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Results for “Application Abstraction”
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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Enabling new applications on Mars through reconfigurable coprocessing
No abstract provided
Algorithms and Applications of the SCEPTRE Radiation Transport Code.
Abstract not provided.
Algorithms and Applications of the ITS Radiation Transport Code.
Abstract not provided.
Integrated System and Application Continuous Performance Monitoring and Analysis Capability.
Abstract not provided.
Efficient Sampling Methods for Machine Learning Error Models with Application to Surrogates of Steady Hypersonic Flows.
Abstract not provided.
Filament Formation in TaO x Thin Films for Memristor Device Application: Modeling Electron Energy Loss Spectra and Electron Transport
Abstract Although understanding filament formation in oxide‐based memristive devices by theory has emerged, there are still fundamental unanswered questions. Importantly, for practical application of thin films the material in its amorphous state is to be considered, but mostly lacking so far, and details on sub‐stoichiometry are also scarce. To gain insight into the optical and electronic properties of sub‐stoichiometric amorphous tantalum oxide (TaO x ), the electron energy loss spectrum (EELS) of model systems is characterized theoretically and electron transport characteristics are analyzed in detail. Calculated blue‐shifts by increasing sub‐stoichiometry explained the measurements, potentially suggesting estimation of oxygen vacancy concentrations through EEL spectra. Electron transport results based on TaO x material models validated by EELS measurements show that oxygen vacancy filamentary paths are initiated at low bias upon increasing sub‐stoichiometry yet noting an interplay with the local amorphous structure. Contact resistances at interfaces of the TaO x switching layer and a tantalum scavenging layer or titanium nitride electrode are quantified, indicating the possibility for either oxygen vacancy‐ or metal cluster‐based conduction mechanisms at the interface. The computational work, combined with experimental characterization for validation, provides a basis for investigating effects of sub‐stoichiometry on filament formation in TaO x thin film memristive devices.
NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 15)
Abstracts are cited for 240 patents and applications for patents introduced into the NASA scientific system during the period of January 1979 through June 1979. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or application for patent.
Vacillation Made Easy: Distribution, Re-distribution, and Un-distribution of DOPL-based Processing
Distributed Objects Protocol Layer, or DOPL, Provides a simple and general data communication abstraction that can support the distribution of C++ applications software functionality among an arbitrary collection of processors. The purposed of the abstraction is to minimize the cost of revising processing distribution decisions throughout the software development cycle, including after software has beed delivered to users.
Binary operations on neuromorphic hardware with application to linear algebraic operations and stochastic equations
Abstract Non-von Neumann computational hardware, based on neuron-inspired, non-linear elements connected via linear, weighted synapses—so-called neuromorphic systems—is a viable computational substrate. Since neuromorphic systems have been shown to use less power than CPUs for many applications, they are of potential use in autonomous systems such as robots, drones, and satellites, for which power resources are at a premium. The power used by neuromorphic systems is approximately proportional to the number of spiking events produced by neurons on-chip. However, typical information encoding on these chips is in the form of firing rates that unarily encode information. That is, the number of spikes generated by a neuron is meant to be proportional to an encoded value used in a computation or algorithm. Unary encoding is less efficient (produces more spikes) than binary encoding. For this reason, here we present neuromorphic computational mechanisms for implementing binary two’s complement operations. We use the mechanisms to construct a neuromorphic, binary matrix multiplication algorithm that may be used as a primitive for linear differential equation integration, deep networks, and other standard calculations. We also construct a random walk circuit and apply it in Brownian motion simulations. We study how both algorithms scale in circuit size and iteration time.
Machine learning enabled discovery of application dependent design principles for two-dimensional materials
Abstract The unique electronic and mechanical properties of two-dimensional (2D) materials make them promising next-generation candidates for a variety of applications. Large-scale searches for high-performing 2D materials are limited to calculating descriptors with computationally demanding first-principles density functional theory. In this work, we alleviate this issue by extending and generalizing crystal graph convolutional neural networks to systems with planar periodicity and train an ensemble of models to predict thermodynamic, mechanical and electronic properties. We carry out a screening of nearly 45,000 structures for two separate applications: mechanical strength and photovoltaics. By collecting statistics of the screened candidates, we investigate structural and compositional design principles that impact the properties of the structures surveyed. Our approach recovers some well-accepted design rules: hybrid organic-inorganic perovskites with lead and tin tend to be good candidates for solar cell applications and titanium based MXenes usually have high stiffness coefficients. Interestingly, other members of the group 4 elements also contribute to increasing the mechanical strength of MXenes. For all-inorganic perovskites, we discover some compositions that have not been deeply studied in the field of photovoltaics and thus open up paths for further investigation. We open-source the code-base to spur further development in this space.
