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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Valve Health Monitoring System Utilizing Smart Instrumentation

The valve monitoring system is a stand alone unit with network capabilities for integration into a higher level health management system. The system is designed for aiding in failure predictions of high-geared ball valves and linearly actuated valves. It performs data tracking and archiving for identifying degraded performance. The data collection types are cryogenic cycles, total cycles, inlet temperature, body temperature torsional strain, linear bonnet strain, preload position, total travel and total directional changes. Events are recorded and time stamped in accordance with the IRIG B True Time. The monitoring system is designed for use in a Class 1 Division II explosive environment. The basic configuration consists of several instrumentation sensor units and a base station. The sensor units are self contained microprocessor controlled and remotely mountable in three by three by two inches. Each unit is potted in a fire retardant substance without any cavities and limited to low operating power for maintaining safe operation in a hydrogen environment. The units are temperature monitored to safeguard against operation outside temperature limitations. Each contains 902-928 MHz band digital transmitters which meet Federal Communication Commission's requirements and are limited to a 35 foot transmission radius for preserving data security. The base-station controller correlates data from the sensor units and generates data event logs on a compact flash memory module for database uploading. The entries are also broadcast over an Ethernet network. Nitrogen purged National Electrical Manufactures Association (NEMA) Class 4 enclosures are used to house the base-station

Jensen, Scott L.↗
NASA NTRS2011

Overview of ERA Ultra High Bypass Propulsor Technology Development

A review of the current research being conducted under the Environmentally Responsible Aviation (ERA) Ultra High Bypass (UHB) Testing subelement is presented. The four exiting tasks under the subelement, a description of each task, and the current status of each are given. The four tasks are: 1. Collaborate with P&W to design, fabricate and test a second generation of Geared Turbofan 2. Design, fabricate and test advanced Over the Rotor acoustic treatment and acoustically treated Soft Vanes 3. Develop a Shape Memory Alloy Variable Area Nozzle concept and demonstrate prototype 4. Refurbish and update the GRC Ultra High Bypass Drive Rig Following the current task updates, an overview of three proposed additional tasks to support the existing tasks is presented. The additional tasks would allow noise reduction and noise diagnostic testing technologies to be demonstrated at TRL 4 as part of existing planned fan model testing in the NASA Glenn 9 x15 Low Speed Wind Tunnel under the ERA UHB Testing subelement.

Hughes, Christopher↗
NASA NTRS2006

Space Exploration: A Risk for Neural Stem Cells

During spaceflights beyond low Earth orbit, astronauts are exposed to potentially carcinogenic and tissue damaging galactic cosmic rays, solar proton events, and secondary radiation that includes neutrons and recoil nuclei produced by nuclear reactions in spacecraft walls or in tissue (1). Such radiation risk may present a significant health risk for human exploration of the moon and Mars. Emerging evidence that generation of new neurons in the adult brain may be essential for learning, memory, and mood (2) and that radiation is deleterious to neurogenesis (3-5) underscores a previously unappreciated possible risk to the cognitive functions and emotional stability of astronauts exposed to radiation in space. Here we use a novel reporter mouse line to identify at-risk populations of stem and progenitor cells in the brain and find, unexpectedly, that quiescent stem-like cells (rather than their rapidly dividing progeny) in the hippocampus constitute the most vulnerable cell population. This finding raises concerns about the possible risks facing astronauts on long duration space missions.

Encinas, Juan M.↗
NASA NTRSDate not supplied

A Machine Learning Approach to Design Shape Memory Alloys for NASA Applications

Shape memory alloys (SMAs) are a unique class of materials with several remarkable properties including shape recovery, superelasticity, etc. Nickel-titanium (NiTi) based alloys are the most widely studied of this class, with compositions including ternary, quaternary, or higher additions being considered. Especially important for many NASA applications is the ability to tune the martensitic phase transition temperature of NiTi alloys by varying the alloy composition and processing conditions. In addition, low hysteresis and an acceptable recoverable transformation strain are required. Over the past several years, a significant database of SMA properties has been assembled by NASA researchers. Such a database is ideal for data science-based approaches. We present results from our machine learning approach for designing new SMAs with target properties within our range of interest. Our developed models are capable of accurately predicting the transition temperature, hysteresis, and transformation strain of SMAs across a wide range of compositions. This approach has the potential to significantly accelerate the discovery and design of new SMA materials.

