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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Caloric Effects Induced by Uniform and Non-uniform Stress in Shape-Memory Materials

A Ginzburg–Landau model is developed that is adequate to describe a square-to-rectangle martensitic transition with associated shape-memory and superelastic properties. Using this model we study caloric effects in the vicinity of the martensitic transition induced by stress and we compare the case of a uniform uniaxial stress and the case of a non-uniform continuous distribution of stresses that produce bending of the material. The former case corresponds to an elastocaloric effect and the latter corresponds to a flexocaloric effect. The aim of the work is to quantitatively compare both cases, which we show must be accomplished in terms of equal amounts of exchanged mechanical work. It is then obtained that the flexocaloric effect is more efficient for low exchanged work but less efficient for large exchanged work.

36 MATERIALS SCIENCE↗
NASA NTRS2007

Correlation between Mechanical Behavior and Actuator-type Performance of Ni-Ti-Pd High-temperature Shape Memory Alloys

High-temperature shape memory alloys in the NiTiPd system are being investigated as lower cost alternatives to NiTiPt alloys for use in compact solid-state actuators for the aerospace, automotive, and power generation industries. A range of ternary NiTiPd alloys containing 15 to 46 at.% Pd has been processed and actuator mimicking tests (thermal cycling under load) were used to measure transformation temperatures, work behavior, and dimensional stability. With increasing Pd content, the work output of the material decreased, while the amount of permanent strain resulting from each load-biased thermal cycle increased. Monotonic isothermal tension testing of the high-temperature austenite and low temperature martensite phases was used to partially explain these behaviors, where a mismatch in yield strength between the austenite and martensite phases was observed at high Pd levels. Moreover, to further understand the source of the permanent strain at lower Pd levels, strain recovery tests were conducted to determine the onset of plastic deformation in the martensite phase. Consequently, the work behavior and dimensional stability during thermal cycling under load of the various NiTiPd alloys is discussed in relation to the deformation behavior of the materials as revealed by the strain recovery and monotonic tension tests.

Bigelow, Glen S.↗
DOE OSTI2017

Later Life Changes in Hippocampal Neurogenesis and Behavioral Functions After Low-Dose Prenatal Irradiation at Early Organogenesis Stage

Purpose: To investigate long-term changes in behavioral functions of mice after exposure to low-dose prenatal radiation at an early organogenesis stage. Methods and Materials: Pregnant C57BL/6J mice were irradiated (20 cGy) at postcoitus day 5.5. The male and female offspring were subjected to different behavioral assays for affective, motor, and cognitive functions at 3, 6, and 12 months of age. Behavioral functions were further correlated with the population of CA1 and CA3 pyramidal neurons and immature neurons in hippocampal dentate gyrus. Results: Prenatally exposed mice of different age groups showed a sex-specific pattern of sustained changes in behavioral functions. Male mice showed significant changes in anxiety-like phenotypes, learning, and long-term memory at age 3 months. At 6 months of age such behavioral functions were recovered to a normal level but could not be sustained at age 12 months. Female mice showed an appreciable recovery in almost all behavioral functions at 12 months. Patterns of change in learning and long-term memory were comparable to the population of CA1 and CA3 pyramidal neurons and doublecortin-positive neurons in hippocampus. Conclusion: Our finding suggests that prenatal (early organogenesis stage) irradiation even at a lower dose level (20 cGy) is sufficient to cause potential changes in neurobehavioral function at later stages of life. Male mice showed relatively higher vulnerability to radiation-induced neurobehavioral changes as compared with female.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Model fusion with physics-guided machine learning: Projection-based reduced-order modeling

