Search NASA⌕ Search

SEARCH · Search NASA

Results for “memory”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Multiferroic and Related Hysteretic Behavior in Ferromagnetic Shape Memory Alloys

In this work, we combine a Ginzburg–Landau model for a ferroelastic transition with the theory of micromagnetism to study the magnetostructural behavior leading to multicaloric effects in ferromagnetic shape memory alloys. We analyze the ferroelastic transition under different conditions of temperature, stress and magnetic field and establish the corresponding phase diagram. On the one hand, our results show that the proper combination of both fields may be used to reduce the transition hysteresis and thus improve the reversibility of the related elastocaloric effects, superelasticity and stress-mediated magnetocaloric effects. On the other hand, the stress-free magnetic field-driven and thermally driven magnetostructural evolution provides physical insight into the low-temperature field-induced domain reorientation, from which we derive strategies to modify the operational temperature ranges and thus the corresponding (magnetic) shape-memory effect.

36 MATERIALS SCIENCE↗
DOE OSTI2017

High Damping of Lightweight TiNi-Ti{sub 2}Ni Shape Memory Composites for Wide Temperature Range Usage

A bimodal porous TiNi-Ti{sub 2}Ni shape memory alloy composite (SMAC) with 59% porosity was fabricated by sintering Ti-46at.%Ni elemental powders with pore-forming agent. The porous TiNi-Ti{sub 2}Ni SMAC contains two irregular pores of about 400 and 120 μm. We investigated the microstructure and pore morphology correlated with the mechanical properties and damping capacities of the SMAC. Ti{sub 2}Ni intermetallic phases with size of 1-3 μm were homogeneously distributed in the TiNi matrix. The porous TiNi-Ti{sub 2}Ni SMAC exhibits exceptionally high inverse mechanical quality factor (Q{sup −1}) of ~0.25 at < 40 °C, which is among the highest value reported for porous/dense shape memory alloys or composites to best of our knowledge, and it shows very high compressive fracture strain of about 25%. Moreover, the fabricated porous SMAC at relatively low strain amplitude can exhibit considerable high Q{sup −1} of 0.06 ~ 0.11 for a wide range of temperature between − 90 and 200 °C, which is attributed to the stress concentration distribution provided by the bimodal structure of pores and the massive interfaces between pore/matrix and TiNi/Ti{sub 2}Ni. These porous SMACs can be an ideal candidate for using as a lightweight damping material in the energy-saving applications.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Phase field simulation of martensitic transformation in pre-strained nanocomposite shape memory alloys

We show in this paper how strain engineering alters the fundamental characteristic of a martensitic transformation (MT) and gives it a new set of properties including large quasi-linear elastic strain response with nearly vanishing hysteresis and low elastic modulus. The work is motivated and inspired by a recent experimental study on elastic and inelastic (transformation) strain matching in a pre-strained nano-composite with Nb nanowires embedded in a NiTi shape memory alloy matrix. In particular, we demonstrate by computer simulation that dislocations at Nb/NiTi interfaces produced by the pre-straining are responsible for the unprecedented properties. Microstructural evolution captured in the simulations reveals that local stress fields associated with the dislocations regulate the nucleation and growth of martensite, turning the otherwise sharp, strong first-order transition into a continuous, high-order like transition. The simulations predict that the stress-strain hysteresis and modulus of the composite decrease with increasing amount of pre-strain, which agrees well with the experimental measurement. This study suggests a design strategy by introducing non-uniform stress fields for enhanced properties of shape memory alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2019

In situ investigation of stress-induced martensitic transformation in granular shape memory ceramic packings

