock on the Magnetic Properties of Pyrrhotite, the Martian Crust, and Meteorites

We performed planar shock recovery experiments on natural pyrrhotite at pressures up to 6.9 GPa. We find that high-field isothermal remanent magnetization in pyrrhotite is demagnetized up to 90% by shock due to preferential removal of low coercivity components of magnetization. Contrary to static experiments, we do not observe complete demagnetization. Post shock permanent changes in magnetic properties include increasing saturation isothermal remanent magnetization, bulk coercivity and lowtemperature memory, and changes in squareness of hysteresis. These changes are consistent with an increase in the volume fraction of single domain grains. The lack of magnetic anomalies over large Martian impact basins is not expected to be solely due to shock demagnetization of the crust. We find that pyrrhotite-bearing rocks and meteorites can retain records of Martian magnetic fields even if shocked to pressures approaching 7 GPa. However, some paleointensity techniques may underestimate this field.

Louzad, Karin L.↗

DOE OSTI2016

Livermore Big Artificial Neural Network Toolkit

Essen, Brian Van↗

DOE OSTI2020

A long short-term memory embedding for hybrid uplifted reduced order models

In this paper, we introduce an uplifted reduced order modeling (UROM) approach through the integration of standard projection based methods with long short-term memory (LSTM) embedding. Our approach has three modeling layers or components. In the first layer, we utilize an intrusive projection approach to model dynamics represented by the largest modes. The second layer consists of an LSTM model to account for residuals beyond this truncation. This closure layer refers to the process of including the residual effect of the discarded modes into the dynamics of the largest scales. However, the feasibility of generating a low rank approximation tails off for higher Kolmogorov n -width systems due to the underlying nonlinear processes. The third uplifting layer, called super-resolution, addresses this limited representation issue by expanding the span into a larger number of modes utilizing the versatility of LSTM. Therefore, our model integrates a physics-based projection model with a memory embedded LSTM closure and an LSTM based super-resolution model. In several applications, we exploit the use of Grassmann manifold to construct UROM for unseen conditions. We performed numerical experiments by using the Burgers and Navier-Stokes equations with quadratic nonlinearity. Finally, our results show robustness of the proposed approach in building reduced order models for parameterized systems and confirm the improved trade-off between accuracy and efficiency.

42 ENGINEERING↗

NASA NTRS2016

Engineering Design Tools for Shape Memory Alloy Actuators: CASMART Collaborative Best Practices and Case Studies

The primary goal of the Consortium for the Advancement of Shape Memory Alloy Research and Technology (CASMART) is to enable the design of revolutionary applications based on shape memory alloy (SMA) technology. In order to help realize this goal and reduce the development time and required experience for the fabrication of SMA actuation systems, several modeling tools have been developed for common actuator types and are discussed herein along with case studies, which highlight the capabilities and limitations of these tools. Due to their ability to sustain high stresses and recover large deformations, SMAs have many potential applications as reliable, lightweight, solid-state actuators. Their advantage over classical actuators can also be further improved when the actuator geometry is modified to fit the specific application. In this paper, three common actuator designs are studied: wires, which are lightweight, low-profile, and easily implemented; springs, which offer actuation strokes upwards of 200 at reduced mechanical loads; and torque tubes, which can provide large actuation forces in small volumes and develop a repeatable zero-load actuation response (known as the two-way shape memory effect). The modeling frameworks, which have been implemented in the design tools, are developed for each of these frequently used SMA actuator types. In order to demonstrate the versatility and flexibility of the presented design tools, as well as validate their modeling framework, several design challenges were completed. These case studies include the design and development of an active hinge for the deployment of a solar array or foldable space structure, an adaptive solar array deployment and positioning system, a passive air temperature controller for regulation flow temperatures inside of a jet engine, and a redesign of the Corvette active hatch, which allows for pressure equalization of the car interior. For each of the presented case studies, a prototype or proof-of-concept was fabricated and the experimental results and lessons learned are discussed. This analysis presents a collection of CASMART collaborative best practices in order to allow readers to utilize the available design tools and understand their modeling principles. These design tools, which are based on engineering models, can provide first-order optimal designs and are a basic and efficient method for either demonstrating design feasibility or refining design parameters. Although the design and integration of an SMA-based actuation system always requires application- and environment-specific engineering considerations, common modeling tools can significantly reduce the investment required for actuation system development and provide valuable engineering insight.

