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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Kokkos Programming Model and Library

High performance computing is in the midst of a many-core revolution driven by rapidly evolving microchips with diverse and heterogeneous architectures, increasing concurrency (thread count), and decreasing memory per thread. Our HPC applications face a major challenge to effectively use these microchips with a single code base that is performance portable across these evolving architectures. Kokkos was developed to enable on-chip concurrency and manage architecture-dependent constraints on memory access patterns. The goal is to maximize the amount of application code that is portable across diverse architectures and still obtain the same (or nearly) performance as a variant of that code written specifically for that architecture. The Kokkos team has ongoing R&D co-design collaborations with Sandia's and vendors' microchip and system engineers to rapidly incorporate evolving computer architecture features into Kokkos. The team is also providing leadership for DOE laboratories' participation on the ISO/C++ language standard committee to subsuming Kokkos features into a future standard.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Precipitation Strengthenable NiTiPd High Temperature Shape Memory Alloys

In binary NiTi alloys, it has long been known that Ni-rich alloys can be heat treated to produce precipitates which both strengthen the matrix against dislocations and improve the behavior of the material under thermal and mechanical cycling. Within recent years, the same effect has been observed in Ni-rich NiTiHf high temperature shape memory alloys and heat treatment regimens have been defined which will reliably produce improved properties. In NiTiPd alloys, precipitation has also been observed, but studies are still underway to define reliable heat treatments and compositions which will provide a balance of strengthening and good thermomechanical properties. For this study, a series of NiTi-32 at.Pd alloys was produced to determine the effect of changing nickeltitanium content on the transformation behavior and heat treatability of the material. Samples were aged at temperatures between 350C and 450C for times up to 100 hours. Actuation type behavior was evaluated using uniaxial constant force thermal cycling (UCFTC) to determine the effect of composition and aging on the material behavior. TEMSEM was used to evaluate the microstructure and determine the types of precipitates formed. The correlation between composition, heat treat, microstructure, and thermomechanical behavior will be addressed and discussed.

Transformation strain↗
DOE OSTI2025

Securing Smart Manufacturing: Detection of Cyber-Physical Attacks in CNC-Based Systems

As Industry 4.0 advances, the integration of computer numerical control (CNC) machines and advanced manufacturing technologies is transforming production into smart manufacturing systems that blend physical and digital processes as cyber-physical systems. However, this increased cyber-physical connectivity exposes manufacturing systems to cyber threats that can cause severe operational and financial disruptions. This paper presents a comparative study on cyber attacks and anomaly detection techniques in manufacturing, focusing on network traffic from CNC machines. The data extracted from network packets includes machine commands and control signals exchanged between the machine's interface and control system, crucial for maintaining operational integrity. We explore two types of cyber attacks, design modification and command injection, which pose substantial risks to CNC machine productivity and system integrity. Our investigation involves experiments on a real CNC system, highlighting the urgent need for effective detection mechanisms. To address these threats, we evaluate three anomaly detection methods: dynamic time warping (DTW), rolling average, and a deep learning, long short-term memory (LSTM) time-series-based autoencoder. Each is assessed for its effectiveness in identifying anomalous behaviors caused by the attacks. Our findings demonstrate the unique strengths and limitations of each detection technique, providing a deeper understanding of their applicability in realworld manufacturing environments. The comparative analysis indicates that while certain methods are highly effective against specific attack types, others offer broader applicability across different attacks. This study contributes to the accurate detection of anomalies in CNC machining processes, thereby enhancing the reliability and security of smart manufacturing systems against diverse cyber threats.

