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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Development of a high capacity bubble domain memory element and related epitaxial garnet materials for application in spacecraft data recorders. Item 2: The optimization of material-device parameters for application in bubble domain memory elements for spacecraft data recorders

Bubble domain materials and devices are discussed. One of the materials development goals was a materials system suitable for operation of 16 micrometer period bubble domain devices at 150 kHz over the temperature range -10 C to +60 C. Several material compositions and hard bubble suppression techniques were characterized and the most promising candidates were evaluated in device structures. The technique of pulsed laser stroboscopic microscopy was used to characterize bubble dynamic properties and device performance at 150 kHz. Techniques for large area LPE film growth were developed as a separate task. Device studies included detector optimization, passive replicator design and test and on-chip bridge evaluation. As a technology demonstration an 8 chip memory cell was designed, tested and delivered. The memory elements used in the cell were 10 kilobit serial registers.

Besser, P. J.↗
NASA NTRS1984

Simulations for Full Unit-memory and Partial Unit-memory Convolutional Codes with Real-time Minimal-byte-error Probability Decoding Algorithm

A program which was written to simulate Real Time Minimal-Byte-Error Probability (RTMBEP) decoding of full unit-memory (FUM) convolutional codes on a 3-bit quantized AWGN channel is described. This program was used to compute the symbol-error probability of FUM codes and to determine the signal to noise (SNR) required to achieve a bit error rate (BER) of 10 to the minus 6th power for corresponding concatenated systems. A (6,6/30) FUM code, 6-bit Reed-Solomon code combination was found to achieve the required BER at a SNR of 1.886 dB. The RTMBEP algorithm was then modified for decoding partial unit-memory (PUM) convolutional codes. A simulation program was also written to simulate the symbol-error probability of these codes.

Vo, Q. D.↗
DOE OSTI2016

NANDFlashSim: High-Fidelity, Microarchitecture-Aware NAND Flash Memory Simulation [NANDFlashSim: Intrinsic Latency Variation Aware NAND Flash Memory System Modeling and Simulation at Microarchitecture Level]

As the popularity of NAND flash expands in arenas from embedded systems to high-performance computing, a high-fidelity understanding of its specific properties becomes increasingly important. Further, with the increasing trend toward multiple-die, multiple-plane architectures and high-speed interfaces, flash memory systems are expected to continue to scale and cheapen, resulting in their broader proliferation. However, when designing NAND-based devices, making decisions about the optimal system configuration is nontrivial, because flash is sensitive to a number of parameters and suffers from inherent latency variations, and no available tools suffice for studying these nuances. The parameters include the architectures, such as multidie and multiplane, diverse node technologies, bit densities, and cell reliabilities. Therefore, we introduce NANDFlashSim, a high-fidelity, latency-variation-aware, and highly configurable NAND-flash simulator, which implements a detailed timing model for 16 state-of-the-art NAND operations. Using NANDFlashSim, we notably discover the following. First, regardless of the operation, reads fail to leverage internal parallelism. Second, MLC provides lower I/O bus contention than SLC, but contention becomes a serious problem as the number of dies increases. Third, many-die architectures outperform many-plane architectures for disk-friendly workloads. Lastly, employing a high-performance I/O bus or an increased page size does not enhance energy savings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The microstructure of a selective laser melting (SLM)-fabricated NiTi shape memory alloy with superior tensile property and shape memory recoverability

A selective laser melting (SLM)-fabricated NiTi with superior tensile property and shape memory recoverability was obtained by using a unique stripe rotation scanning strategy. Here, the alloy characteristics, formation mechanisms and evolution in terms of twins, dislocations and precipitations of the alloy were systematically studied. Compared to the conventional smelting-followed-by-machining, the SLM fabrication process involves rapid solidification and repeated heating, which confer distinctive characteristics to the microstructures of SLM-fabricated NiTi alloys. Rapid solidification promotes the formation of a supersaturation solid solution matrix containing a high concentration vacancies, which in turn aggregate to generate a high density of dislocations. During subsequent repeated heating stages, these dislocations occur thermal motion along three directions of <001 > , <111> and <110 >, leading to the formation of thermal kinks, helical dislocations and wave morphology. Simultaneously, precipitated particles Ti 3 Ni 4 repeatedly nucleate and heterogeneous grow with the movement of dislocation. Such precipitation behavior, termed repeated precipitation, has not been previously reported in the conventional NiTi alloys, suggesting that it could be a unique characteristic of such alloy. After martensitic transformation, only two twins, {1 1¯ 1} type I twin and compound twin, are detected. The twinning lamellae of these two twins, where precipitation and dislocation pile-ups exist, often have uneven thickness and chaotic arrangement. Besides, the unique self-accommodated microstructures, such as secondary {1 1¯ 1} type I twin and compound twin with “herring-bone’’ lamellae, which often appear in the deformed or nanocrystalline NiTi, can also be observed. These unique microstructures may confer the distinctive properties to the SLM-fabricated NiTi.