Design tools↗

DOE OSTI2015

Magnetization switching of a metallic nanomagnet via current-induced surface spin-polarization of an underlying topological insulator

We consider a thermally stable, metallic nanoscale ferromagnet (FM) subject to spin-polarized current injection and exchange coupling from the spin-helically locked surface states of a topological insulator (TI) to evaluate possible non-volatile memory applications. We consider parallel transport in the TI and the metallic FM, and focus on the efficiency of magnetization switching as a function of transport between the TI and the FM. Transport is modeled as diffusive in the TI beneath the FM, consistent with the mobility in the TI at room temperature, and in the FM, which essentially serves as a constant potential region albeit spin-dependent except in the low conductivity, diffusive limit. Thus, it can be captured by drift-diffusion simulation, which allows for ready interpretation of the results. We calculate switching time and energy consumed per write operation using self-consistent transport, spin-transfer-torque (STT), and magnetization dynamics calculations. Calculated switching energies and times compare favorably to conventional spin-torque memory schemes for substantial interlayer conductivity. Nevertheless, we find that shunting of current from the TI to a metallic nanomagnet can substantially limit efficiency. Exacerbating the problem, STT from the TI effectively increases the TI resistivity. We show that for optimum performance, the sheet resistivity of the FM layer should be comparable to or larger than that of the TI surface layer. Thus, the effective conductivity of the FM layer becomes a critical design consideration for TI-based non-volatile memory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

Unipolar resistive switching behavior of amorphous YCrO{sub 3} films for nonvolatile memory applications

Amorphous YCrO{sub 3} (YCO) films were prepared on Pt/TiO{sub 2}/SiO{sub 2}/Si substrate by pulsed laser deposition in order to investigate resistive switching (RS) phenomenon. The Pt/YCO/Pt device showed stable unipolar RS with resistance ratio of ∼10{sup 5} between low and high resistance states, excellent endurance and retention characteristics, as well as, non-overlapping switching voltages with narrow dispersions. Based on the x-ray photoelectron spectroscopy and temperature dependent switching characteristics, observed RS was mainly ascribed to the oxygen vacancies. Moreover, current-voltage characteristics of the device in low and high resistance states were described by Ohmic and trap controlled space–charge limited conduction mechanisms, respectively.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1993

Massively parallel computation of RCS with finite elements

One of the promising combinations of finite element approaches for scattering problems uses Whitney edge elements, spherical vector wave-absorbing boundary conditions, and bi-conjugate gradient solution for the frequency-domain near field. Each of these approaches may be criticized. Low-order elements require high mesh density, but also result in fast, reliable iterative convergence. Spherical wave-absorbing boundary conditions require additional space to be meshed beyond the most minimal near-space region, but result in fully sparse, symmetric matrices which keep storage and solution times low. Iterative solution is somewhat unpredictable and unfriendly to multiple right-hand sides, yet we find it to be uniformly fast on large problems to date, given the other two approaches. Implementation of these approaches on a distributed memory, message passing machine yields huge dividends, as full scalability to the largest machines appears assured and iterative solution times are well-behaved for large problems. We present times and solutions for computed RCS for a conducting cube and composite permeability/conducting sphere on the Intel ipsc860 with up to 16 processors solving over 200,000 unknowns. We estimate problems of approximately 10 million unknowns, encompassing 1000 cubic wavelengths, may be attempted on a currently available 512 processor machine, but would be exceedingly tedious to prepare. The most severe bottlenecks are due to the slow rate of mesh generation on non-parallel machines and the large transfer time from such a machine to the parallel processor. One solution, in progress, is to create and then distribute a coarse mesh among the processors, followed by systematic refinement within each processor. Elimination of redundant node definitions at the mesh-partition surfaces, snap-to-surface post processing of the resulting mesh for good modelling of curved surfaces, and load-balancing redistribution of new elements after the refinement are auxiliary steps expected to result in a robust low i/o system for very large finite element problems.

Parker, Jay↗

DOE OSTI2022

Study of carbon nanotube embedded honey as a resistive switching material

In this paper, natural organic honey embedded with carbon nanotubes (CNTs) was studied as a resistive switching material for biodegradable nonvolatile memory in emerging neuromorphic systems. CNTs were dispersed in a honey-water solution with the concentration of 0.2 wt% CNT and 30 wt% honey. The final honey-CNT-water mixture was spin-coated and dried into a thin film sandwiched in between Cu bottom electrode and Al top electrode to form a honey-CNT based resistive switching memory (RSM). Surface morphology, electrical characteristics and current conduction mechanism were investigated. The results show that although CNTs formed agglomerations in the dried honey-CNT film, both switching speed and the stability in SET and RESET process of honey-CNT RSM were improved. The mechanism of current conduction in CNT is governed by Ohm’s law in low-resistance state and the low-voltage range in high-resistance state, but transits to the space charge limited conduction at high voltages approaching the SET voltage.