Williams, Bethanie [Tennessee Technological Univer↗
DOE OSTI2014

Reticulation of low density shape memory polymer foam with an in vivo demonstration of vascular occlusion

Recently, predominantly closed-cell low density shape memory polymer (SMP) foam was reported to be an effective aneurysm filling device in a porcine model (Rodriguez et al., Journal of Biomedical Materials Research Part A 2013: (http://dx.doi.org/10.1002/jbm.a.34782)). Because healing involves blood clotting and cell migration throughout the foam volume, a more open-cell structure may further enhance the healing response. This research sought to develop a non-destructive reticulation process for this SMP foam to disrupt the membranes between pore cells. Non-destructive mechanical reticulation was achieved using a gravity-driven floating nitinol pin array coupled with vibratory agitation of the foam and supplemental chemical etching. Lastly, reticulation resulted in a reduced elastic modulus and increased permeability, but did not impede the shape memory behavior. Reticulated foams were capable of achieving rapid vascular occlusion in an in vivo porcine model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Hysteretic Superconducting Heat-Flux Quantum Modulator

In this work, we discuss heat transport in a thermally biased superconducting quantum-interference device (SQUID) in the presence of an external magnetic flux, when a non-negligible inductance of the SQUID ring is taken into account. A properly sweeping driving flux causes the thermal current to modulate and behave hysteretically. The response of this device is analyzed as a function of both the hysteresis parameter and the degree of asymmetry of the SQUID, highlighting the parameter range over which hysteretic behavior is observable. Markedly, the temperature of the SQUID also shows hysteretic evolution, with sharp transitions characterized by temperature jumps up to, e.g., approximately 0.02 K for a realistic Al-based setup. In view of these results, the proposed device can effectively find an application as a temperature-based superconducting memory element, working even at gigahertz frequencies by suitably choosing the superconductor on which the device is based.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Securing 3D NAND Without Density Loss via In-Situ Encryption Using a Single Transistor XOR Cell

In this article, we push lightweight XOR-based in-situ encryption to extreme density by proposing a singletransistor XOR memory cell and applying it to 3D NAND, enabling secure data storage without density loss. Using a ferroelectric field-effect transistor (FeFET) as an example technology, we demonstrate that: i) a single-transistor memory can realize the XOR function by exploiting the ability to charge the source and drain separately and control current flow direction, eliminating the need for conventional encrypted cells that rely on complementary devices; ii) with a XOR-based cipher, encryption and decryption can be mapped to in-situ array operations, where ciphertext is stored as the threshold voltage (VTH) states of FeFETs in a NAND string, and decryption is achieved through read operations using key-dependent complementary source/drain bias; iii) the proposed technique is scalable to multi-level cell (MLC) storage by encrypting and decrypting data bit by bit; iv) using an integrated NAND FeFET array, we experimentally demonstrate encryption and decryption operations for both single-level cell (SLC) and MLC storage; v) systemlevel benchmarking shows that the proposed technique achieves 48× and 278× improvements in encryption and decryption throughput, respectively, compared to AES.

36 MATERIALS SCIENCE↗
NASA NTRS2001

Compact Holographic Data Storage

NASA's future missions would require massive high-speed onboard data storage capability to Space Science missions. For Space Science, such as the Europa Lander mission, the onboard data storage requirements would be focused on maximizing the spacecraft's ability to survive fault conditions (i.e., no loss in stored science data when spacecraft enters the 'safe mode') and autonomously recover from them during NASA's long-life and deep space missions. This would require the development of non-volatile memory. In order to survive in the stringent environment during space exploration missions, onboard memory requirements would also include: (1) survive a high radiation environment (1 Mrad), (2) operate effectively and efficiently for a very long time (10 years), and (3) sustain at least a billion write cycles. Therefore, memory technologies requirements of NASA's Earth Science and Space Science missions are large capacity, non-volatility, high-transfer rate, high radiation resistance, high storage density, and high power efficiency. JPL, under current sponsorship from NASA Space Science and Earth Science Programs, is developing a high-density, nonvolatile and rad-hard Compact Holographic Data Storage (CHDS) system to enable large-capacity, high-speed, low power consumption, and read/write of data in a space environment. The entire read/write operation will be controlled with electrooptic mechanism without any moving parts. This CHDS will consist of laser diodes, photorefractive crystal, spatial light modulator, photodetector array, and I/O electronic interface. In operation, pages of information would be recorded and retrieved with random access and high-speed. The nonvolatile, rad-hard characteristics of the holographic memory will provide a revolutionary memory technology meeting the high radiation challenge facing the Europa Lander mission. Additional information is contained in the original extended abstract.