36 MATERIALS SCIENCE↗
DOE OSTI2025

ScaWL: Scaling k-WL (Weisfeiler-Lehman) Algorithms in Memory and Performance on Shared and Distributed-Memory Systems

The k-dimensional Weisfeiler-Lehman (k-WL) algorithm—developed as an efficient heuristic for testing if two graphs are isomorphic—is a fundamental kernel for node embedding in the emerging field of graph neural networks. Unfortunately, the k-WL algorithm has exponential storage requirements, limiting the size of graphs that can be handled. This work presents a novel k-WL scheme with a storage requirement orders of magnitude lower while maintaining the same accuracy as the original k-WL algorithm. Due to the reduced storage requirement, our scheme allows for processing much bigger graphs than previously possible on a single compute node. For even bigger graphs, we provide the first distributed-memory implementation. Our k-WL scheme also has significantly reduced communication volume and offers high scalability. Our experimental results demonstrate that our approach is significantly faster and has superior scalability compared to five other implementations employing state-of-the-art techniques.

algorithims↗
NASA NTRS2022

Ruggedness Evaluation of ASTM International Standard Test Methods for Shape Memory Materials: E3097 Standard Test Method for Mechanical Uniaxial Constant Force Thermal Cycling of Shape Memory Alloys

This paper evaluates the ruggedness testing of the newly released ASTM International E3097 Standard Test Method for Mechanical Uniaxial Constant Force Thermal Cycling of Shape Memory Alloys. The ruggedness experiment was designed with eight runs in two replicates, consisting of seven factors of strain rate (ė), heating and cooling rates (Ṫ(heat) and Ṫ(cool), respectively), upper and lower cycle temperatures (UCT and LCT, respectively), hold time (thold), and minimum load (Fmin) imparted on the samples. The results indicate that the hold time factor had no effect on any result variable. The minimum load factor, alternatively, had the greatest effect on several result variables, with the greatest influence on the strains at martensite start and finish (strain variation ~0.1 percent), and the strains at the upper and lower cycle temperatures (strain variation of 0.14 percent). The UCT was found to have a large effect on the austenite and martensite finish tangent line and data intersect, denoted by Af* and Mf*, by ~17 and 4 °C, respectively. The testing methodology, analysis techniques, and resulting conclusions on the ruggedness of the test methods are presented.

shape memory alloy↗
NASA NTRS2023

Ruggedness Evaluation of ASTM International Standard Test Methods for Shape Memory Materials: E3098 Standard Test Method for Mechanical Uniaxial Pre-strain and Thermal Free Recovery of Shape Memory Alloys

This paper evaluates the ruggedness testing of the recently released ASTM International E3098 Standard Test Method for Mechanical Uniaxial Pre-strain and Thermal Free Recovery of Shape Memory Alloys. The ruggedness experiment was designed with eight runs in two replicates, consisting of seven factors: strain rate (ė), heating and cooling rates (Ṫ heat and Ṫ cool , respectively), upper and lower cycle temperatures (UCT and LCT, respectively), hold time (t hold ), and minimum load (F min ) imparted on the samples. The results indicate that the strain rate factor had no effect on any result variable, whereas the minimum load factor had a large effect on several result variables including the austenite start and martensite finish temperatures as well as most of the strain measurements from the thermal cycle. The lower cycle temperature factor had a large effect on the stress at maximum strain during pre-straining (e i ), denoted by S i , at ~7.2 MPa, as well as the A f , at 5.47 °C. Closely following the LCT effect, Ṫ heat was found to have a large effect on the austenite start temperature (A s ), at a value of ~2.72 °C. The testing methodology, analysis techniques, and resulting conclusions on the ruggedness of the test method are presented.