The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the rapid advancement of data-driven and especially deep learning models in the field of fluid mechanics. Although these methods are proven successful for many applications, there is a grand challenge of improving their generalizability. This is particularly essential when data-driven models are employed within outer-loop applications like optimization. In this work, we put forth a physics-guided machine learning (PGML) framework that leverages the interpretable physics-based model with a deep learning model. Leveraging a concatenated neural network design from multi-modal data sources, the PGML framework is capable of enhancing the generalizability of data-driven models and effectively protects against or inform about the inaccurate predictions resulting from extrapolation. We apply the PGML framework as a novel model fusion approach combining the physics-based Galerkin projection model and long- to short-term memory (LSTM) network for parametric model order reduction of fluid flows. We demonstrate the improved generalizability of the PGML framework against a purely data-driven approach through the injection of physics features into intermediate LSTM layers. Our quantitative analysis shows that the overall model uncertainty can be reduced through the PGML approach, especially for test data coming from a distribution different than the training data. Moreover, we demonstrate that our approach can be used as an inverse diagnostic tool providing a confidence score associated with models and observations. The proposed framework also allows for multi-fidelity computing by making use of low-fidelity models in the online deployment of quantified data-driven models.

42 ENGINEERING↗
NASA NTRS2007

Reusable, Extensible High-Level Data-Distribution Concept

A framework for high-level specification of data distributions in data-parallel application programs has been conceived. [As used here, distributions signifies means to express locality (more specifically, locations of specified pieces of data) in a computing system composed of many processor and memory components connected by a network.] Inasmuch as distributions exert a great effect on the performances of application programs, it is important that a distribution strategy be flexible, so that distributions can be adapted to the requirements of those programs. At the same time, for the sake of productivity in programming and execution, it is desirable that users be shielded from such error-prone, tedious details as those of communication and synchronization. As desired, the present framework enables a user to refine a distribution type and adjust it to optimize the performance of an application program and conceals, from the user, the low-level details of communication and synchronization. The framework provides for a reusable, extensible, data-distribution design, denoted the design pattern, that is independent of a concrete implementation. The design pattern abstracts over coding patterns that have been found to be commonly encountered in both manually and automatically generated distributed parallel programs. The following description of the present framework is necessarily oversimplified to fit within the space available for this article. Distributions are among the elements of a conceptual data-distribution machinery, some of the other elements being denoted domains, index sets, and data collections (see figure). Associated with each domain is one index set and one distribution. A distribution class interface (where "class" is used in the object-oriented-programming sense) includes operations that enable specification of the mapping of an index to a unit of locality. Thus, "Map(Index)" specifies a unit, while "LocalLayout(Index)" specifies the local address within that unit. The distribution class can be extended to enable specification of commonly used distributions or novel user-defined distributions. A data collection can be defined over a domain. The term "data collection" in this context signifies, more specifically, an abstraction of mappings from index sets to variables. Since the index set is distributed, the addresses of the variables are also distributed.

James, Mark↗
DOE OSTI2026

Physical Modeling and Design of a Nonvolatile Optically Gated High‐Power Diamond Transistor

In this work, we present the theory and modeling framework of a diamond optically gated junction field‐effect transistor (DOGFET). The device utilizes nitrogen substitutional centers in type‐1b diamond to optically modulate a p‐ boron doped diamond channel. Using sub‐gap lasers with intensities as low as 100 W/cm 2 , electrons are optically excited from substitutional nitrogen sites to the conduction band of the diamond substrate, thus enabling the optical gate to exercise control on modulating the space‐charge region at the junction and therefore the channel conductivity. We show that the device can deliver a current of 7 μA/μm, or equivalently 1750 A/cm 2 , while switching at a frequency greater than 100 kHz, in a form factor of 5 μm 2 . The breakdown voltage is found to be greater than 1850 V, with a breakdown field strength of ~13 MV/cm. Moreover, the device supports nonvolatile operation with a “memory effect” enabling single transistor state retention. The presented simulation framework provides a physically grounded insight into the limits and opportunities of optoelectronic diamond systems.