Stress-induced martensitic transformation can occur in granular shape memory materials when individual particles experience high stresses and transform from a high-symmetry austenite phase to a low-symmetry martensite phase. This involves a highly heterogeneous distribution of the driving force and very low mechanical constraint for martensite nucleation, so the transformation behavior can be dramatically different from the well-documented case of monolithic solids. In this work, we investigate the stress-induced martensitic transformation in granular shape memory ceramic packings, which consist of single-crystal micro-particles of ZrO 2 -12 at%CeO 2 and ZrO 2 -15 at%CeO 2 . Using in situ neutron diffraction, we study how the phase fraction, lattice strain, and integral peak broadness evolve during external loading, unloading, and subsequent heating. Several peculiar features are discovered, including (i) a continuous mode of transformation with a wide range of transformation loads, (ii) co-evolution of the packing structure, contact deformation, and martensitic transformation, and (iii) a strong correlation between the peak broadening and the transformed phase fraction. In addition, we show the first direct evidence of reversible stress-induced martensitic transformation in granular materials. We additionally discuss the mechanism for martensite nucleation and growth in granular packings and show how that leads to the observed transformation characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Phase diagram of magnetic shape memory alloy Ni 50 Mn $50–x$ In $x$ , 0 < $x$ , 25 from first principles, via spin cluster expansion and phonon vibrational entropies

The metamagnetic shape memory Heusler alloy Ni 50 Mn $50–x$ In $x$ exhibits a rich phase diagram featuring competing magnetic states, coupled magnetic–structural phase transitions, and strong compositional sensitivity. Existing first-principles approaches struggletocapturetheintertwinedchemical, magnetic, andvibrationaleffectsinthesealloys, necessitating a more integrated modeling framework. We develop a spin cluster expansion (spin-CE) framework augmented by a quasi-harmonic phonon model to capture both configurational (chemical and magnetic) and vibrational contributions to the free energy of Ni 50 Mn $50–x$ In $x$ over the full compositional range 0 ≤ x ≤25. The spin-CE includes both chemical clusters and composition-dependent Ising spin interactions, with parameters fit to a first-principles density functional theory (DFT) dataset. Using this approach, we predict the complete magnetostructural phase diagram and transformation temperatures of Ni 50 Mn $50–x$ In $x$ across the composition space. We find that vibrational entropy alone is insufficient to reproduce the martensitic transformation in the magnetic shape memory alloy regime, highlighting the essential role of magnetism. Incorporating both magnetic and vibrational contributions allows us to reproduce all experimentally known phases, including the disappearance of the stable martensite phase at a critical In concentration and the Curie temperature of the austenite phase. The method also captures the transition with increasing In in martensite from antiferromagnetic to ferromagnetic order and predicts re-entrant ferromagnetism, though the latter occurs at higher In content than reported experimentally. We discuss possible sources of this discrepancy and highlight the broader applicability of the method to other magnetostructurally complex materials, where it may offer mechanistic insight and predictive design capabilities.

Cluster expansion↗
DOE OSTI2025

Microstructure-sensitive mechanical behavior of an additively manufactured psuedoelastic shape memory alloy

The additive manufacturing of shape memory alloys into complex geometries enables fabrication of advanced functional systems across a variety of fields and domains. This work presents results focused on the mechanical behavior of additively manufactured shape memory pseudoelastic NiTi. The deformation induced solid state phase transformation from austenite to martensite allows this system to accommodate large recoverable strains. This deformation behavior is fundamentally driven by crystal-scale transformation physics. Laser powder bed fusion processing reveals that the resulting microstructure, both grain morphology and crystallographic texture, is strongly dependent on the manufacturing processing history. Exhaustive mechanical testing demonstrates that these microstructural factors strongly impact both tensile and cyclic stress–strain behavior. Cyclic dissipative behavior, however, is similar across all tested microstructures following an initial transient period. Remarkably, analysis of spatial strain fields during tensile loading reveals two distinctly different localization “modes”. The first is initiation of localized deformation bands which continuously propagate through the tensile bar during loading. In the second mode localization is observed but lacks propagation; instead additional localization cites nucleate during subsequent loading. The latter phenomena is suspected to be driven by grain-scale deformation physics as the localized band morphologies coincide with grain morphologies. These phenomena strongly impact the resulting aggregate stress–strain behavior. Hence, manufacturers and designers of psuedoelastic functional components must at the very least consider the potential variability in properties when considering additive manufacturing processing. More ideally the process–structure–property relations can be used to further tailor and optimize final functional performance.