Shape memory alloys↗
DOE OSTI2019

Photo‐/Thermal‐Responsive Field‐Effect Transistor upon Blending Polymeric Semiconductor with Hexaarylbiimidazole toward Photonically Programmable and Thermally Erasable Memory Device

Abstract It is shown that the semiconducting performance of field‐effect transistors (FETs) with PDPP4T (poly(diketopyrrolopyrrole‐quaterthiophene)) can be reversibly tuned by UV light irradiation and thermal heating after blending with the photochromic hexaarylbiimidazole compound ( p ‐NO 2 ‐HABI). A photo‐/thermal‐responsive FET with a blend thin film of PDPP4T and p ‐NO 2 ‐HABI is successfully fabricated. The transfer characteristics are altered significantly with current enhanced up to 10 6 ‐fold at V G = 0 V after UV light irradiation. However, further heating results in the recovery of the transfer curve. This approach can be extended to other semiconducting polymers such as P3HT (poly(3‐hexyl thiophene)), PBTTT (poly(2,5‐bis(3‐tetradecylthiophen‐2‐yl)thieno[3,2‐b] thiophene)) and PDPPDTT (poly(diketopyrrolopyrrole‐dithienothiophene)). It is hypothesized that TPIRs (2,4,5‐triphenylimidazolyl radicals) formed from p ‐NO 2 ‐HABI after UV light irradiation can interact with charge defects at the gate dielectric–semiconductor interface and those in the semiconducting layer to induce more hole carriers in the semiconducting channel. The application of the blend thin film of PDPP4T and p ‐NO 2 ‐HABI is further demonstrated to fabricate the photonically programmable and thermally erasable FET‐based nonvolatile memory devices that are advantageous in terms of i) high ON/OFF current ratio, ii) nondestructive reading at low electrical bias, and iii) reasonably highly stable ON‐state and OFF‐state.

Liu, Yidong↗
DOE OSTI2020

Time-Sequenced Flow Field Prediction in an Optima Spark-Ignition Direct-Injection Engine Using Bidirectional Recurrent Neural Network (bi-RNN) with Long Short-Term Memory

To further improve the energy conversion efficiency of internal combustion engine, the transient and complex air flow movement inside the cylinder needs to be better understood and controlled. Although the in-cylinder flow fields are highly stochastic with strong cycle-to-cycle fluctuations, machine learning can still provide an efficient way to learn and regress the complex flow movement process inside the cylinder. In this work, a bidirectional recurrent neural network (bi-RNN) model with long short-term memory was applied to predict the in-cylinder flow fields at different time steps using training data from mull-cycle particle image velocimetry (PIV) measurements. To evaluate the agreement between the true and predicted flow fields, structure and magnitude comparison indices are calculated both globally and locally. The comparison results show that the bi-RNN model can accurately predict the bulk flow and vortex motions from early intake stroke to compression stroke. This work demonstrates that the machine learning model has the potential to predict the underlying dynamics of the interaction between in-cylinder flows and provides a reliable way to improve temporal resolution in PIV flow data to better reveal transient in-cylinder flow features.

Bi-RNN model↗
DOE OSTI2015

H-treatment impact on conductive-filament formation and stability in Ta{sub 2}O{sub 5}-based resistive-switching memory cells

In this article, we evidence the lower formation energy and improved stability of the conductive filament (CF) formed in TiN\Ta{sub 2}O{sub 5}\Ta resistive-switching memory cells treated in NH{sub 3} atmosphere at 400 °C. This annealing treatment results in (i) lower forming voltage, (ii) lower CF resistance, and (iii) longer retention lifetime of the oxygen-vacancy (V{sub o}) chain constituting the CF. Atomistic insights into these processes are provided by ab initio calculations performed for hydrogen (H) species incorporated in non-stoichiometric Ta{sub 2}O{sub 5} supercells: (i) V{sub o} formation energy is reduced by the presence of H, (ii) V{sub o}-chain CF conductivity is increased by V{sub o} + OH complex formation, and (iii) V{sub o}-chain retention is strengthened by the stable V{sub o} + OH complex. As a result, efficient CF formation and excellent state stability are obtained after 15 days at 250 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2018

BMS symmetry, soft particles and memory

Here, in this work, we revisit unitary irreducible representations of the Bondi–Metzner–Sachs (BMS) group discovered by McCarthy. Representations are labelled by an infinite number of supermomenta in addition to 4-momentum. Tensor products of these irreducible representations lead to particle-like states dressed by soft gravitational modes. Conservation of 4-momentum and supermomentum in the scattering of such states leads to a memory effect encoded in the outgoing soft modes. We note there exist irreducible representations corresponding to soft states with strictly vanishing 4-momentum, which may nevertheless be produced by scattering of particle-like states. This fact has interesting implications for the S-matrix in gravitational theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