Chao, T. H.↗
DOE OSTI2017

Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures

Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load balancing, and efficient memory transfer between different devices. It is relatively easy to meet these requirements for highly structured scientific applications. In contrast, a number of scientific and engineering applications are unstructured. Getting performance on accelerators for these applications is extremely challenging because many of these applications employ irregular algorithms which exhibit data-dependent control-ow and irregular memory accesses. Furthermore, these applications are often iterative with dependency between steps, and thus making it hard to parallelize across steps. As a result, parallelism in these applications is often limited to a single step. Numerical simulation of charged particles beam dynamics is one such application where the distribution of work and memory access pattern at each time step is irregular. Applications with these properties tend to present significant branch and memory divergence, load imbalance between different processor cores, and poor compute and memory utilization. Prior research on parallelizing such irregular applications have been focused around optimizing the irregular, data-dependent memory accesses and control-ow during a single step of the application independent of the other steps, with the assumption that these patterns are completely unpredictable. We observed that the structure of computation leading to control-ow divergence and irregular memory accesses in one step is similar to that in the next step. It is possible to predict this structure in the current step by observing the computation structure of previous steps. In this dissertation, we present novel machine learning based optimization techniques to address the parallel implementation challenges of such irregular applications on different HPC architectures. In particular, we use supervised learning to predict the computation structure and use it to address the control-ow and memory access irregularities in the parallel implementation of such applications on GPUs, Xeon Phis, and heterogeneous architectures composed of multi-core CPUs with GPUs or Xeon Phis. We use numerical simulation of charged particles beam dynamics simulation as a motivating example throughout the dissertation to present our new approach, though they should be equally applicable to a wide range of irregular applications. The machine learning approach presented here use predictive analytics and forecasting techniques to adaptively model and track the irregular memory access pattern at each time step of the simulation to anticipate the future memory access pattern. Access pattern forecasts can then be used to formulate optimization decisions during application execution which improves the performance of the application at a future time step based on the observations from earlier time steps. In heterogeneous architectures, forecasts can also be used to improve the memory performance and resource utilization of all the processing units to deliver a good aggregate performance. We used these optimization techniques and anticipation strategy to design a cache-aware, memory efficient parallel algorithm to address the irregularities in the parallel implementation of charged particles beam dynamics simulation on different HPC architectures. Experimental result using a diverse mix of HPC architectures shows that our approach in using anticipation strategy is effective in maximizing data reuse, ensuring workload balance, minimizing branch and memory divergence, and in improving resource utilization.

Arumugam, Kamesh↗
NASA NTRS2004

Three-Dimensional High-Order Spectral Volume Method for Solving Maxwell's Equations on Unstructured Grids

