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At least 1,459 records · Page 81

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DOE OSTI2018

Processing-induced strain glass states in a Ni 49.5 Ti 50.5 shape memory alloy

Shape memory alloys (SMAs) represent a revolutionary and innovative class of active materials which can provide potential solutions to many of today's engineering problems due to their compact form, high energy densities, and multifunctional capabilities. While many applications in the biomedical, aerospace, and automotive industries have already been investigated and realized for Nickel-Titanium (NiTi) based SMAs, the effects of restricting the ferroelastic transformation to nanosized domains is not well understood and the potential remains untapped. In binary NiTi, the martensitic transformation, which is characterized by long-range strain ordering (LRO), can be replaced with a strain glass transition, which consists of an LRO parent phase and a short-range strain ordered glassy phase. Such alloys have been named strain glass alloys (SGAs) due to the fact that they exhibit a glassy state which results from compositionally- or processing-induced strain. While SGAs do not exhibit a stress-free, temperature-induced macroscopic phase change, they still exhibit the strain recovery and actuation capabilities intrinsic to near equiatomic NiTi and other SMAs. It has been shown in the available literature that certain compositions, for example 51.5 at. % Nickel in binary NiTi, can create a strain glass; however, these compositionally-induced NiTi SGAs generally have transformation temperatures below 173 K and this will restrict their practical applications. In the present study, a new method for producing a strain glass phase in Ti-rich NiTi through sufficient plastic deformation via cold work is reported; the resulting SGA exhibits a temperature-induced ferroelastic recovery above room temperature. Additionally, the macroscopic actuation capabilities are improved when compared to both compositionally-induced SGAs and the base material due to the increased functional stresses of the SGA. To better understand the transition from an SMA to an SGA, Ni49.5Ti50.5 (at. %) rods were processed to several degrees of cold work and characterized via scanning and transmission electron microscopy, differential scanning calorimetry, thermomechanical testing, and synchrotron radiation x-ray diffraction. The experimental results indicate that twin size decreases with additional cold work and, around 45% thickness reduction, stress-free thermal cycling no longer results in a measurable phase transformation; however, mechanically-induced phase transformation is still possible, where fully recoverable strains in these SGAs were observed to be above 4.5% when loaded at room temperature and recovered at 150 °C.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows

Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows, but often require a set of ensemble forward simulations to estimate forecast covariance. On the other hand, predictor-corrector type nudging approaches are still attractive due to their simplicity of implementation when more complex methods need to be avoided. However, optimal estimate of nudging gain matrix might be cumbersome. In this paper, we put forth a fully nonintrusive recurrent neural network approach based on a long short-term memory (LSTM) embedding architecture to estimate the nudging term, which plays a role not only to force the state trajectories to the observations but also acts as a stabilizer. Furthermore, our approach relies on the power of archival data and the trained model can be retrained effectively due to power of transfer learning in any neural network applications. In order to verify the feasibility of the proposed approach, we perform twin experiments using Lorenz 96 system. Our results demonstrate that the proposed LSTM nudging approach yields more accurate estimates than both extended Kalman filter (EKF) and ensemble Kalman filter (EnKF) when only sparse observations are available. With the availability of emerging AI-friendly and modular hardware technologies and heterogeneous computing platforms, we articulate that our simplistic nudging framework turns out to be computationally more efficient than either the EKF or EnKF approaches.

42 ENGINEERING↗

DOE OSTI2018

Generation of narrow-band single-photon states via spontaneous parametric down-conversion for quantum memories in doped crystals

We present experimental results on the generation of narrow-band single-photon states via cavity-enhanced spontaneous parametric down-conversion in a PPLN crystal and demonstrate the feasibility of conditional preparation of single 867-nm photons with a 70-MHz linewidth, compatible with optical quantum memory devices based on Nd{sup 3+}-doped isototopically pure Y{sup 7}LiF{sub 4} crystals. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Advanced high-temperature fluid reactors, such as sodium-cooled fast reactors (SFRs) and molten salt–cooled reactors (MSCRs), require coolant purification systems to prevent fluid contamination and local freezing that can lead to plugging. Liquid sodium purification can be achieved with a cold trap, where the sodium temperature is reduced to a near-freezing point to precipitate out impurities. Automation of monitoring of the cold trap performance with machine learning algorithms can aid in early detection of incipient anomalies. An efficient approach to loss-of-coolant–type anomaly detection in a cold trap monitored with more than two dozen thermal-hydraulic sensors consists of a long short-term memory (LSTM) autoencoder. This work develops the uncertainty quantification of the LSTM autoencoder performance for cold trap anomaly detection using the Monte Carlo (MC) dropout method. The MC dropout methodology creates a distribution of sister distributions that all slightly differ from each other because of random neurons being turned off for testing. The variances of the sister network distributions are used to make an uncertainty interval. Our analysis shows that the uncertainty in the autoencoder performance is largest near the peak of the anomaly signal. Using the MC dropout method, we investigate the uncertainty in the anomaly detection with missing sensor inputs. This capability allows the reactor operator to evaluate resilience of the anomaly detection system and to make informed decisions about continuity of operation in the event of sensor failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Annular Flow Simulation Supported by Iterative In-Memory Mesh Adaptation

