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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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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Lightweight shape memory self-deployable structures for Gossamer applications

Currently, existing approaches for producing large, ultra-lightweight, deployable structures in space typically rely upon electro-mechanical mechanisms and mechanically expandable that are heavy, not stowage volume efficient, expensive and complex. Therefore, one of the major efforts at NASA and DoD has been to develop expandable structures characterized by low mass and small launch volume. As a result, space inflatable structures have emerged 9-10 years ago.

ultra-lightweight↗
NASA NTRS1976

A microcomputer-based low-cost Omega navigation system

The application of a low cost, commercially available microcomputer as the navigation processor for a simplified OMEGA navigation system is an area of current research. The interface of a low cost front end OMEGA sensor is described and an example of the phase processing software and navigation routines is given. Emphasis is placed on the description of results obtained with the software version of the OMEGA burst filter known as the memory aided phase locked loop.

Lilley, R. W.↗
NASA NTRS1978

Suggested architecture for a specialized fluid dynamics computer

Future flow simulations in 3-D will require computers with extremely large main memories and an advantageous ratio between computer cost and arithmetic speed. Since random access memories are very expensive, a pipeline design is proposed which allows the use of much cheaper sequential devices without any sacrifice in speed for vector references (even with arbitrary spacing between successive elements). Also scalar arithmetic can be performed efficiently. The comparatively low speed of the proposed machine (about 10 to the 7th power operations per second) would be offset by a very low price per unit, making mass production possible.

Fornberg, B.↗
NASA NTRS2002

Compositionally-Graded Shape Memory Film for Self-Deployment of Membrane Reflectors and Optics

The next generation of space systems will require large apertures in order to image faint targets or cover large areas of Earth. These large apertures must be able to fit inside a launch vehicle fairing, be light enough for launch into orbit, and deploy on orbit with repeatability and reliability. The current state-of-the-art in flight optics is represented by the 4 meter LAMP telescope, with an areal density of 10 km sq m. Development of a Beryllium mirror demonstration article for NGST (Next Generation Space Telescope) at the University of Arizona indicate areal densities of 0.5 kg sq m with flight hardware in the 12 meter range. With progressive improvements in existing deployment, packaging, and structural technologies, the size of optics and reflectors will continue to increase, while mass is reduced. However, without a breakthrough in materials, packaging and/or deployment technologies, the goal for Gossamer structures of 0.1 kg sq m is unachievable for the near and mid-term NASA missions. Membrane technology provides the best hope of achieving such low areal densities. In combination with advances in membrane materials and structures, development of revolutionary techniques for deployment systems can provide significant improvements in large aperture technology. In this paper, the results of a six-month Phase I research effort to demonstrate the application of thin film NiTi to aerospace-qualified membrane and mesh materials are presented. Deposition of shape memory thin film was achieved Astromesh (trademark) metal mesh and CP-1, and optical-quality polymer membrane. Not only was full-coating deposition demonstrated, but also small segment deposition which holds potential for local surface control. Deployment of these materials was also demonstrated, setting the stage for the development of a larger test article.

Hill, Lisa↗
DOE OSTI2024

Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications

With more scientific fields relying on neural networks (NNs) to process data incoming at extreme throughputs and latencies, it is crucial to develop NNs with all their parameters stored on-chip. In many of these applications, there is not enough time to go off-chip and retrieve weights. Even more so, off-chip memory such as DRAM does not have the bandwidth required to process these NNs as fast as the data is being produced (e.g., every 25 ns). As such, these extreme latency and bandwidth requirements have architectural implications for the hardware intended to run these NNs: 1) all NN parameters must fit on-chip, and 2) codesigning custom/reconfigurable logic is often required to meet these latency and bandwidth constraints. In our work, we show that many scientific NN applications must run fully on chip, in the extreme case requiring a custom chip to meet such stringent constraints.