Shreyas Jaikumar Honrao↗
DOE OSTI2011

Higher Molecular Weight Leads to Improved Photoresponsivity Charge Transport and Interfacial Ordering in a Narrow Bandgap Semiconducting Polymer

Increasing the molecular weight of the low-bandgap semiconducting copolymer, poly[(4,4-didoecyldithieno[3,2-b:2',3'-d]silole)-2,6-diyl-alt-(2,1,3-benzothiadiazole)-4,7-diyl], Si-PDTBT, from 9 kDa to 38 kDa improves both photoresponsivity and charge transport properties dramatically. The photocurrent measured under steady state conditions is 20 times larger in the higher molecular weight polymer (HM{sub n} Si-PDTBT). Different decays of polarization memory in transient photoinduced spectroscopy measurements are consistent with more mobile photoexcitations in HM{sub n} Si-PDTBT relative to the lower molecular weight counterpart (LM{sub n} Si-PDTBT). Analysis of the current-voltage characteristics of field effect transistors reveals an increase in the mobility by a factor of 700 for HM{sub n} Si-PDTBT. Near edge X-ray absorption fine structure (NEXAFS) spectroscopy and grazing incidence small angle X-ray scattering (GISAXS) measurements demonstrate that LM{sub n} Si-PDTBT forms a disordered morphology throughout the depth of the film, whereas HM{sub n} Si-PDTBT exhibits pronounced {pi}-{pi} stacking in an edge-on configuration near the substrate interface. Increased interchain overlap between polymers in the edge-on configuration in HM{sub n} Si-PDTBT results in the higher carrier mobility. The improved optical response, transport mobility, and interfacial ordering highlight the subtle role that the degree of polymerization plays on the optoelectronic properties of conjugated polymer based organic semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Global Data Re-allocation via Communication Aggregation in Chapel

Chapel is a parallel programming language designed to improve the productivity and ease of use of conventional and parallel computers. This language currently delivers sub optimal performance when executing codes that perform global data re-allocation operations on distributed memory architectures. This is mainly due to data communication that is done without aggregation (one message for each remote array element). In this work, we analyze Chapel's standard Block and Cyclic distribution modules and optimize the communication routines for array assignments by performing aggregation. Thanks to the expressive power of Chapel, the compiler and runtime have enough information to do communication aggregation without user intervention. The runtime relies on the low-level GAS Net networking layer, whose versions of one-sided bulk put/get routines that support strides are particularly useful for us. Experimental results conducted on Hector (a Cray XE6) and Jaguar (Cray XK6)reveal that the implemented techniques can lead to significant reductions in communication time.

Sanz, Alberto↗
DOE OSTI2022

Space-Split Algorithm for Sensitivity Analysis of Discrete Chaotic Systems With Multidimensional Unstable Manifolds

Accurate approximations of the change of a system's output and its statistics with respect to the input are highly desired in computational dynamics. Ruelle's linear response theory provides breakthrough mathematical machinery for computing the linear response of chaotic dynamical systems. In this paper, we propose an algorithm for sensitivity analysis of discrete chaos with an arbitrary number of positive Lyapunov exponents. We combine the concept of perturbation space-splitting, which regularizes Ruelle's original expression, together with measure-based parameterization of the expanding subspace. We use these tools to rigorously derive trajectory-following recursive relations that converge exponentially fast, and construct a memory-efficient Monte Carlo scheme for derivatives of the output statistics. Thanks to the regularization and lack of simplifying assumptions on the system's behavior, our method is immune to the common problems of other popular methods such as the exploding tangent solutions and unphysical shadowing directions. Here, we provide a ready-to-use algorithm, analyze its complexity, and demonstrate several numerical examples of sensitivity computation using physically-inspired low-dimensional systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the QVAE has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the MODIS dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