Various flow regimes exist in a boiling water reactor (BWR) as the steam quality increases in the uprising coolant flow, from bubbly flow, slug/churn flow, to annular flow. The annular flow is characterized by the presence of a fast-moving gas core and the surrounding liquid film flowing on the conduit wall. Additionally, entrained droplets can be observed in the gas core with ingested bubbles in the liquid film. The dynamics occurring on the wavy interface between the liquid film and gas core plays a crucial role in affecting the heat transfer rate and pressure drop within the BWR core. However, a fundamental understanding of annular flow is still lacking, partly due to the difficulty in obtaining detailed local data in annular flow experiments. In the current study, a novel simulation framework is developed for the annular flow by coupling a computational fluid dynamics flow solver with state-of-the-art meshing software. The gas-liquid interface is tracked with the level set method. Based on the computed flow solutions, the computational mesh is dynamically adapted in memory to meet the local mesh resolution requirement. This iterative simulation-adaptation framework can ensure the fine mesh resolution across the interface, which not only helps mitigate the mass conservation degradation known to level set methods but also improves the representation of dramatic interface topological changes such as wave breaking and droplet entrainment. The present investigation will shed light onto the complex interfacial processes involved in annular flow and generate much needed simulation data for annular flow modeling.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2017

Elastocaloric cooling of additive manufactured shape memory alloys with large latent heat

The stress-induced martensitic phase transformation of shape memory alloys (SMAs) is the basis for elastocaloric cooling. In this paper, we employ additive manufacturing to fabricate TiNi SMAs, and demonstrate compressive elastocaloric cooling in the TiNi rods with transformation latent heat as large as 20 J g -1 . Adiabatic compression on as-fabricated TiNi displays cooling ΔT as high as -7.5 °C with recoverable superelastic strain up to 5%. Unlike conventional SMAs, additive manufactured TiNi SMAs exhibit linear superelasticity with narrow hysteresis in stress–strain curves under both adiabatic and isothermal conditions. Microstructurally, we find that there are Ti 2 Ni precipitates typically one micron in size with a large aspect ratio enclosing the TiNi matrix. Finally, a stress transfer mechanism between reversible phase transformation in the TiNi matrix and mechanical deformation in Ti 2 Ni precipitates is believed to be the origin of the unique superelasticity behavior.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Optical quantum memory for noble-gas spins based on spin-exchange collisions

Optical quantum memories, which store and preserve the quantum state of photons, rely on a coherent mapping of the photonic state onto matter states that are optically accessible. Here we outline and characterize schemes to map the state of photons onto long-lived but optically inaccessible collective states of noble-gas spins. The mapping employs coherent spin-exchange interaction arising from random collisions with alkali vapor. We propose efficient storage strategies in two operating regimes and analyze their performance for several proposed experimental configurations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Role of point defects in stress-induced martensite transformations in NiTi shape memory alloys: A molecular dynamics study

Shape-memory properties of equiatomic NiTi rely on the thermal- or stress-induced reversible martensitic phase transformation between the $\textit{B}$2 and $\textit{B}$19' phases. Irradiation defects suppress thermal-induced transformations, but their effects on stress-induced transformations are poorly understood. In this work, we use molecular dynamics to investigate the effect of vacancies, vacancy clusters, interstitials, and antisite defects on stress-induced transformations. All defects suppress the transformation, but vacancy clusters do so to the greatest extent, while antisite defects do so to the least extent. The responsible mechanisms are grain boundary pinning and chemical disordering.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Effect of directed aging on nonlinear elasticity and memory formation in a material

Disordered solids often change their elastic response as they slowly age. Using experiments and simulations, we study how aging disordered planar networks under an applied stress affects their nonlinear elastic response. We are able to modify dramatically the elastic properties of our systems in the nonlinear regime. Using simulations, we study two models for the microscopic evolution of properties of such a material; the first considers changes in the material strength, while the second considers distortions in the microscopic geometry. Both models capture different aspects of the experiments including the encoding of memories of the aging history of the system and the dramatic effects on the material’s nonlinear elastic properties. Our results demonstrate how aging can be used to create complex elastic behavior in the nonlinear regime.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Modular Spiking Neural Circuits for Mapping Long Short-Term Memory on a Neurosynaptic Processor