A three-dimensional, high-order, conservative, and efficient discontinuous spectral volume (SV) method for the solutions of Maxwell's equations on unstructured grids is presented. The concept of discontinuous 2nd high-order loca1 representations to achieve conservation and high accuracy is utilized in a manner similar to the Discontinuous Galerkin (DG) method, but instead of using a Galerkin finite-element formulation, the SV method is based on a finite-volume approach to attain a simpler formulation. Conventional unstructured finite-volume methods require data reconstruction based on the least-squares formulation using neighboring cell data. Since each unknown employs a different stencil, one must repeat the least-squares inversion for every cell at each time step, or to store the inversion coefficients. In a high-order, three-dimensional computation, the former would involve impractically large CPU time, while for the latter the memory requirement becomes prohibitive. In the SV method, one starts with a relatively coarse grid of triangles or tetrahedra, called spectral volumes (SVs), and partition each SV into a number of structured subcells, called control volumes (CVs), that support a polynomial expansion of a desired degree of precision. The unknowns are cell averages over CVs. If all the SVs are partitioned in a geometrically similar manner, the reconstruction becomes universal as a weighted sum of unknowns, and only a few universal coefficients need to be stored for the surface integrals over CV faces. Since the solution is discontinuous across the SV boundaries, a Riemann solver is thus necessary to maintain conservation. In the paper, multi-parameter and symmetric SV partitions, up to quartic for triangle and cubic for tetrahedron, are first presented. The corresponding weight coefficients for CV face integrals in terms of CV cell averages for each partition are analytically determined. These discretization formulas are then applied to the integral form of the Maxwell equations. All numerical procedures for outer boundary, material interface, zonal interface, and interior SV face are unified with a single characteristic formulation. The load balancing in a massive parallel computing environment is therefore easier to achieve. A parameter is introduced in the Riemann solver to control the strength of the smoothing term. Important aspects of the data structure and its effects to communication and the optimum use of cache memory are discussed. Results will be presented for plane TE and TM waves incident on a perfectly conducting cylinder for up to fifth order of accuracy, and a plane wave incident on a perfectly conducting sphere for up to fourth order of accuracy. Comparisons are made with exact solutions for these cases.

Liu, Yen↗
NASA NTRS1996

Constitutive Models for Shape Memory Alloy Polycrystals

Shape memory alloys (SMA) exhibiting the superelastic or one-way effects can produce large recoverable strains upon application of a stress. In single crystals this stress and resulting strain are very orientation dependent. We show experimental stress/strain curves for a Ni-Al single crystal for various loading orientations. Also shown are model predictions; the open and closed circles indicate recoverable strains obtained at various stages in the transformation process. Because of the strong orientation dependence of shape memory properties, crystallographic texture can be expected to play an important role in the mechanical behavior of polycrystalline SMA. It is desirable to formulate a constitutive model to better understand and exploit the unique properties of SMA.

Comstock, R. J., Jr.↗
DOE OSTI2016

Strategic priming with multiple antigens can yield memory cell phenotypes optimized for infection with Mycobacterium tuberculosis : A computational study

Lack of an effective vaccine results in 9 million new cases of tuberculosis (TB) every year and 1.8 million deaths worldwide. While many infants are vaccinated at birth with BCG (an attenuated M. bovis ), this does not prevent infection or development of TB after childhood. Immune responses necessary for prevention of infection or disease are still unknown, making development of effective vaccines against TB challenging. Several new vaccines are ready for human clinical trials, but these trials are difficult and expensive; especially challenging is determining the appropriate cellular response necessary for protection. The magnitude of an immune response is likely key to generating a successful vaccine. Characteristics such as numbers of central memory (CM) and effector memory (EM) T cells responsive to a diverse set of epitopes are also correlated with protection. Promising vaccines against TB contain mycobacterial subunit antigens (Ag) present during both active and latent infection. We hypothesize that protection against different key immunodominant antigens could require a vaccine that produces different levels of EM and CM for each Ag-specific memory population. We created a computational model to explore EM and CM values, and their ratio, within what we term Memory Design Space. Our model captures events involved in T cell priming within lymph nodes and tracks their circulation through blood to peripheral tissues. We used the model to test whether multiple Ag-specific memory cell populations could be generated with distinct locations within Memory Design Space at a specific time point post vaccination. Boosting can further shift memory populations to memory cell ratios unreachable by initial priming events. By strategically varying antigen load, properties of cellular interactions within the LN, and delivery parameters (e.g., number of boosts) of multi-subunit vaccines, we can generate multiple Ag-specific memory populations that cover a wide range of Memory Design Space. As a result, given a set of desired characteristics for Ag-specific memory populations, we can use our model as a tool to predict vaccine formulations that will generate those populations.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1925

The Effects of Shielding the Tips of Airfoils

Tests have recently been made at Langley Memorial Aeronautical Laboratory to ascertain whether the aerodynamic characteristics of an airfoil might be substantially improved by imposing certain limitations upon the air flow about its tips. All of the modified forms were slightly inferior to the plain airfoil at small lift coefficients: however, by mounting thin plates, in planes perpendicular to the span, at the wing tips, the characteristics were improved throughout the range above three-tenths of the maximum lift coefficient. With this form of limitation the detrimental effect was slight; at the higher lift coefficients there resulted a considerable reduction of induced drag and consequently, of power required for sustentation. The slope of the curve of lift versus angle of attack was increased.

