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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.
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Experiments have confirmed the feasibility of an innovative, new class of very simple, reliable, low mass, low packaging volume, and low-cost self-deployable structures for space and commercial applications.
Shape memory alloys (SMAs) are an enabling component in the development of compact, lightweight, durable, high-force actuation systems particularly for use where hydraulics or electrical motors are not practical. However, commercial shape memory alloys based on NiTi are only suitable for applications near room temperature, due to their relatively low transformation temperatures, while many potential applications require higher temperature capability. Consequently, a family of (Ni,Pt)(sub 1-x)Ti(sub x) shape memory alloys with Ti concentrations ranging from about 15 to 25 at.% have been developed for applications in which there are requirements for SMA actuators to exert high forces at operating temperatures higher than those of conventional binary NiTi SMAs. These alloys can be heat treated in the range of 500 C to produce a series of fine precipitate phases that increase the strength of alloy while maintaining a high transformation temperature, even in Ti-lean compositions.
In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.
Abstract Rare earth emitters enable critical quantum resources including spin qubits, single photon sources, and quantum memories. Yet, probing of single ions remains challenging due to low emission rate of their intra-4 f optical transitions. One feasible approach is through Purcell-enhanced emission in optical cavities. The ability to modulate cavity-ion coupling in real-time will further elevate the capacity of such systems. Here, we demonstrate direct control of single ion emission by embedding erbium dopants in an electro-optically active photonic crystal cavity patterned from thin-film lithium niobate. Purcell factor over 170 enables single ion detection, which is verified by second-order autocorrelation measurement. Dynamic control of emission rate is realized by leveraging electro-optic tuning of resonance frequency. Using this feature, storage, and retrieval of single ion excitation is further demonstrated, without perturbing the emission characteristics. These results promise new opportunities for controllable single-photon sources and efficient spin-photon interfaces.
We investigated the influence of thermal agitation on the electric field induced precessional magnetization switching probability with perpendicular easy axis by solving the Fokker-Planck equation numerically with finite difference method. The calculated results show that the thermal agitation during the reversal process crucially influences the switching probability. The switching probability can be achieved is only determined by the thermal stability factor Δ of the free layer, it is independent on the device dimension, which is important for the high density device application. Ultra-low error rate down to the order of 10{sup −9} can be achieved for the device of thermal stability factor Δ of 40. Low damping factor α material should be used for the free layer for high reliability device applications. These results exhibit potential of electric field induced precessional magnetization switching with perpendicular easy axis for ultra-low power, high speed and high density magnetic random access memory (MRAM) applications.
Magnetoelectric (ME) coupling in materials offer tremendous advantages in device functionality enabling technologies including advanced electronic memory, combining electronic speed, and efficiency with magnetic robustness. However, low cost polycrystalline ME materials are excluded from most commercial applications, operating only at cryogenic temperatures, impractically large electric/magnetic fields, or with low ME coefficients (1-100 mV/cm Oe). Despite this, the technological potential of single compound ME coupling has continued to drive research into multiferroics over the last two decades. Here we show that by manipulating the large induced atomic strain within the polycrystalline, room temperature multiferroic compound 0.7BiFeO{sub 3}–0.3PbTiO{sub 3}, we can induce a reversible, piezoelectric strain controlled ME effect. Employing an in situ neutron diffraction experiment, we have demonstrated that this piezomagnetoelectric effect manifests with an applied electric field >8 kV/mm at the onset of piezoelectric strain, engineered in to the compound by crystallographic phase mixing. This produces a remarkable intrinsic ME coefficient of 1276 mV/cm Oe, due to a strain driven modification to the oxygen sub-lattice, inducing an increase in magnetic moment per Fe{sup 3+} ion of +0.142 μ{sub B}. This work provides a framework for investigations into strain engineered nanostructures to realize low-cost ME devices designed from the atoms up, as well as contributing to the deeper understanding of single phase ME coupling mechanisms.
Inspired by the parallelism and efficiency of the brain, several candidates for artificial synapse devices have been developed for neuromorphic computing, yet a nonlinear and asymmetric synaptic response curve precludes their use for backpropagation, the foundation of modern supervised learning. Spintronic devices—which benefit from high endurance, low power consumption, low latency, and CMOS compatibility—are a promising technology for memory, and domain-wall magnetic tunnel junction (DW-MTJ) devices have been shown to implement synaptic functions such as long-term potentiation and spike-timing dependent plasticity. In this work, we propose a notched DW-MTJ synapse as a candidate for supervised learning. Using micromagnetic simulations at room temperature, we show that notched synapses ensure the non-volatility of the synaptic weight and allow for highly linear, symmetric, and reproducible weight updates using either spin transfer torque (STT) or spin–orbit torque (SOT) mechanisms of DW propagation. We use lookup tables constructed from micromagnetics simulations to model the training of neural networks built with DW-MTJ synapses on both the MNIST and Fashion-MNIST image classification tasks. Accounting for thermal noise and realistic process variations, the DW-MTJ devices achieve classification accuracy close to ideal floating-point updates using both STT and SOT devices at room temperature and at 400 K. Our work establishes the basis for a magnetic artificial synapse that can eventually lead to hardware neural networks with fully spintronic matrix operations implementing machine learning.