Additive manufacturing↗
DOE OSTI2022

Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool

We present machine learning methods to predict hydrologic features such as streamflow and soil moisture from spatially and temporally varying hydrological and meteorological data. Here, we used a temporal reduction technique to reduce computation and memory requirements and trained a Long Short-Term Memory (LSTM) network to predict soil moisture and streamflow over multiple watersheds. We show LSTM networks can be trained in a fraction of the time required by complex process-based and attention-based models such as Soil and Water Assessment Tool (SWAT) and GeoMAN without sacrificing accuracy. We also demonstrate that outside data - sourced from a watershed other than the target - can be used to train LSTM to comparable or even superior prediction accuracy. The success of LSTM in such spatially-inductive settings shows hydrologic features can be predicted with minimal prior knowledge of the watershed in question. Finally, we make all methodologies of this work publicly available as an end-to-end software pipeline that facilitates rapid prototyping of hydrologic learners.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Loss circulation prevention in geothermal drilling by shape memory polymer

Geothermal formations are naturally fractured with large fracture openings and networks. This can often lead to frequent drilling fluid loss events which is a major contributor to the cost and non-productive time in geothermal drilling. Development of smarter technologies and methods to tackle problem lost circulation is vital for geothermal drilling cost reduction required for geothermal energy to be recognized as a competitive alternative energy source. Mitigation of this problem and other drilling problems such as stuck pipe can minimize overall project cost. In this report, a thermoset shape memory polymer that can be activated by formation natural heat was assessed to seal near wellbore fractures in geothermal wells. The performance of the shape memory polymer (SMP) was evaluated using artificial fractures created in aluminum discs and cylindrical granite cores. Rheology and particle size distribution were considered. A novel testing setup was built for this work, which allows testing of sealing efficiency under dynamic conditions at high temperature. Analysis showed that the SMP has efficiently succeeded in forming a strong plug inside the fractures and stopped fluid loss at high sealing pressure. This smart loss circulation material can expand within the fractures to reduces non-drilling time and strengthen the wellbore in high-temperature drilling operations.

58 GEOSCIENCES↗
DOE OSTI2024

Active interlocking metasurfaces enabled by shape memory alloys

Interlocking metasurfaces (ILMs) are a newly developed joining technology that relies on arrays of interlocking features that transmit force and constrain motion between adjoining bodies in one or more directions. This study explores harnessing the shape memory effect (SME) in Nickel-Titanium shape memory alloys (NiTi SMAs) in structures fabricated using additive manufacturing (AM) to advance the development of active ILMs by creating unit cells that open or close at specific temperatures. The study encompasses designing and fabricating two distinct interlocking array configurations using near-equiatomic NiTi powder and the laser powder bed fusion (L-PBF) AM technique, following a previously developed AM process optimization framework to manufacture defect-free parts. To guide the design process, finite element analysis (FEA) was employed to predict strain values during engage-disengage cycles. The martensitic transformation characteristics of the ILMs were characterized. Thermomechanical testing revealed that the ILMs demonstrate high locking force once engaged, coupled with complete shape recovery and good cyclic stability. Digital image correlation (DIC) was also employed to validate the FEA predictions during the engage-disengage cycles. The results indicate that NiTi SMA-based ILMs can be designed and fabricated into complex shapes using L-PBF. By leveraging the SME, the functionality of an ILM can be improved upon. The combination of computational modeling, additive manufacturing, and thermomechanical and physical property characterization provides a framework for designing future ILMs out of active materials.