Due to the distributed and asynchronous nature of neural computation through low-energy spikes, brain-inspired hardware systems offer high energy efficiency and massive parallelism. One such platform is the IBM TrueNorth neurosynaptic system. Recently, TrueNorth compatible representation learning algorithms have emerged, achieving close to the state-of-the-art performance in various data sets. However, its application in temporal sequence processing models, such as recurrent neural networks (RNNs), is still only at the proof of concept level. There is an inherent difficulty in capturing temporal dynamics of an RNN using spiking neurons, which is only exasperated by the hardware constraints in connectivity and synaptic weight resolution. This paper presents a design flow that overcomes these difficulties and maps a special case of recurrent networks called long short-term memory (LSTM) onto a spike-based platform. The framework utilizes various approximation techniques, such as activation discretization, weight quantization, and scaling and rounding, spiking neural circuits that implement the complex gating mechanisms, and a store-and-release technique to enable neuron synchronization and faithful storage. While the presented techniques can be applied to map LSTM to any spiking neural network (SNN) simulator/emulator, here we choose the TrueNorth chip as the target platform by adhering to its hardware constraints. Three LSTM applications, parity check, extended Reber grammar, and question classification, are evaluated. The tradeoffs among accuracy, performance, and energy tradeoffs achieved on TrueNorth are demonstrated. Finally, this is compared with the performance on an SNN platform without hardware constraints, which represents the upper bound of the achievable accuracy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks

Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT devices in smart grids. This paper investigates the IoT device modeling of a thermostatic load and implements the recurrent neural networks model for short-term load forecasting in this IoT-based thermostatic load. The recurrent neural network structure is leveraged to build a load forecasting model on temporal correlation. The temporal recurrent neural network layers including long short-term memory cells are employed to learn the data from both the simulation platform and New South Wales residential datasets. The simulation results are provided for demonstration.

electric load forecasting↗

DOE OSTI2024

Variation-Resilient FeFET-Based In-Memory Computing Leveraging Probabilistic Deep Learning

Reliability issues stemming from device level nonidealities of nonvolatile emerging technologies like ferroelectric field-effect transistors (FeFETs), especially at scaled dimensions, cause substantial degradation in the accuracy of in-memory crossbar-based AI systems. Here, in this work, we present a variation-aware design technique to characterize the device level variations and to mitigate their impact on hardware accuracy employing a Bayesian neural network (BNN) approach. An effective conductance variation model is derived from the experimental measurements of cycle-to-cycle (C2C) and device-to-device (D2D) variations performed on FeFET devices fabricated using 28 nm high-k metal gate technology. The variations were found to be a function of different conductance states within the given programming range, which sharply contrasts earlier efforts where a fixed variation dispersion was considered for all conductance values. Such variation characteristics formulated for three different device sizes at different read voltages were provided as prior variation information to the BNN to yield a more exact and reliable inference. Near-ideal accuracy for shallow networks (MLP5 and LeNet models) on the MNIST dataset and limited accuracy decline by ~3.8%–16.1% for deeper AlexNet models on CIFAR10 dataset under a wide range of variations corresponding to different device sizes and read voltages, demonstrates the efficacy of our proposed device-algorithm co-design technique.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Nanoscale compositional analysis of NiTi shape memory alloy films deposited by DC magnetron sputtering

The formation of surface oxide layer as well as compositional changes along the thickness for NiTi shape memory alloy thin films deposited by direct current magnetron sputtering at substrate temperature of 300 °C in the as-deposited condition as well as in the postannealed (at 600 °C) condition have been thoroughly studied by using secondary ion mass spectroscopy, x-ray photoelectron spectroscopy, and scanning transmission electron microscopy-energy dispersive x-ray spectroscopy techniques. Formation of titanium oxide (predominantly titanium dioxide) layer was observed in both as-deposited and postannealed NiTi films, although the oxide layer was much thinner (8 nm) in as-deposited condition. The depletion of Ti and enrichment of Ni below the oxide layer in postannealed films also resulted in the formation of a graded microstructure consisting of titanium oxide, Ni{sub 3}Ti, and B2 NiTi. A uniform composition of B2 NiTi was obtained in the postannealed film only below a depth of 200–250 nm from the surface. Postannealed film also exhibited formation of a ternary silicide (Ni{sub x}Ti{sub y}Si) at the film–substrate interface, whereas no silicide was seen in the as-deposited film. The formation of silicide also caused a depletion of Ni in the film in a region ∼250–300 nm just above the film substrate interface.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Directed aging, memory, and nature’s greed

Disordered materials are often out of equilibrium and evolve very slowly in a rugged and tortuous energy landscape. This slow evolution, referred to as aging, is deemed undesirable as it often leads to material degradation. However, we show that aging also encodes a memory of the stresses imposed during preparation. Because of inhomogeneous local stresses, the material itself decides how to evolve by modifying stressed regions differently from those under less stress. Because material evolution occurs in response to stresses, aging can be “directed” to produce sought-after responses and unusual functionalities that do not inherently exist. Aging obeys a natural “greedy algorithm” as, at each instant, the material simply follows the path of most rapid and accessible relaxation. Our experiments and simulations illustrate directed aging in examples in which the material’s elasticity transforms as desired because of an imposed deformation.