Weng, Olivia↗
DOE OSTI2016

Multi-step resistive switching behavior of Li-doped ZnO resistance random access memory device controlled by compliance current

The multi-step resistive switching (RS) behavior of a unipolar Pt/Li{sub 0.06}Zn{sub 0.94}O/Pt resistive random access memory (RRAM) device is investigated. It is found that the RRAM device exhibits normal, 2-, 3-, and 4-step RESET behaviors under different compliance currents. The transport mechanism within the device is investigated by means of current-voltage curves, in-situ transmission electron microscopy, and electrochemical impedance spectroscopy. It is shown that the ion transport mechanism is dominated by Ohmic behavior under low electric fields and the Poole-Frenkel emission effect (normal RS behavior) or Li{sup +} ion diffusion (2-, 3-, and 4-step RESET behaviors) under high electric fields.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Trajectory prediction dimensionality reduction for low-cost connected automated vehicle systems

Here, to facilitate low-cost connected automated vehicle (CAV) system development, this study proposes two interpretable dimensionality reduction techniques in vehicle trajectory prediction, i.e., the piecewise Taylor series approximation (PTA) and the piecewise Fourier series approximation (PFA), to lower computation complexity, reduce device investment, and decrease computation energy consumption. Two benchmarks are developed, the long short-term memory (LSTM)-based model without dimensionality reduction and the LSTM-based model with encoder-decoder (a widely used dimensionality reduction technique). Results show that the four predictions have similar accuracy, and the training time (proportional to computation energy consumption) of models with dimensionality reduction techniques is greatly reduced. The reduction is even more significant when PTA/PFA is used. Sensitivity analysis advises PFA/PTA parameter selections to reduce computation complexity without significant loss of prediction accuracy. Further, the robustness of the LSTM PTA/PFA is proven by the investigation of data noises.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders

The anomalies in the high voltage converter modulator (HVCM) remain a major down time for the spallation neutron source facility, that delivers the most intense neutron beam in the world for scientific materials research. In this work, we propose neural network architectures based on Recurrent AutoEncoders (RAE) to detect anomalies ahead of time in the power signals coming from the HVCM. Bi-directional gated recurrent unit, bi-directional long-short term memory (LSTM), and convolutional LSTM (ConvLSTM) are developed, trained, and tested using real experimental signals from the HVCM module. The results show a good performance of the proposed RAE models, achieving precision up to 91%, recall up to 88%, false omission rate as low as 20% (i.e. 80% of the anomalies were detected), and area under the ROC curve up to 0.9. The three RAE models provide very comparable performance, with LSTM showing slightly better performance than GRU and ConvLSTM. The RAE models are benchmarked against other anomaly detection methods, including isolation forest, support vector machine, local outlier factor, feedforward and convolutional autoencoders, and others; showing a better performance. Here, the results of this study demonstrate the promising potential of RAE in anomaly detection for real-world power systems, and for increasing the reliability of the HVCM modules in the spallation neutron source.

42 ENGINEERING↗
DOE OSTI2023

Infrared Signatures for Phase Identification in Hafnium Oxide Thin Films

Phase identification in HfO 2 -based thin films is a prerequisite to understanding the mechanisms stabilizing the ferroelectric phase in these materials, which hold great promise in next-generation non-volatile memory and computing technology. While grazing-incidence X-ray diffraction is commonly employed for this purpose, it has difficulty unambiguously differentiating between the ferroelectric phase and other metastable phases that may exist due to similarities in the d-spacings, their low intensities, and the overlapping of reflections. Infrared signatures provide an alternative route. However, their use in phase identification remains limited because phase control has overwhelmingly been accomplished via substituents, thereby convoluting infrared signatures between the substituent and the phase changes they induce. Herein, we report the infrared optical responses of three undoped hafnium oxide films where annealing conditions have been used to create films consisting primarily of the ferroelectric polar orthorhombic Pca2 1 , antipolar orthorhombic Pbca, and monoclinic P2 1 /c phases, as was confirmed via transmission electron microscopy (TEM), UVvisible optical properties, and electrical property measurements. Vibrational signatures acquired from synchrotron nano-Fourier transform infrared spectroscopy (nano-FTIR) are shown to be capable of differentiating between the phases in a non-destructive, rapid, and nanoscale manner. The utility of nano-FTIR is illustrated for a film exhibiting an antiferroelectric polarization response. In this sample, it is proven that this behavior results from the Pbca phase rather than the often-cited tetragonal phase. Here, by demonstrating that IRspectroscopy can unambiguously distinguish phases in this material, this work establishes a tool needed to isolate the factors dictating ferroelectric phase stability in HfO 2 -based materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Effect of recording condition on the diffraction efficiency of magnetic hologram with magnetic garnet films