Data mining, similarity search, quantum machine le↗
NASA NTRS1973

Investigation of chemical vapor deposition of garnet films for bubble domain memories

The important process parameters and control required to grow reproducible device quality ferrimagnetic films by chemical vapor deposition (CVD) were studied. The investigation of the critical parameters in the CVD growth process led to the conclusion that the required reproducibility of film properties cannot be achieved with individually controlled separate metal halide sources. Therefore, the CVD growth effort was directed toward replacement of the halide sources with metallic sources with the ultimate goal being the reproducible growth of complex garnet compositions utilizing a single metal alloy source. The characterization of the YGdGaIG films showed that certain characteristics of this material, primarily the low domain wall energy and the large temperature sensitivity, severely limited its potential as a useful material for bubble domain devices. Consequently, at the time of the change from halide to metallic sources, the target film compositions were shifted to more useful materials such as YGdTmGaIG, YEuGaIG and YSmGaIG.

Besser, P. J.↗
DOE OSTI2023

An organic electrochemical transistor for multi-modal sensing, memory and processing

By integrating sensing, memory and processing functionalities, biological nervous systems are energy and area efficient. Emulating such capabilities in artificial systems is, however, challenging and is limited by the device heterogeneity of sensing and processing cores. Here we report an organic electrochemical transistor capable of sensing, memory and processing. The device has a vertical traverse architecture and a crystalline–amorphous channel that can be selectively doped by ions to enable two reconfigurable modes: a volatile receptor and a non-volatile synapse. As a volatile receptor, the device is capable of multi-modal sensing and is responsive to stimuli such as ions and light. As a non-volatile synapse, it is capable of 10-bit analogue states, low switching stochasticity and good state retention. We also show that the homogeneous integration of the devices could provide functions such as conditioned reflexes and could be used for real-time cardiac disease diagnoses via reservoir computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Graphene nanopores as negative differential resistance devices

We present graphene nanopores as new negative differential resistance (NDR) devices, and study their quantum transport properties using non-equilibrium Green's function and the density functional tight binding method. The proposed device structure is created on intrinsic armchair-edged graphene nanoribbons with uniform widths, where the central scattering region has a nanopore in the interior, and the two ends of the nanoribbon act naturally as connecting electrodes. We show that nitrogen-passivated scattering regions generally result in pronounced NDR properties, while hydrogen-passivated ones do not. This NDR effect occurs at low bias voltages, below 1 V, and achieves extraordinarily high peak-to-valley current ratio, while still attaining very high peak current densities. In addition, very sharp current peaks in the μA range can occur in the I-V curves, and through varying structural dimensions of the proposed structure multiple NDR regions can be realized. These results suggest that the device has promising potential in applications such as high frequency oscillators, memory devices, and fast switches.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Linear-scaling time-dependent density-functional theory beyond the Tamm-Dancoff approximation: Obtaining efficiency and accuracy with in situ optimised local orbitals

