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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Micro-Ball-Lens Optical Switch Driven by SMA Actuator

The figure is a simplified cross section of a microscopic optical switch that was partially developed at the time of reporting the information for this article. In a fully developed version, light would be coupled from an input optical fiber to one of two side-by-side output optical fibers. The optical connection between the input and the selected output fiber would be made via a microscopic ball lens. Switching of the optical connection from one output fiber to another would be effected by using a pair of thin-film shape-memory-alloy (SMA) actuators to toggle the lens between two resting switch positions. There are many optical switches some made of macroscopic parts by conventional fabrication techniques and some that are microfabricated and, hence, belong to the class of microelectromechanical systems (MEMS). Conventionally fabricated optical switches tend to be expensive. MEMS switches can be mass-produced at relatively low cost, but their attractiveness has been diminished by the fact that, heretofore, MEMS switches have usually been found to exhibit high insertion losses. The present switch is intended to serve as a prototype of low-loss MEMS switches. In addition, this is the first reported SMA-based optical switch. The optical fibers would be held in V grooves in a silicon frame. The lens would have a diameter of 1 m; it would be held by, and positioned between, the SMA actuators, which would be made of thin films of TiNi alloy. Although the SMA actuators are depicted here as having simple shapes for the sake of clarity of illustration, the real actuators would have complex, partly net-like shapes. With the exception of the lens and the optical fibers, the SMA actuators and other components of the switch would be made by microfabrication techniques. The components would be assembled into a sandwich structure to complete the fabrication of the switch. To effect switching, an electric current would be passed through one of the SMA actuators to heat it above its transition temperature, thereby causing it to deform to a different "remembered" shape. The two SMA actuators would be stiff enough that once switching had taken place and the electrical current was turned off, the lens would remain latched in the most recently selected position. In a test, the partially developed switch exhibited an insertion loss of only -1.9 dB and a switching contrast of 70 dB. One the basis of prior research on SMA actuators and assuming a lens displacement of 125 m between extreme positions, it has been estimated that the fully developed switch would be capable of operating at a frequency as high as 10 Hz.

Yang, Eui-Hyeok↗
NASA NTRS1996

Performance of Integrated Fiber Optic, Piezoelectric, and Shape Memory Alloy Actuators/Sensors in Thermoset Composites

Recently, scientists and engineers have investigated the advantages of smart materials and structures by including actuators in material systems for controlling and altering the response of structural environments. Applications of these materials systems include vibration suppression/isolation, precision positioning, damage detection and tunable devices. Some of the embedded materials being investigated for accomplishing these tasks include piezoelectric ceramics, shape memory alloys, and fiber optics. These materials have some benefits and some shortcomings; each is being studied for use in active material design in the SPICES (Synthesis and Processing of Intelligent Cost Effective Structures) Consortium. The focus of this paper concerns the manufacturing aspects of smart structures by incorporating piezoelectric ceramics, shape memory alloys and fiber optics in a reinforced thermoset matrix via resin transfer molding (RTM).

Trottier, C. Michael↗
NASA NTRS1999

Vibration Analysis and Control of Flexible Beam by Using Smart Damping Structures

The temperature effects on frequency, loss factor and control of a flexible beam with a constrained viscoelastic layer and shape memory alloy layer (SMA) are discussed. It is shown that the temperature in the SMA (actuation) layer is very important in the determination of frequency and loss factor of such a structure. The effects of damping layer shear modulus and damping layer height as affected by the temperature are also discussed. As temperature plays such an important role, it is, therefore, imperative to evaluate temperature effects on the control of the system as well. Results with and without active control are discussed.

Chen, Q.↗
DOE OSTI2021

Temperature effects in spin-dependent Hall currents in an ideal skyrmion gas

The quest of novel reliable and fast performing logic and memory elements in classical and quantum computing requires discoveries of new effects in novel quantum materials. Skyrmions are such magnetic textures where electron scattering can essentially change the shape and location of the skyrmion, which can be used as a memory element being more efficient than domain walls as memory device in classical computing. Because the skyrmion motion is sensitive even to very small currents, we study electron scattering by skyrmions in an ideal skyrmion gas in ferromagnetic environment. In such systems, the direct and Hall currents become spin-dependent. For applications it is important to consider a Hall effect in the whole range of temperatures under the assumption of the skyrmion existence. In this study we find the nonmonotonic temperature dependence of the direct spin-up conductivity, i. e., the conductivity for the current directed along the applied electric field due to the electrons with the spin parallel to the ferromagnetic moment. Such a behavior contradicts the traditional understanding where the temperature only increases the value of the conductivity. The spin-down Hall conductivity is found to be even more dramatic exhibiting the conductivity sign change (i.e., the change in the current direction) with temperature for small skyrmion sizes. The found effects strongly depend on Fermi energy. The most pronounced dependencies take place if the Fermi energy is slightly below and above the bottom of the upper (spin-down) energy band of an ideal 2D electron gas. In addition, we also find that the direct and Hall resistivities, ρ xx and ρ xy , are independent of the exchange integral J for 2J > ε F .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2008