Engineering - Electronic and electrical engineerin↗
DOE OSTI2013

On optimizing Jacobi–Davidson method for calculating eigenvalues in low dimensional structures using eight band k · p model

The paper presents two ways of improving the Jacobi–Davidson method for calculating the eigenvalues and eigenvectors described by eight-band k · p model for quantum dots and other low dimensional structures. First, the method is extended by the application of time reversal symmetry operator. This extension allows efficient calculations of the twofold degeneracy present in the multiband k · p model and other interior eigenvalues. Second, the preconditioner for the indefinite matrix which comes from the discretization of the eight band k · p Hamiltonian is presented. The construction of this preconditioner is based on physical considerations about energy band structure in the k · p model. On the basis of two real examples, it is shown that the preconditioner can significantly shorten the time needed to calculate the interior eigenvalues, despite the fact that the memory usage of the preconditioner and Hamiltionian is comparable. Finally, some technical details for implementing the eight band k · p Hamiltonian and the eigensolver are provided.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Realization of a reversible switching in TaO{sub 2} polymorphs via Peierls distortion for resistance random access memory

Transition-metal-oxide based resistance random access memory (RRAM) is a promising candidate for next-generation universal non-volatile memories. Searching and designing appropriate materials used in the memories becomes an urgent task. Here, a structure with the TaO{sub 2} formula was predicted using evolutionary algorithms in combination with first-principles calculations. This triclinic structure (T-TaO{sub 2}) is both energetically and dynamically more favorable than the commonly believed rutile structure (R-TaO{sub 2}). The metal-insulator transition (MIT) between metallic R-TaO{sub 2} and T-TaO{sub 2} (band gap: 1.0 eV) is via a Peierls distortion, which makes TaO{sub 2} a potential candidate for RRAM. The energy barrier for the reversible phase transition is 0.19 eV/atom and 0.23 eV/atom, respectively, suggesting low power consumption for the resistance switch. The present findings about the MIT as the resistance-switch mechanism in Ta-O system will stimulate experimental work to fabricate tantalum oxides based RRAM.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Verification and Validation of the ENDF/B-VII.1 v4.3m1 MPACT 51-Group Cross Section Library

The MPACT neutronics module of the Consortium for Advanced Simulation of Light Water Reactors (CASL) core simulator is a 3-D whole core transport code being developed for the Virtual Environment for Reactor Analysis (VERA) CASL toolset. Key characteristics of the MPACT code include (1) a subgroup method for resonance selfshielding and (2) a whole-core transport solver with a 2-D/1-D synthesis method. The MPACT code requires a cross section library to support all of its core simulation capabilities; a cross section library would be MPACT’s most influencing component for simulation accuracy. Due to the limitation of computing capacity even in high performance computing, the cross section library must be developed to enhance computational efficiency in memory and computing time without losing accuracy and generality. This requires a very coarse energy group structure with about 50 groups. Multigroup (MG) neutron cross section libraries for the CASL VERA-CS neutronics simulator MPACT have been created by using the AMPX/SCALE code package developed at Oak Ridge National Laboratory (ORNL). A new 51-group structure was developed for efficient simulation to be applicable for both pressurized water reactor (PWR) and boiling water reactor (BWR) simulations. New v4.3m1 MPACT 51-group library with the ENDF/B- VII.1 nuclear data were developed for MPACT. Since SCALE KENO has some issues in the probability table and has a low cutoff energy for the free gas model, this issue has been corrected. This study focused on the development of the ENDF/B-VII.1 v4.3m1 MPACT MG library, including verification and validation. This document includes the library generation methodology and procedure, the verification procedure, and benchmark results compared to continuous energy Monte Carlo results using newly developed cross section libraries.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Abstract. Quantifying the spatiotemporal dynamics in subsurface hydrological flows over a long time window usually employs a network of monitoring wells. However, such observations are often spatially sparse with potential temporal gaps due to poor quality or instrument failure. In this study, we explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. We use a well network that monitors the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer at the U.S. Department of Energy's Hanford site. This 10-year-long dataset contains hourly temperature, specific conductance, and groundwater table elevation measurements from 42 wells with gaps of various lengths. We employ a long short-term memory (LSTM) model to capture the temporal variations in the observed system behaviors needed for gap filling. The performance of the LSTM-based gap-filling method was evaluated against a traditional autoregressive integrated moving average (ARIMA) method in terms of error statistics and accuracy in capturing the temporal patterns of river corridor wells with various dynamics signatures. Our study demonstrates that the ARIMA models yield better average error statistics, although they tend to have larger errors during time windows with abrupt changes or high-frequency (daily and subdaily) variations. The LSTM-based models excel in capturing both high-frequency and low-frequency (monthly and seasonal) dynamics. However, the inclusion of high-frequency fluctuations may also lead to overly dynamic predictions in time windows that lack such fluctuations. The LSTM can take advantage of the spatial information from neighboring wells to improve the gap-filling accuracy, especially for long gaps in system states that vary at subdaily scales. While LSTM models require substantial training data and have limited extrapolation power beyond the conditions represented in the training data, they afford great flexibility to account for the spatial correlations, temporal correlations, and nonlinearity in data without a priori assumptions. Thus, LSTMs provide effective alternatives to fill in data gaps in spatially distributed time-series observations characterized by multiple dominant frequencies of variability, which are essential for advancing our understanding of dynamic complex systems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Data storage, image tube type