This review explores the intersection of bio-plausible artificial intelligence in the form of spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain, highlighting their collective potential for low-power edge computing environments. Through detailed investigation at the device, circuit, and system levels, we highlight the pivotal synergies between SNNs and IMC architectures. Additionally, we emphasize the critical need for comprehensive system-level analyses, considering the inter-dependencies among algorithms, devices, circuit, and system parameters, crucial for optimal performance. An in-depth analysis leads to the identification of key system-level bottlenecks arising from device limitations, which can be addressed using SNN-specific algorithm–hardware co-design techniques. This review underscores the imperative for holistic device to system design-space co-exploration, highlighting the critical aspects of hardware and algorithm research endeavors for low-power neuromorphic solutions.
Decoherence between qubits is a major bottleneck in quantum computations. Decoherence results from intrinsic quantum and thermal fluctuations as well as noise in the external fields that perform the measurement and preparation processes. With prescribed colored noise spectra for intrinsic and extrinsic noise, we present a numerical method, Quantum Accelerated Stochastic Propagator Evaluation (Q-ASPEN), to solve the time-dependent noise-averaged reduced density matrix in the presence of intrinsic and extrinsic noise. Q-ASPEN is arbitrarily accurate and can be applied to provide estimates for the resources needed to error-correct quantum computations. We employ spectral tensor trains, which combine the advantages of tensor networks and pseudospectral methods, as a variational ansatz to the quantum relaxation problem and optimize the ansatz using methods typically used to train neural networks. Here, the spectral tensor trains in Q-ASPEN make accurate calculations with tens of quantum levels feasible. We present benchmarks for Q-ASPEN on the spin-boson model in the presence of intrinsic noise and on a quantum chain of up to 32 sites in the presence of extrinsic noise. In our benchmark, the memory cost of Q-ASPEN scales as a low-order polynomial in the size of the system once the number of system states surpasses the number of basis functions used in the spectral expansion.
Resistive random-access memories are promising for nonvolatile memory and brain-inspired computing applications. High variability and low yield of these devices are key drawbacks hindering reliable training of physical neural networks. In this paper, we show that doping an oxide electrolyte, Al 2 O 3 , with electronegative metals makes resistive switching significantly more reproducible, surpassing the reproducibility requirements for obtaining reliable hardware neuromorphic circuits. Based on density functional theory calculations, the underlying mechanism is hypothesized to be the ease of creating oxygen vacancies in the vicinity of electronegative dopants due to the capture of the associated electrons by dopant midgap states and the weakening of Al-O bonds. These oxygen vacancies and vacancy clusters also bind significantly to the dopant, thereby serving as preferential sites and building blocks in the formation of conducting paths. Throughout this work, we validate this theory experimentally by implanting different dopants over a range of electronegativities in devices made of multiple alternating layers of Al 2 O 3 and WN and find superior repeatability and yield with highly electronegative metals, Au, Pt, and Pd. These devices also exhibit a gradual SET transition, enabling multibit switching that is desirable for analog computing.
With the recent emergence of large-scale knowledge dis- covery, data mining and social network analysis, irregular applications have gained renewed interest. Classic cache-based high-performance architectures do not provide optimal performances with such kind of workloads, mainly due to the very low spatial and temporal locality of the irregular control and memory access patterns. In this paper, we present a multi-node, multi-core, fine-grained multi-threaded shared-memory system architecture specifically designed for the execution of large-scale irregular applications, and built on top of three pillars, that we believe are fundamental to support these workloads. First, we offer transparent hardware support for Partitioned Global Address Space (PGAS) to provide a large globally-shared address space with no software library overhead. Second, we employ multi-threaded multi-core processing nodes to achieve the necessary latency tolerance required by accessing global memory, which potentially resides in a remote node. Finally, we devise hardware support for inter-thread synchronization on the whole global address space. We first model the performances by using an analytical model that takes into account the main architecture and application characteristics. We describe the hardware design of the proposed cus- tom architectural building blocks that provide support for the above- mentioned three pillars. Finally, we present a limited-scale evaluation of the system on a multi-board FPGA prototype with typical irregular kernels and benchmarks. The experimental evaluation demonstrates the architecture performance scalability for different configurations of the whole system.
MFiX was integrated with preconditioners and linear solver packages in Trilinos via MFiX, Fortran, C, and C++ wrappers. The MFiX wrapper interpreted the structure of the matrix and vector from MFiX while the Fortran wrapper transfered this information to a low-level language, C. The C-wrapper transformed the memory semantic between Fortran and C++ language. The C++ wrapper passed the matrix and vector to a Trilinos package and got the solution. This solution was transferred to MFiX via C++, C, Fortran, MFiX wrapper. The framework was used to integrate first, second, and third generation linear solvers in Trilinos with MFiX.