Effect of Upper-Cycle Temperature on the Load-Biased, Strain-Temperature Response of NiTi

Over the past decade, interest in shape memory alloy based actuators has increased as the primary benefits of these solid-state devices have become more apparent. However, much is still unknown about the characteristic behavior of these materials when used in actuator applications. Recently we have shown that the maximum temperature reached during thermal cycling under isobaric conditions could significantly affect the observed mechanical response of NiTi (55 wt% Ni), especially the amount of transformation strain available for actuation and thus work output. This investigation extends that original work to ascertain whether further increases in the upper-cycle temperature would produce additional improvement in the work output of the material, which has a stress-free Af of 113 oC, and to determine the optimum cyclic conditions. Thus, isobaric, thermal-cycle experiments were conducted in the aforementioned alloy at various stress levels from 50-300 MPa using upper-cycle temperatures of 165, 200, 230, 260, 290, 320 and 350 oC. The data indicated that the amount of applied stress influenced the transformation strain available in the system, as would be expected. However, the maximum temperature reached during the thermal excursion also plays a role in determining the transformation strain, with the maximum transformation strain being developed by thermal cycling to 290 oC. In situ, neutron diffraction showed that the differences in transformation strain were related to differences in martensite texture within the microstructure when cycling to different upper-cycle temperatures. Hence, understanding this effect is important to optimizing the operation of SMA-based actuators and could lead to new methods for processing and training shape memory alloys for optimal performance.

Padula, Santo, II↗
NASA NTRS2011

Effect of Upper-Cycle Temperature on the Load-Biased, Strain-Temperature Response of NiTi

Over the past decade, interest in shape memory alloy based actuators has increased as the primary benefits of these solid-state devices have become more apparent. However, much is still unknown about the characteristic behavior of these materials when used in actuator applications. Recently we have shown that the maximum temperature reached during thermal cycling under isobaric conditions could significantly affect the observed mechanical response of NiTi (55 wt% Ni), especially the amount of transformation strain available for actuation and thus work output. The investigation we report here extends that original work to ascertain whether further increases in the upper-cycle temperature would produce additional changes in the work output of the material, which has a stress-free austenite finish temperature of 113 C, and to determine the optimum cyclic conditions. Thus, isobaric, thermal-cycle experiments were conducted on the aforementioned alloy at various stresses from 50-300 MPa using upper-cycle temperatures of 165, 200, 230, 260, 290, 320 and 350 C. The data indicated that the amount of applied stress influenced the transformation strain, as would be expected. However, the maximum temperature reached during the thermal excursion also plays an equally significant role in determining the transformation strain, with the maximum transformation strain observed during thermal cycling to 290 C. In situ neutron diffraction at stress and temperature showed that the differences in transformation strain were mostly related to changes in martensite texture when cycling to different upper-cycle temperatures. Hence, understanding this effect is important to optimizing the operation of SMA-based actuators and could lead to new methods for processing and training shape memory alloys for optimal performance.

Padula, Santo, II↗
NASA NTRS2002

A Nonlinear Theory for Smart Composite Structures

The paper discusses the following: (1) Development of a completely coupled thermo-piezoelectric-mechanical theory for the analysis of composite shells with segmented and distributed piezoelectric sensor/actuators and shape memory alloys. The higher order displacement theory will be used to capture the transverse shear effects in anisotropic composites. The original theory will be modified to satisfy the stress continuity at ply interfaces. (2) Development of a finite element technique to implement the mathematical model. (3) Investigation of the coupled structures/controls interaction problem to study the complex trade-offs associated with the coupled problem.

Chattopadhyay, Aditi↗
NASA NTRS1989

Active control of buckling of flexible beams

The feasibility of using the rapidly growing technology of the shape memory alloys actuators in actively controlling the buckling of large flexible structures is investigated. The need for such buckling control systems is becoming inevitable as the design trends of large space structures have resulted in the use of structural members that are long, slender, and very flexible. In addition, as these truss members are subjected mainly to longitudinal loading they become susceptible to structural instabilities due to buckling. Proper control of such instabilities is essential to the effective performance of the structures as stable platforms for communication and observation. Mathematical models are presented that simulate the dynamic characteristics of the shape memory actuator, the compressive structural members, and the associated active control system. A closed-loop computer-controlled system is designed, based on the developed mathematical models, and implemented to control the buckling of simple beams. The performance of the computer-controlled system is evaluated experimentally and compared with the theoretical predictions to validate the developed models. The obtained results emphasize the importance of buckling control and suggest the potential of the shape memory actuators as attractive means for controlling structural deformation in a simple and reliable way.

Baz, A.↗
NASA NTRS1986

Progression-regression effects in tracking repeated patterns

Subjects used a position control system to perform compensatory tracking of a repeated input pattern. The input pattern was 20 seconds in duration and was either an arctangent function or the sum of two sine waves. Tracking error decreased with practice and increased with the addition of a concurrent memory task. The shape of the ensemble-average tracking error resembled the shape of the input velocity signal throughout these changes in performance. Regression analyses were used to parameterize these effects and compare these results with the predictions of several conceptualizations of perceptual-motor learning.