36 MATERIALS SCIENCE↗

NASA NTRS2008

Intelligent Memory Module Overcomes Harsh Environments

Solar cells, integrated circuits, and sensors are essential to manned and unmanned space flight and exploration, but such systems are highly susceptible to damage from radiation. Especially problematic, the Van Allen radiation belts encircle Earth in concentric radioactive tori at distances from about 6,300 to 38,000 km, though the inner radiation belt can dip as low as 700 km, posing a severe hazard to craft and humans leaving Earth s atmosphere. To avoid this radiation, the International Space Station and space shuttles orbit at altitudes between 275 and 460 km, below the belts range, and Apollo astronauts skirted the edge of the belts to minimize exposure, passing swiftly through thinner sections of the belts and thereby avoiding significant side effects. This radiation can, however, prove detrimental to improperly protected electronics on satellites that spend the majority of their service life in the harsh environment of the belts. Compact, high-performance electronics that can withstand extreme environmental and radiation stress are thus critical to future space missions. Increasing miniaturization of electronics addresses the need for lighter weight in launch payloads, as launch costs put weight at a premium. Likewise, improved memory technologies have reduced size, cost, mass, power demand, and system complexity, and improved high-bandwidth communication to meet the data volume needs of the next-generation high-resolution sensors. This very miniaturization, however, has exacerbated system susceptibility to radiation, as the charge of ions may meet or exceed that of circuitry, overwhelming the circuit and disrupting operation of a satellite. The Hubble Space Telescope, for example, must turn off its sensors when passing through intense radiation to maintain reliable operation. To address the need for improved data quality, additional capacity for raw and processed data, ever-increasing resolution, and radiation tolerance, NASA spurred the development of the Radiation Tolerant Intelligent Memory Stack (RTIMS).

Source record↗

DOE OSTI2020

Neural-based time series forecasting of loss of coolant accidents in nuclear power plants

During the last few years, deep learning in neural networks has demonstrated impressive successes in the areas of computer vision, speech and image recognition, text generation, and many others. However, sensitive engineering areas such as nuclear engineering benefited less from these efficient techniques. In this work, deep learning expert systems are utilized to model and predict time series progression of a design-basis nuclear accident, featuring a loss of coolant accident. Two major findings are accomplished in this work. First, the ability to train expert systems with high accuracy, which could help nuclear power plant operators to figure out plant responses during the accident. Second, building fast, efficient, and accurate deep models to simulate nuclear phenomena, which could be valuable to nuclear computational science. In this work, large amount of time series data is obtained from simulation tools by simulating different conditions of the base-case/nominal accident scenario. Four critical outputs/responses are monitored during the accident (e.g. temperature, pressure, break flow rate, water level). Two approaches are adopted in this work. The first approach is to use feedforward deep neural networks (DNN) to fit all time steps and outputs in a single model. The second approach is to use long short-term memory (LSTM) to fit all time steps together for each reactor response separately. Both DNN and LSTM demonstrate very good performance in predicting the test and base-case scenarios, with accuracy as low as 92% and as high as 99%, where these test scenarios are unknown to the expert systems and are not included in the model training. In addition, both approaches demonstrate a significant reduction in computational costs, as the deep expert system is able to accurately predict the accident 100,000 times faster than the original simulation tool. Given sufficient data, the methodology adopted in this study demonstrates that DNN/LSTM expert systems can be used as a decision support system to model advanced time series phenomena within nuclear power plants with high accuracy and negligible computational costs.

42 ENGINEERING↗

DOE OSTI2015

Magnetic phase transitions in Ta/CoFeB/MgO multilayers

We study thin films and magnetic tunnel junction nanopillars based on Ta/Co{sub 20}Fe{sub 60}B{sub 20}/MgO multilayers by electrical transport and magnetometry measurements. These measurements suggest that an ultrathin magnetic oxide layer forms at the Co{sub 20}Fe{sub 60}B{sub 20}/MgO interface. At approximately 160 K, the oxide undergoes a phase transition from an insulating antiferromagnet at low temperatures to a conductive weak ferromagnet at high temperatures. This interfacial magnetic oxide is expected to have significant impact on the magnetic properties of CoFeB-based multilayers used in spin torque memories.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