Reid, Elliott G↗
NASA NTRS2017

Evaluation of the Radiation Susceptibility of a 3D NAND Flash Memory

We evaluated the heavy ion and proton-induced single-event effects (SEE) for a 3D NAND flash. The 3D NAND showed similar single-event upset (SEU) sensitivity to a planar NAND of similar density and performance in the multiple-cell level (MLC) storage mode. However, the single-level-cell (SLC) storage mode of the 3D NAND showed significantly reduced SEU susceptibility. Additionally, the 3D NAND showed less MBU susceptibility than the planar NAND, with reduced number of upset bits per byte and reduced cross sections overall. However, the 3D architecture exhibited angular sensitivities for both base and face angles, reflecting the anisotropic nature of the SEU vulnerability in space. Furthermore, the SEU cross section decreased with increasing fluence for both the 3D NAND and the latest generation planar NAND, indicating a variable upset rate for a space mission. These unique characteristics introduce complexity to traditional ground irradiation test procedures.

single-event upset (SEU)↗
NASA NTRS2017

The Effects of Fiber Orientation and Adhesives on Tensile Properties of Carbon Fiber Reinforced Polymer Matrix Composite with Embedded Nickel-Titanium Shape Memory Alloys

Nickel-titanium (NiTi) shape memory alloy (SMA) sections were embedded within carbon fiber reinforced polymer matrix composite (CFRPPMC) laminates and their tensile properties were evaluated with simultaneous monitoring of modal acoustic emissions. The test specimens were fabricated in three different layup configurations and two different thin film adhesives were applied to bond the SMA with the PMC. A trio of acoustic sensors were attached to the specimens during tensile testing to monitor the modal acoustic emission (AE) as the materials experienced mechanical failure. The values of ultimate tensile strengths, strains, and moduli were obtained. Cumulative AE energy of events and specimen failure location were determined. In conjunction, optical and scanning electron microscopy techniques were used to examine the break areas of the specimens. The analysis of AE data revealed failure locations within the specimens which were validated from the microscopic images. The placement of 90 deg plies in the outer ply gave the strongest acoustic signals during break as well as the cleanest break of the samples tested. Overlapping 0 deg ply layers surrounding the SMA was found to be the best scenario to prevent failure of the specimen itself.

shape memory alloy↗
NASA NTRS2018

The Effects of Fiber Orientation and Adhesives on Tensile Properties of Carbon Fiber Reinforced Polymer Matrix Composite with Embedded Nickel-Titanium Shape Memory Alloys

Tensile tests of Nickel-titanium (NiTi) shape memory alloys (SMA) embedded within carbon fiber reinforced polymer matrix composite (CFRP/PMC) laminates were evaluated with simultaneous monitoring of modal acoustic emissions (MAE). Three different layup configurations utilizing two different thin film adhesives were applied to bond the materials. Ultimate tensile strengths, strains, and moduli were obtained along with cumulative AE energy of events and specimen failure location. Scanning electron microscopy was used to examine the break areas of the specimens post-test. Microscopy was used to validate failure locations revealed from MAE analysis. A unique finding within this research showed that 90° plies in the outer ply gave the strongest acoustic signals as well as the cleanest fracture of the specimens tested. Overlapping 0° ply layers surrounding the SMA was found to be the best scenario to prevent failure of the specimen itself.