A star sensor/mapper to determine positional data for the small astronomy satellites was tested to detect stars of plus 4 visual magnitude. It utilizes two information channels with memory so that it can be used with a low-data-rate telemetry system. One channel yields star amplitude information; the other yields the time of star occurrence as the star passes across an N-slit reticle/photomultiplier detector system. Some of the features of the star sensor/mapper are its low weight of 6.5 pounds, low power consumption of 0.4 watt, bandwidth switching to match the satellite spin rate, optical equalization of sensitivity over the 5-by-10 deg field of view, and self-deployable sunshade. The attitude determination accuracy is 3 arc minutes. This is determined by such parameters as the reticle configuration, optical train, and telemetry readout. The optical and electronic design of the star sensor/mapper, its expansion capabilities, and its features are discussed.
Vector quantization is known to be an effective compression scheme to achieve a low bit rate so as to minimize communication channel bandwidth and also to reduce digital memory storage while maintaining the necessary fidelity of the data. However, the large number of computations required in vector quantizers has been a handicap in using vector quantization for low-rate source coding. An adaptive vector quantization algorithm is introduced that is inherently suitable for simple hardware implementation because it has a simple architecture. It allows fast encoding and decoding because it requires only addition and subtraction operations.
A technique of predicting an upper bound on the rate at which single-event upsets due to ionizing radiation occur in semiconducting memory cells is described. The upper bound on the upset rate, which depends on the high-energy particle environment in earth orbit and accelerator cross-section data, is given by the product of an upper-bound linear energy-transfer spectrum and the mean cross section of the memory cell. Plots of the spectrum are given for low-inclination and polar orbits. An alternative expression for the exact upset rate is also presented. Both methods rely only on experimentally obtained cross-section data and are valid for sensitive bit regions having arbitrary shape.
For NiTi based alloys, the shape memory effect is governed by a transition from a low-temperature martensite phase to a high-temperature austenite phase. Despite considerable experimental and computational work, basic questions regarding the stability of the phases and the martensitic phase transition remain unclear even for the simple case of binary, equiatomic NiTi. We perform ab initio molecular dynamics simulations to describe the temperature-dependent behavior of NiTi and resolve several of these outstanding issues. Structural correlation functions and finite temperature phonon spectra are evaluated to determine phase stability. In particular, we show that finite temperature, entropic effects stabilize the experimentally observed martensite (B19') and austenite (B2) phases while destabilizing the theoretically predicted (B33) phase. Free energy computations based on ab initio thermodynamic integration confirm these results and permit estimates of the transition temperature between the phases. In addition to the martensitic phase transition, we predict a new transition between the B33 and B19' phases. The role of defects in suppressing these phase transformations is discussed.
Aggressive design goals have been set for future aero-propulsion systems with regards to fuel economy, noise, and emissions. To meet these challenging goals, advanced propulsion concepts are being explored and current operating margins are being re-evaluated to find additional concessions that can be made. One advanced propulsion concept being evaluated is a geared turbofan with a variable area fan nozzle (VAFN), developed by NASA. This engine features a small core, a fan driven by the low pressure turbine through a reduction gearbox, and a shape memory alloy (SMA)-actuated VAFN. The VAFN is designed to allow both a small exit area for efficient operation at cruise, while being able to open wider at high power conditions to reduce backpressure on the fan and ensure a safe level of stall margin is maintained. The VAFN is actuated via a SMA-based system instead of a conventional system to decrease overall weight of the system, however, SMA-based actuators respond relatively slowly, which introduces dynamic issues that are investigated in this work. This paper describes both a control system designed specifically for issues associated with SMAs, and dynamic analysis of the geared turbofan VAFN with the SMA actuators. Also, some future recommendations are provided for this type of propulsion system.
Demand response from residential customers has significant potential to support power system operations, but accurate flexibility estimation is challenging due to the limited resolution of advanced metering infrastructure (AMI) data. Most utility AMI measurements are recorded at hourly intervals, with only a small portion at higher resolutions, and even fewer households have appliance-level energy usage data. To address this issue, this paper proposes a two-stage long short-term memory (LSTM) framework for estimating household flexibility envelopes from low-resolution AMI data. In the first stage, the heating, ventilating, and air-conditioning (HVAC) load and non-HVAC loads are estimated by using a model trained on a small set of households with appliance-level profiles. These estimated data are then used to compute the upper- and lower-flexibility bounds, which are subsequently down-sampled to lower-resolution data. In the second stage, these flexibility bounds serve as training inputs for another LSTM model, enabling direct prediction of flexibility envelopes for households with only hourly AMI data. This method is validated using Pecan Street data from two different areas, and the results demonstrate its applicability and effectiveness.