We present a solution of the full time-dependent density-functional theory (TDDFT) eigenvalue equation in the linear response formalism exhibiting a linear-scaling computational complexity with system size, without relying on the simplifying Tamm-Dancoff approximation (TDA). The implementation relies on representing the occupied and unoccupied subspaces with two different sets of in situ optimised localised functions, yielding a very compact and efficient representation of the transition density matrix of the excitation with the accuracy associated with a systematic basis set. The TDDFT eigenvalue equation is solved using a preconditioned conjugate gradient algorithm that is very memory-efficient. The algorithm is validated on a small test molecule and a good agreement with results obtained from standard quantum chemistry packages is found, with the preconditioner yielding a significant improvement in convergence rates. The method developed in this work is then used to reproduce experimental results of the absorption spectrum of bacteriochlorophyll in an organic solvent, where it is demonstrated that the TDA fails to reproduce the main features of the low energy spectrum, while the full TDDFT equation yields results in good qualitative agreement with experimental data. Furthermore, the need for explicitly including parts of the solvent into the TDDFT calculations is highlighted, making the treatment of large system sizes necessary that are well within reach of the capabilities of the algorithm introduced here. Finally, the linear-scaling properties of the algorithm are demonstrated by computing the lowest excitation energy of bacteriochlorophyll in solution. The largest systems considered in this work are of the same order of magnitude as a variety of widely studied pigment-protein complexes, opening up the possibility of studying their properties without having to resort to any semiclassical approximations to parts of the protein environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Ultra-low switching energy and scaling in electric-field-controlled nanoscale magnetic tunnel junctions with high resistance-area product

We report electric-field-induced switching with write energies down to 6 fJ/bit for switching times of 0.5 ns, in nanoscale perpendicular magnetic tunnel junctions (MTJs) with high resistance-area product and diameters down to 50 nm. The ultra-low switching energy is made possible by a thick MgO barrier that ensures negligible spin-transfer torque contributions, along with a reduction of the Ohmic dissipation. We find that the switching voltage and time are insensitive to the junction diameter for high-resistance MTJs, a result accounted for by a macrospin model of purely voltage-induced switching. The measured performance enables integration with same-size CMOS transistors in compact memory and logic integrated circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

THE FORMATION OF YOUNG DENSE STAR CLUSTERS THROUGH MERGERS

Young star clusters such as NGC 3603 and Westerlund 1 and 2 in the Milky Way and R136 in the Large Magellanic Cloud are dynamically more evolved than expected based on their current relaxation times. In particular, the combination of a high degree of mass segregation, a relatively low central density, and the large number of massive runaway stars in their vicinity are hard to explain with the monolithic formation of these clusters. Young star clusters can achieve such a mature dynamical state if they formed through the mergers of a number of less massive clusters. The shorter relaxation times of less massive clusters cause them to dynamically evolve further by the time they merge, and the merger product preserves the memory of the dynamical evolution of its constituent clusters. With a series of N-body simulations, we study the dynamical evolution of single massive clusters and those that are assembled through merging smaller clusters together. We find that the formation of massive star clusters through the mergers of smaller clusters can reproduce the currently observed spatial distribution of massive stars, the density, and the characteristics (number and mass distribution) of the stars ejected as runaways from young dense clusters. We therefore conclude that these clusters and possibly other young massive star clusters formed through the mergers of smaller clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

A Method to Identify Seawater Pycnocline Boundaries Based on LSTM Neural Network and Its Application in Underwater Glider Missions

Underwater glider is a kind of buoyancy driven underwater vehicle; it has the features of low operating noise, long endurance and flexible maneuverability. During the glider long-time navigation, there is a high probability to encounter the pycnocline, which has a great influence on underwater glider’s navigation as well as application. In this work, an operation flow of glider hovering movement in the pycnocline was discussed in detail in order to improve the observation performance of glider. We firstly developed an LSTM (Long Short-Term Memory) neural network to identify the pycnocline boundaries. Then the accurate buoyancy adjustment of glider hovering movement was calculated based on the obtained pycnocline information. Finally a hardware in the loop simulation was implemented to verify the feasibility of the method, and the simulation results showed a reasonable performance of glider hovering in the pycnocline.

Song, Dalei↗
DOE OSTI2020

Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design

In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classication is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to signicantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low effciency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive syn- chronizations, which are very dicult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named PIM-CapsNet. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's ineffciency by leveraging the processing-in-memory capability of today's 3D stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference effciency through minimizing data movement and maximizing parallel processing in memory. Evaluation results demonstrate that our proposed design can achieve substantial improvement on both performance and energy savings for CapsNet inference, with almost zero accuracy loss. The results also suggest good performance scalability in optimizing the routing procedure with increasing network size.