Additive manufacturing↗
DOE OSTI2018

2D nanocomposite of hexagonal boron nitride nanoflakes and molybdenum disulfide quantum dots applied as the functional layer of all-printed flexible memory device

Highlights: • We have proposed a flexible, rewritable and NVM device based on an advanced 2D nanocomposite. • 2D nanocomposite is synthesized by mixing hBN flakes and MoS{sub 2} QDs. • Entire device was fabricated through extremely simple and highly controllable printing technology. • The RRAM device was tested electrically, mechanically, thermally and chemically. - Abstract: In this study, we have proposed a flexible, rewritable and nonvolatile memory device based on an advanced 2D nanocomposite of hexagonal boron nitride (hBN) flakes and molybdenum disulfide quantum dots (MoS{sub 2} QDs). Complete device fabrication was carried out by using extremely simple and highly controllable all printed technology. The electrical characteristics exhibited by the as developed memory devices included the switching ratio, electrical endurance and retention time of ∼10{sup 3}, 10{sup 3} and 10{sup 4} respectively. The device turned ON and OFF at the SET and RESET threshold voltages of +1.4 V and −1 V respectively. The obtained results of electrical and thermal characterizations exhibited that the switching ratio decreases via either increasing temperature (300 K–380 K) or device size (42 μm–100 μm) hence verifying the formation of conductive filament through the functional layer. Moreover, no major degradation in the switching characteristics was observed even after 1500 bending cycles.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Computational study of clot formation in aneurysms treated with shape memory polymer foam

To prevent aneurysmal rupture, intracranial aneurysms are often treated with endovascular metal coils that fill the aneurysm sac and stimulate thrombus formation, thereby isolating the aneurysm from the arterial flow. Despite its widespread use, this method can result in suboptimal outcomes leading to aneurysm recurrence. Recently, shape memory polymer foam has been proposed as an alternative aneurysm filler. In this work, a computational thrombus model is used to predict the clotting response within idealized 2D aneurysms virtually treated with foam. The results are compared to previously reported clot formation predictions in identical 2D aneurysm geometries filled with simplified endovascular metal coil shapes. Additionally, each of the foam-filled aneurysms reached at least 94% thrombus occlusion regardless of foam pore size or orientation, whereas the final thrombus occlusion within the coil-filled aneurysms varied from 80.8 to 92.2% with many of the cases leaving large areas in the aneurysm neck unfilled. Based on the simulations presented here, shape memory polymer foams may be able to produce more predictable, uniform, and complete clotting results than bare metal coils, independent of foam geometry or orientation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

High-energy synchrotron radiation X-ray diffraction measurements during in situ aging of a NiTi-15 at. % Hf high temperature shape memory alloy

NiTiHf high temperature shape memory alloys (HTSMAs) have begun to show considerable progress in their ability to be manufactured and are now on the cusp of becoming viable alloys for use in the aerospace industry, mainly as actuation devices. By aging the alloys at intermediate temperatures between 450 and up to 700 °C, Ni-rich NiTiHf HTSMAs form the H-phase, a Ni-rich nanoscale precipitate which can be used to strengthen the alloy and improve the shape memory response; however, precipitate phase evolution and growth has not yet been studied in-situ nor have the effects of previous thermo-mechanical processing on their formation. In this study, H-phase precipitate formation is observed experimentally in situ during aging of a Ni-rich NiTiHf alloy using high energy synchrotron radiation X-ray diffraction (SR-XRD) measurements. The effects of prior thermo-mechanical treatments, i.e. hot and cold rolling, on its formation rate and coarsening rate are compared. It is shown that growth of the H-phase can be observed in situ using SR-XRD and the H-phase formation rate seems to exhibit linear growth when aging at 550 °C, while increasing the temperature to 650 °C shows a logarithmic trend that plateaus after approximately 1 h of aging. Other properties such as incubation time, austenite lattice strains, texture evolution, and overall comparison between hot and cold rolling are discussed. New potential peak age conditions are suggested based on the diffraction data and evaluated using three-point bending and TEM imaging.

36 MATERIALS SCIENCE↗
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↗
DOE OSTI2020

Tuning the Mechanical Properties of Shape Memory Metallic Glass Composites with Brick and Mortar Designs

We use molecular dynamics simulations to characterize the tensile deformation and failure of shape memory alloy-bulk metallic glass composites (BMGCs). As the failure in BMGCs shifts from the MG matrix to the second phase different strategies are required to further improve their mechanical properties. Results show that a staggered brick BMGC displays enhanced ductility with mild strength loss following a homogeneously distributed plasticity. This demonstrates that judiciously designed BMGCs can effectively synergize the mechanical properties of metallic glasses and shape memory alloys by effectively distributing stress and preventing the generation of critical shear bands and premature failure while preserving strength.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Freestanding Oxide Ferroelectric Tunnel Junction Memories Transferred onto Silicon