A holographic memory has been attracting attention as recording media with high recording density and high data transfer rate. We have studied the magnetic garnets as a rewritable and long life media for magnetic holography. However, since the signal intensity of reconstructed image was relatively low, the effects of recording conditions on the diffraction efficiency of magnetic hologram were investigated with experiments and the numerical simulation using COMSOL multi-physics. The diffraction efficiency tends to decrease as increasing the spatial frequency, and the use of short pulse laser with the pulse width of 50 ps was found to be effective to achieve high diffraction efficiency. This suggests that the formation of clear magnetic fringe similar to interference pattern can be obtained by the use of short pulse laser since undesirable heat diffusion during radiation does not occur. On the other hand, the diffraction efficiency increased as increasing the film thickness up to 3.1 μm but was saturated in the garnet film thicker than 3.1 μm in the case of spatial frequency of 1500 line pair/mm. The numerical simulation showed that the effective depth of magnetic fringe was limited about 1.8 μm irrespective of the garnet film thickness because the fringes were connected by thermal diffusion near the surface of the film, and the effective depth is limited due to this connection of the magnetic fringe. Avoiding this fringe connection, much higher diffraction efficiency will be achieved.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Beta (β) tungsten thin films: Structure, electron transport, and giant spin Hall effect

We use a simple magnetron sputtering process to fabricate beta (β) tungsten thin films, which are capable of generating giant spin Hall effect. As-deposited thin films are always in the metastable β-W phase from 3.0 to 26.7 nm. The β-W phase remains intact below a critical thickness of 22.1 nm even after magnetic thermal annealing at 280 °C, which is required to induce perpendicular magnetic anisotropy (PMA) in a layered structure of β-W/Co{sub 40}Fe{sub 40}B{sub 20}/MgO. Intensive annealing transforms the thicker films (>22.1 nm) into the stable α-W phase. We analyze the structure and grain size of both β- and α-W thin films. Electron transport in terms of resistivity and normal Hall effect is studied over a broad temperature range of 10 K to at least 300 K on all samples. Very low switching current densities are achieved in β-W/Co{sub 40}Fe{sub 40}B{sub 20}/MgO with PMA. These basic properties reveal useful behaviors in β-W thin films, making them technologically promising for spintronic magnetic random access memories and spin-logic devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Demonstration of solar-blind Al{sub x}Ga{sub 1−x}N-based heterojunction phototransistors

Al{sub 0.4}Ga{sub 0.6}N/Al{sub 0.65}Ga{sub 0.35}N heterojunction phototransistors have been fabricated from the epi-structure grown by low-pressure metal organic chemical vapor deposition on c-plane sapphire substrates. P-type conductivity of the AlGaN base layer was realized by using indium surfactant-assisted Mg-delta doping method. Regrowth technique was used to suppress the Mg memory effect on the n-type emitter. The fabricated devices with a 150-μm-diameter active area exhibited a bandpass spectral response between 235 and 285 nm. Dark current was measured to be less than 10 pA for bias voltages below 2.0 V. A high optical gain of 1.9 × 10{sup 3} was obtained at 6 V bias.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml

Abstract Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implementation of two types of recurrent neural network layers—long short-term memory and gated recurrent unit—within the hls4ml framework. We demonstrate that our implementation is capable of producing effective designs for both small and large models, and can be customized to meet specific design requirements for inference latencies and FPGA resources. We show the performance and synthesized designs for multiple neural networks, many of which are trained specifically for jet identification tasks at the CERN Large Hadron Collider.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs

Despite foreseeing tremendous speedups over conventional deep neural networks, the performance advantage of binarized neural networks (BNNs) has merely been showcased on general-purpose processors such as CPUs and GPUs. In fact, due to being unable to leverage bit-level-parallelism with a word-based architecture, GPUs have been criticized for extremely low utilization (1%) when executing BNNs. Consequently, the latest tensorcores in NVIDIA Turing GPUs start to experimentally support bit computation. In this work, we look into this brand new bit computation capability and characterize its unique features. We show that the stride of memory access can significantly affect performance delivery and a data-format co-design is highly desired to support the tensorcores for achieving superior performance than existing software solutions without tensorcores. We realize the tensorcore-accelerated BNN design, particularly the major functions for fully-connect and convolution layers — bit matrix multiplication and bit convolution. Evaluations on two NVIDIA Turing GPUs show that, with ResNet-18, our BTC-BNN design can process ImageNet at a rate of 5.6K images per second, 77% faster than state-of-the-art. Our BNN approach is released on https://github.com/pnnl/TCBNN.