Method and apparatus for the storage of digital or analog electrical signals are provided by a memory storage system employing a conventional vidicon tube. At the beginning of an operating cycle, the vidicon is conditioned to accept electrical data input by exposing its photosensitive target to a short, high intensity light flash. A first electron beam scan of the photosensitive surface then sets up a charge pattern on the photosensitive target. A second electron beam scan of the photosensitive surface by an unmodulated electron beam then develops an output signal across an output resistor by using capacitive currents. The conditioning and scanning steps are operated repetitively at high speed using conventional television camera scan, sync, and power supply circuitry to provide a low cost data storage system.

Lipoma, P. C.↗
NASA NTRS1975

LSI computer for 1980's space mission

The low power, high reliability, SUMC-III-C computer is one member of the Space Ultrareliable Modular Computer (SUMC) family designed by NASA/MSFC to meet a wide range of missions through modularity in word size, throughput, and reliability. The SUMC-III-C, directed at such missions as the Large Space Telescope and Space Tug, uses custom designed CMOS/SOS LSI arrays with critical computer paths packaged on thick-film hybrids. This technology provides 300 to 600 logic gates per array with a typical stage delay of 3 to 5 nsec. SUMC-III-C is fully S/360 compatible at the user level, and includes virtual memory for the first time in an aerospace computer.

Clapp, W. A.↗
DOE OSTI2014

New algorithms for iterative matrix‐free eigensolvers in quantum chemistry

New algorithms for iterative diagonalization procedures that solve for a small set of eigen‐states of a large matrix are described. The performance of the algorithms is illustrated by calculations of low and high‐lying ionized and electronically excited states using equation‐of‐motion coupled‐cluster methods with single and double substitutions (EOM‐IP‐CCSD and EOM‐EE‐CCSD). We present two algorithms suitable for calculating excited states that are close to a specified energy shift (interior eigenvalues). One solver is based on the Davidson algorithm, a diagonalization procedure commonly used in quantum‐chemical calculations. The second is a recently developed solver, called the “Generalized Preconditioned Locally Harmonic Residual (GPLHR) method.” We also present a modification of the Davidson procedure that allows one to solve for a specific transition. The details of the algorithms, their computational scaling, and memory requirements are described. The new algorithms are implemented within the EOM‐CC suite of methods in the Q‐Chem electronic structure program. © 2014 Wiley Periodicals, Inc.