Crystalline oxide ferroelectric tunnel junctions allow persistent encoding of information in electric polarization, featuring nondestructive readout and scalability that can exceed current commercial high-speed, nonvolatile ferroelectric memories. Yet, the well-established fabrication of epitaxial devices on oxide substrates is difficult to adapt to silicon substrates for integration into complementary metal-oxide-semiconductor electronics. In this work, we report ferroelectric tunnel junctions based on 2.8 nm-thick BaTiO 3 films grown epitaxially on SrTiO 3 growth substrates, released, and relaminated onto silicon. The performance of the transferred devices is comparable to devices characterized on the oxide substrate, suggesting a viable route toward next-generation nonvolatile memories broadly integrable with different materials platforms.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

A Dual Approach in Direct Ink Writing of Thermally Cured Shape Memory Rubber Toughened Epoxy

Bisphenol A-based epoxies are much used in a wide range of composite and coating applications due to their excellent thermomechanical properties. However, their 3D printability remains a challenge with most reported materials suffering from high brittleness and low toughness. In this work, we have described especially modified epoxy resins that enable 3D printing with both fast and slow curing rates. These materials exhibit greatly enhanced toughness, tunable thermomechanical properties, and excellent shape memory behavior. Two different printing systems, including a two-part static mixing printhead and a single extrusion printhead, were developed for fast- and slow-curing epoxies, respectively. The rheology of inks in both systems has been modified into printable thixotropic fluids with the aid of silica nanoparticles and other additives. Epoxide-functionalized telechelic polybutadiene was added into the resins, which are then introduced inside the epoxy network after cross-linking. The addition of polybutadiene rubber significantly improves the toughness (over 135%), fracture strain (over 200%), and shape memory behavior. By adding different amounts of the rubber telechelic, thermomechanical properties, including modulus, elongation, and Tg of epoxy, can be well controlled in a wide range to satisfy different applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Off-resonant Raman quantum memory in impurity crystals: signal-to-noise ratio analysis

The possibility of implementing an off-resonant Raman scheme of optical quantum memory on the basis of an ensemble of three-level atoms is investigated under the condition of equal polarisations of resonant transitions forming the Λ-scheme. The developed model is used to analyse the signal-to-noise ratio at the output of an optical quantum memory device in {sup 143}Nd{sup 3+} : Y {sup 7}LiF{sub 4}. It is shown that this ratio can significantly exceed unity for single-photon input pulses. The required values of the parameters can be obtained by using an impurity crystal in the form of a whispering-gallery mode ring resonator. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Mechanisms enabling reconfigurability and long-term retention in vanadium oxide electrochemical memory

Phase coexistence in nanoscale electrochemical random-access memory (ECRAM) has recently been demonstrated to enable both information storage and extraordinary reconfigurability. These proof-of-principle demonstrations have left the mechanistic details of such a process unresolved. Particularly, the mechanisms that stabilize the multiple phases, and the underlying processes behind sustained memory retention, remain unclear, and are necessary to design such devices. Here we report microscale ECRAM devices composed of V⁢O𝑥, which enables us to directly probe the active region in an operando fashion using optical techniques. Using Raman mapping, we show the phase coexistence driven by the electrochemical injection of O vacancies to be spatially uniform (i.e., with no filaments). The stability was observed to be unusually long, with 1% loss over 14 years in ambient conditions. First-principles calculations of the oxygen vacancy formation energies in V⁢O 𝑥 further support the thermodynamic coexistence of multiple V⁢O 𝑥 phases and clarify the origin of the observed long-term retention in the ECRAM devices. Further, we demonstrate single devices that can be voltage programmed to exhibit synaptic, neuronal, and reconfigurable logic gate functionalities. Furthermore, we not only uncover the phase coexistence mechanism that may help device design, but also demonstrate the circuit-level applications of reconfigurability.

Electrical conductivity↗