Jagacinski, R. J.↗
NASA NTRS2024

Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi

Formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.

Gabriel Plummer↗
NASA NTRSDate not supplied

Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi

Formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.

Gabriel Plummer↗
DOE OSTI2021

Data of High-Temperature Dynamic LCM Testing Setup

Data from high temperature dynamic sealing tests for various fracture widths, at various temperatures (degrees F), with 5 wt.% bentonite-based mud containing various material fiber contents, at 100 to 400 psi differential pressure. Data from pressure test and evaluation of the dynamic lost circulation materials (LCM) testing unit to reflect the condition of open and sealed fracture using fracture width of 1000 microns at 120 degrees F. Links to two papers based on the data - "Loss circulation prevention in geothermal drilling by shape memory polymer" which was published in Geothermics 89 (2021) 101943) as well as "Evaluating sealability of blended smart polymer and fiber additive for geothermal drilling with the effect of fracture opening size", published in the Journal of Petroleum Science and Engineering 206 (2021) 108998.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi

The formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.

Gabriel Plummer↗
DOE OSTI2021

A PIPS + SrI 2 (Eu) detector for atmospheric radioxenon monitoring

The PIPS–SrI 2 (Eu) is a prototype atmospheric radioxenon detection system designed at Oregon State University in support of international efforts towards monitoring clandestine nuclear weapon testing activities. This detector aims to address some shortcomings found in currently deployed beta–gamma atmospheric radioxenon detection systems, such as lackluster energy resolution and memory effect, by employing modern detection materials and readout. The system uses a PIPSBox, a silicon-based gas cell, for electron detection, and a pair of ultrabright, D-shaped SrI 2 (Eu) scintillators coupled to silicon photomultipliers for photon detection. A custom eight-channel digital pulse processor equipped with a field programmable gate-array (FPGA) identifies electron–photon coincidences between the volumes in near real-time. Gas samples of the four radioxenon isotopes of interest were independently measured with the PIPS–SrI 2 (Eu) detection system to determine energy resolution and efficiency. Application of FPGA-based coincidence discrimination in near real-time reduced the ambient background count rate by 95.85 ± 0.04%. Using parameters from the Xenon International gas processing unit and assuming a blank sample and zero memory effect the minimum detectable concentrations (MDCs) for the isotopes were calculated to be 0.12 ± 0.03, 0.27 ± 0.05, 0.15 ± 0.02, and 1.00 ± 0.08 mBq/m 3 air for 131m Xe, 133 Xe, 133m Xe, and 135 Xe, respectively. These MDC estimates compare well with other radioxenon detection systems employed in the International Monitoring System (IMS) and indicate that the PIPS–SrI 2 (Eu) is in compliance with the Comprehensive Nuclear Test-Ban-Treaty Organization (CTBTO) sensitivity requirement of ≤ 1 mBq/m 3 for 133 Xe.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Effect of hatch spacing and laser power on microstructure, texture, and thermomechanical properties of laser powder bed fusion (L-PBF) additively manufactured NiTi

This study systematically evaluates the effects of laser powder bed fusion additive manufacturing (L-PBF-AM) parameters (hatch spacing and laser power) on the thermomechanical behavior and microstructure of Ni 50.8 Ti 49.2 shape memory alloy. The samples were fabricated with hatch spacings from 40 to 240 µm and laser powers of 50 and 100 W at a constant scanning speed of 125 mm/s, resulting in parts with volumetric energy density levels from 55 to 666 J/mm 3 and two sets of linear energy densities of 0.4 and 0.8 J/mm. The results showed a reduced melt pool size and discontinuity of scan tracks with decreased laser power. Additionally, the porosity level was increased with larger hatch spacing and lower laser power. More notably, the transformation temperatures increased, and the critical stress, recoverable strain, and functional stability of samples improved with lower hatch spacing, where the recovery ratio of up to 90% was observed, regardless of the employed laser power. This study also discussed the relationship between the fabrication process and texture formation in the L-PBF-AM process. In conclusion, the advantage of L-PBF-AM was revealed in tailoring the microstructure from highly textured samples in [1 1 1] or [0 0 1] direction when hatch spacing lower than laser beam focused was employed, to the appearance of equiaxed solidification front with island grains and random orientations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗
NASA NTRS1990

Adaptive structures - Test hardware and experimental results

The facilities and procedures used at JPL to test adaptive structures such as the large deployable reflector (LDR) are described and preliminary results are reported. The applications of adaptive structures in future NASA missions are outlined, and the techniques which are employed to modify damping, stiffness, and isolation characteristics, as well as geometric changes, are listed. The development of adaptive structures is shown to be effective as a result of new actuators and sensors, and examples are listed for categories such as fiber optics, shape-memory materials, piezoelectrics, and electrorheological fluids. Some ground test results are described for laboratory truss structures and truss test beds, which are shown to be efficient and easy to assemble in space. Adaptive structures are shown to be important for precision space structures such as the LDR, and can alleviate ground test requirements.

Wada, Ben K.↗
DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