Zuev, Dmitry↗
DOE OSTI2024

Ductile Glassy Polymer Networks Capable of Large Plastic Deformation and Heat-Induced Elastic Recovery

Many thermoplastic polymers are ductile by combining strength and large deformations. These deformations are irreversible - known as plastic deformation. Elastomers can deform reversibly but have low strength. To this end, we developed glassy and ductile polyamide networks capable of large plastic deformation (>200% strain) and high strength (~50 MPa tensile strength and ~1500 MPa Young’s modulus), similar to those of polyolefins and Nylon-66. We discovered that hydrogen bonding between meta-phthalamide groups was essential to the ductility. Since these polyamide networks are covalently bonded, we demonstrated their unique durability by repeatable elastic recovery at elevated temperatures, exhibiting indifferent tensile properties in each cycle. Furthermore, when we fixed the strain during the elastic recovery, these polyamide networks actuated stresses of 9–18 MPa, among the highest reported in shape-memory polymer actuators. Here, we envision these ductile, glassy polymer networks as promising alternatives to ductile thermoplastics, given the combined benefit of ductility and durability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation

Abstract There is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.

Pawar, Suraj↗
DOE OSTI2024

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Effects of Oxygen Modification on the Structural and Magnetic Properties of Highly Epitaxial La0.7Sr0.3MnO3 (LSMO) thin films

Abstract La 0.7 Sr 0.3 MnO 3 , a strong semi-metallic ferromagnet having robust spin polarization and magnetic transition temperature ( T C ) well above 300 K, has attracted significant attention as a possible candidate for a wide range of memory, spintronic, and multifunctional devices. Since varying the oxygen partial pressure during growth is likely to change the structural and other physical functionalities of La 0.7 Sr 0.3 MnO 3 (LSMO) films, here we report detailed investigations on structure, along with magnetic behavior of LSMO films with same thickness (~30 nm) but synthesized at various oxygen partial pressures: 10, 30, 50, 100, 150, 200 and 250 mTorr. The observation of only ( 00 l ) reflections without any secondary peaks in the XRD patterns confirms the high-quality synthesis of the above-mentioned films. Surface morphology of the films reveals that these films are very smooth with low roughness, the thin films synthesized at 150 mTorr having the lowest average roughness. The increasing of magnetic T C and sharpness of the magnetic phase transitions with increasing oxygen growth pressure suggests that by decreasing the oxygen growth pressure leads to oxygen deficiencies in grown films which induce oxygen inhomogeneity. Thin films grown at 150 mTorr exhibits the highest magnetization with T C = 340 K as these thin films possess the lowest roughness and might exhibit lowest oxygen vacancies and defects. Interpretation and significance of these results in the 30 nm LSMO thin films prepared at different oxygen growth pressures are also presented, along with the existence and growth pressure dependence of negative remanent magnetization (NRM) of the above-mentioned thin films.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Effect of current compliance and voltage sweep rate on the resistive switching of HfO{sub 2}/ITO/Invar structure as measured by conductive atomic force microscopy

The electrical characterization of HfO{sub 2}/ITO/Invar resistive switching memory structure was studied using conductive atomic force microscopy (AFM) with a semiconductor parameter analyzer, Agilent 4156C. The metal alloy Invar was used as the metal substrate to ensure good ohmic contact with the substrate holder of the AFM. A conductive Pt/Ir AFM tip was placed in direct contact with the HfO{sub 2} surface, such that it acted as the top electrode. Nanoscale current-voltage (I-V) characteristics of the HfO{sub 2}/ITO/Invar structure were measured by applying a ramp voltage through the conductive AFM tip at various current compliances and ramp voltage sweep rates. It was found that the resistance of the low resistance state (RLRS) decreased with increasing current compliance value, but resistance of high resistance state (RHRS) barely changed. However, both the RHRS and RLRS decreased as the voltage sweep rate increased. The reasons for this dependency on current compliance and voltage sweep rate are discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