Derek Quade↗
DOE OSTI2025

Harnessing ferro-valleytricity in pentalayer rhombohedral graphene for memory and compute

Two-dimensional materials with multiple degrees of freedom, including spin, valleys, and orbitals, open up an exciting avenue for engineering multifunctional devices. Beyond spintronics, these degrees of freedom can lead to novel quantum effects such as valley-dependent Hall effects and orbital magnetism, which could revolutionize next-generation electronics. However, achieving independent control over valley polarization and orbital magnetism has been a challenge due to the need for large electric fields. A recent breakthrough involving pentalayer rhombohedral graphene has demonstrated the ability to individually manipulate anomalous Hall signals and orbital magnetic hysteresis, forming what is known as a valley-magnetic quartet. Here, we leverage the electrically tunable ferro-valleytricity of pentalayer rhombohedral graphene to develop nonvolatile memory and in-memory computation applications. We propose an architecture for a dense, scalable, and selector-less nonvolatile memory array that harnesses the electrically tunable ferro-valleytricity. In our designed array architecture, nondestructive read and write operations are conducted by sensing the valley state through two different pairs of terminals, allowing for independent optimization of read/write peripheral circuits. The power consumption of our PRG-based array is remarkably low, with only ∼6 nW required per write operation and ∼2.3 nW per read operation per cell. This consumption is orders of magnitude lower than that of the majority of state-of-the-art cryogenic memories. Additionally, we engineer in-memory computation by implementing majority logic operations within our proposed nonvolatile memory array without modifying the peripheral circuitry. In conclusion, our framework presents a promising pathway toward achieving ultra-dense cryogenic memory and in-memory computation capabilities.

2D materials↗
DOE OSTI2019

Nanoscale magnetic Josephson junctions and superconductor/ferromagnet proximity effects for low-power spintronics

Competition between superconducting and ferromagnetic ordering at interfaces between ferromagnets (F) and superconductors (S) gives rise to several proximity effects such as odd-triplet superconductivity and spin-polarized supercurrents. A prominent example of an S/F proximity effect is the spin switch effect observed in S/F/N/F superconducting spin valve multilayers, in which the superconducting transition temperature T c is controlled by the angle Φ between the magnetic moments of the F layers separated by a nonmagnetic metallic spacer N. We carried out an experimental study of spin switch effect in Nb/Co/Cu/Co/CoO x nanowires measured as a function of bias current flowing in the plane of the layers. These measurements reveal an unexpected dependence of T c (Φ) on the bias current: T c (Φ) – T c (0) changes sign with increasing current bias. We attribute the origin of this bias dependence of the spin switch effect to a spin Hall current originating in Nb and flowing perpendicular to the plane of the multilayer, which suppresses T c of the multilayer. The bias dependence of spin switch effect can be important for hybrid F/S devices such as cryogenic memory for superconducting computers when device dimensions are scaled down to the nanometer length scale.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

AHF: Array-Based Half-Facet Data Structure for Mixed-Dimensional and Non-Manifold Meshes

We present an Array-based Half-Facet mesh data structure, or AHF, for efficient mesh query and modification operations. The AHF extends the compact array-based half-edge and half-face data structures (T.J. Alumbaugh and X. Jiao, Compact array-based mesh data structures, IMR, 2005) to support mixed-dimensional and non-manifold meshes. The design goals of our data structure include generality to support such meshes, efficiency of neighborhood queries and mesh modification, compactness of memory footprint, and facilitation of interoperability of mesh-based application codes. To accomplish these goals, our data structure uses sibling half-facets as a core abstraction, coupled with other explicit and implicit representations of entities. A unique feature of our data structure is a comprehensive implementation in MATLAB, which allows rapid prototyping, debugging, testing, and deployment of meshing algorithms and other mesh-based numerical methods. We have also developed C++ implementation built on top of MOAB (T.J. Tautges, R. Meyers, and K. Merkley, MOAB: A Mesh-Oriented Database, Sandia National Laboratories, 2004). We present some comparisons of the memory requirements and computational costs, and also demonstrate its effectiveness with a few sample applications.

Dyedov, Volodymyr↗