Zhang, Xingyao↗
DOE OSTI2021

Low-Cost Aero Technology Demonstrations

The main focus of this work was to demonstrate the use of polymeric additive manufacturing (AM) to create tooling for both preforming and consolidation. Polymeric tooling was utilized where both modest and higher pressures are used for part consolidation. The key focus for the AM tooling development was for fabrication of complex structures such as ducting, C-channel stiffened skins, and airfoils where conventional male tooling would typically be trapped in the cured part. The AM tooling was evaluated for use as a tool master used to fabricate and re-shape deformable/re-formable mandrels based on SpinTech’s shape memory composite technology known as Smart Tooling. The AM tooling was also evaluated for use as a mold for composite infusion and consolidation. Key performance parameters were tracked for project schedule completion with each step comprising of “art to part” cycle time, cost, and model fidelity for dimensions, performance, and cost. The primary focus of this demonstration was to determine if a 50% cost reduction was achievable, for each AM tooling-set, as compared to conventional processes. UDRI leveraged project partner SpinTech, who manufactures tools and parts in these categories and thus provided a baseline regarding current best practices and provided valuable feedback during the entirety of this demonstration. This demonstration primarily focused on the use of AM tooling for fabrication of three composite component structures which are typically utilized in aircraft and comprise salient geometric features of broad interest. These components are often tooling intensive and have features requiring extraction of male tools which are usually trapped by the geometry. The three structures selected by the team included: 1) A one-piece airfoil shell comprised of compound contours where male tooling would be trapped unless the part were manufactured in two halves as is typically the case. 2) A one-piece duct used for air handling, comprised of compound contours where male tooling would be trapped unless the part were manufactured in two halves, or a washout mandrel were to be used. 3) A co-cured C-channel stiffened skin where typically C-channels would be individually manufactured and then bonded to a cured skin. The demonstration was comprised of three main tasks: • Task 1: AM Tool Feasibility Study – ensure the AM tooling meets the performance requirements as specified by SpinTech to match baseline performance. • Task 2: Complex Tool Demonstration – Fabricate tooling, preforms, and parts representative of an airfoil and duct. • Task 3: Large Aerostructure Fabrication Demonstration – Fabricate tooling, preforms, and part representative of a C-channel stiffened skin. With the conclusion of this project, a decision tree was developed to determine the key considerations necessary to determine if use of AM tooling for the three selected structures was able to attain the same quality as historically achieved on metallic tooling, while providing a significant cost reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Carbon doped GaN buffer layer using propane for high electron mobility transistor applications: Growth and device results

The creation of a semi insulating (SI) buffer layer in AlGaN/GaN High Electron Mobility Transistor (HEMT) devices is crucial for preventing a current path beneath the two-dimensional electron gas (2DEG). In this investigation, we evaluate the use of a gaseous carbon gas precursor, propane, for creating a SI GaN buffer layer in a HEMT structure. The carbon doped profile, using propane gas, is a two stepped profile with a high carbon doping (1.5 × 10{sup 18 }cm{sup −3}) epitaxial layer closest to the substrate and a lower doped layer (3 × 10{sup 16 }cm{sup −3}) closest to the 2DEG channel. Secondary Ion Mass Spectrometry measurement shows a uniform incorporation versus depth, and no memory effect from carbon doping can be seen. The high carbon doping (1.5 × 10{sup 18 }cm{sup −3}) does not influence the surface morphology, and a roughness root-mean-square value of 0.43 nm is obtained from Atomic Force Microscopy. High resolution X-ray diffraction measurements show very sharp peaks and no structural degradation can be seen related to the heavy carbon doped layer. HEMTs are fabricated and show an extremely low drain induced barrier lowering value of 0.1 mV/V, demonstrating an excellent buffer isolation. The carbon doped GaN buffer layer using propane gas is compared to samples using carbon from the trimethylgallium molecule, showing equally low leakage currents, demonstrating the capability of growing highly resistive buffer layers using a gaseous carbon source.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