Li, Ang↗
DOE OSTI2024

High energy density picoliter-scale zinc-air microbatteries for colloidal robotics

The recent interest in microscopic autonomous systems, including microrobots, colloidal state machines, and smart dust, has created a need for microscale energy storage and harvesting. However, macroscopic materials for energy storage have noted incompatibilities with microfabrication techniques, creating substantial challenges to realizing microscale energy systems. Here, we photolithographically patterned a microscale zinc/platinum/SU-8 system to generate the highest energy density microbattery at the picoliter (10 −12 liter) scale. The device scavenges ambient or solution-dissolved oxygen for a zinc oxidation reaction, achieving an energy density ranging from 760 to 1070 watt-hours per liter at scales below 100 micrometers lateral and 2 micrometers thickness in size. The parallel nature of photolithography processes allows 10,000 devices per wafer to be released into solution as colloids with energy stored on board. Within a volume of only 2 picoliters each, these primary microbatteries can deliver open circuit voltages of 1.05 ± 0.12 volts, with total energies ranging from 5.5 ± 0.3 to 7.7 ± 1.0 microjoules and a maximum power near 2.7 nanowatts. We demonstrated that such systems can reliably power a micrometer-sized memristor circuit, providing access to nonvolatile memory. We also cycled power to drive the reversible bending of microscale bimorph actuators at 0.05 hertz for mechanical functions of colloidal robots. Additional capabilities, such as powering two distinct nanosensor types and a clock circuit, were also demonstrated. The high energy density, low volume, and simple configuration promise the mass fabrication and adoption of such picoliter zinc-air batteries for micrometer-scale, colloidal robotics with autonomous functions.

Robotics↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1993

Image coding using entropy-constrained residual vector quantization

The residual vector quantization (RVQ) structure is exploited to produce a variable length codeword RVQ. Necessary conditions for the optimality of this RVQ are presented, and a new entropy-constrained RVQ (ECRVQ) design algorithm is shown to be very effective in designing RVQ codebooks over a wide range of bit rates and vector sizes. The new EC-RVQ has several important advantages. It can outperform entropy-constrained VQ (ECVQ) in terms of peak signal-to-noise ratio (PSNR), memory, and computation requirements. It can also be used to design high rate codebooks and codebooks with relatively large vector sizes. Experimental results indicate that when the new EC-RVQ is applied to image coding, very high quality is achieved at relatively low bit rates.

Kossentini, Faouzi↗
NASA NTRS1993

A transient FETI methodology for large-scale parallel implicit computations in structural mechanics, part 2

Explicit codes are often used to simulate the nonlinear dynamics of large-scale structural systems, even for low frequency response, because the storage and CPU requirements entailed by the repeated factorizations traditionally found in implicit codes rapidly overwhelm the available computing resources. With the advent of parallel processing, this trend is accelerating because explicit schemes are also easier to parallellize than implicit ones. However, the time step restriction imposed by the Courant stability condition on all explicit schemes cannot yet and perhaps will never be offset by the speed of parallel hardware. Therefore, it is essential to develop efficient and robust alternatives to direct methods that are also amenable to massively parallel processing because implicit codes using unconditionally stable time-integration algorithms are computationally more efficient than explicit codes when simulating low-frequency dynamics. Here we present a domain decomposition method for implicit schemes that requires significantly less storage than factorization algorithms, that is several times faster than other popular direct and iterative methods, that can be easily implemented on both shared and local memory parallel processors, and that is both computationally and communication-wise efficient. The proposed transient domain decomposition method is an extension of the method of Finite Element Tearing and Interconnecting (FETI) developed by Farhat and Roux for the solution of static problems. Serial and parallel performance results on the CRAY Y-MP/8 and the iPSC-860/128 systems are reported and analyzed for realistic structural dynamics problems. These results establish the superiority of the FETI method over both the serial/parallel conjugate gradient algorithm with diagonal scaling and the serial/parallel direct method, and contrast the computational power of the iPSC-860/128 parallel processor with that of the CRAY Y-MP/8 system.

Farhat, Charbel↗