Induced Ferromagnetism in Epitaxial Uranium Dioxide Thin Films
Abstract Actinide materials have various applications that range from nuclear energy to quantum computing. Most current efforts have focused on bulk actinide materials. Tuning functional properties by using strain engineering in epitaxial thin films is largely lacking. Using uranium dioxide (UO 2 ) as a model system, in this work, the authors explore strain engineering in actinide epitaxial thin films and investigate the origin of induced ferromagnetism in an antiferromagnet UO 2 . It is found that UO 2+ x thin films are hypostoichiometric ( x <0) with in‐plane tensile strain, while they are hyperstoichiometric ( x >0) with in‐plane compressive strain. Different from strain engineering in non‐actinide oxide thin films, the epitaxial strain in UO 2 is accommodated by point defects such as vacancies and interstitials due to the low formation energy. Both epitaxial strain and strain relaxation induced point defects such as oxygen/uranium vacancies and oxygen/uranium interstitials can distort magnetic structure and result in magnetic moments. This work reveals the correlation among strain, point defects and ferromagnetism in strain engineered UO 2+ x thin films and the results offer new opportunities to understand the influence of coupled order parameters on the emergent properties of many other actinide thin films.
Molecular‐Level Insight into the Chlorofluorocarbons Adsorption by Defective Covalent Organic Polymers
Abstract Halocarbons have important industrial applications, but because of their contribution to global warming and the fact that they can cause ozone depletion, they are considered highly toxic. Hence, the techniques that can capture and recover the used halocarbons with energy‐efficient methods have been recently received greater attention. In this contribution, we report the capture of dichlorodifluoromethane (R12), which has high global warming and ozone depletion potential, using covalent organic polymers (COPs). The defect‐engineered COPs were synthesized and demonstrated outstanding sorption capacities, ~226 wt % of R12 combined with linear‐shaped adsorption isotherms. We further identified the plausible microscopic adsorption mechanism of the investigated COPs via grand canonical Monte Carlo simulations applied to non‐defective and a collection of atomistic models of the defective COPs. The modeling work suggests that significant R12 adsorption performance is attributed to a gradual increment of porosities due to isolated/interconnected micro‐/meso‐pore channels and the change of the long‐range ordering of both COPs. The successive hierarchical‐pore‐filling mechanism promotes R12 molecular adsorption via moderate van der Waals adsorbate‐adsorbent interactions in the micropores of both COPs at low pressure followed by adsorbate‐adsorbate interactions in the extra‐voids created at moderate to high pressure ranges. This continuous pore‐filling mechanism makes defective COPs as promising sorbents for halocarbon adsorption.
Smectic C Self‐Assembly in Mesogen‐Free Liquid Crystalline Chiral Polyethers with Sulfonylated Methyl‐Branched Side Chains
Abstract To realize advanced electrical applications for ferroelectric liquid crystalline polymers, high spontaneous polarization ( P s ) is highly desired. However, current ferroelectric liquid crystalline polymers usually exhibit a low P s . In this work, mesogen‐free, chiral polyethers containing sulfonylated methyl‐branched alkyl side chains with a (CH 2 ) 3 O spacer between the sulfonyl and the branched alkyl groups are designed and synthesized. In contrast to the linear n ‐alkyl side chains, the methyl‐branched alkyl side chains induce chain tilting in the smectic layers. When double chirality exists in both the main chain and the side chains, a crystalline structure is observed after mechanical stretching. Intriguingly, when single chirality exists in either the backbone or the side chains, a liquid crystalline smectic C phase is obtained. The electric displacement–electric field study, however, does not show typical ferroelectric switching, although the dielectric constants are relatively high for these liquid crystalline polymers. This is likely because the dipole–dipole interactions among neighboring sulfonyl groups along the main chain are so strong that the ferroelectric switching is hindered in the samples. For the future work, it is desired to weaken the dipole–dipole interaction to achieve ferroelectricity in these mesogen‐free liquid crystalline polymers.
Interfacial and Bulk Assembly of Anisotropic Gold Nanostructures: Implications for Photonics and Plasmonics
Abstract not provided
The Climatic Impact‐Driver Framework for Assessment of Risk‐Relevant Climate Information
Abstract The climate science and applications communities need a broad and demand‐driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the “climatic impact‐driver” (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as “physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions.” We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co‐developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities.
High-throughput terahertz imaging: progress and challenges
Abstract Many exciting terahertz imaging applications, such as non-destructive evaluation, biomedical diagnosis, and security screening, have been historically limited in practical usage due to the raster-scanning requirement of imaging systems, which impose very low imaging speeds. However, recent advancements in terahertz imaging systems have greatly increased the imaging throughput and brought the promising potential of terahertz radiation from research laboratories closer to real-world applications. Here, we review the development of terahertz imaging technologies from both hardware and computational imaging perspectives. We introduce and compare different types of hardware enabling frequency-domain and time-domain imaging using various thermal, photon, and field image sensor arrays. We discuss how different imaging hardware and computational imaging algorithms provide opportunities for capturing time-of-flight, spectroscopic, phase, and intensity image data at high throughputs. Furthermore, the new prospects and challenges for the development of future high-throughput terahertz imaging systems are briefly introduced.
Solving differential‐algebraic equations in power system dynamic analysis with quantum computing
Abstract Power system dynamics are generally modeled by high dimensional non‐linear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system non‐linearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.