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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Parallel Calculation of Sensitivity Derivatives for Aircraft Design using Automatic Differentiation

Sensitivity derivative (SD) calculation via automatic differentiation (AD) typical of that required for the aerodynamic design of a transport-type aircraft is considered. Two ways of computing SD via code generated by the ADIFOR automatic differentiation tool are compared for efficiency and applicability to problems involving large numbers of design variables. A vector implementation on a Cray Y-MP computer is compared with a coarse-grained parallel implementation on an IBM SP1 computer, employing a Fortran M wrapper. The SD are computed for a swept transport wing in turbulent, transonic flow; the number of geometric design variables varies from 1 to 60 with coupling between a wing grid generation program and a state-of-the-art, 3-D computational fluid dynamics program, both augmented for derivative computation via AD. For a small number of design variables, the Cray Y-MP implementation is much faster. As the number of design variables grows, however, the IBM SP1 becomes an attractive alternative in terms of compute speed, job turnaround time, and total memory available for solutions with large numbers of design variables. The coarse-grained parallel implementation also can be moved easily to a network of workstations.

Bischof, c. H.↗
DOE OSTI2023

Synergistic Coupling Effect of Electronic Conductivity and Interphase Compatibility on High-Voltage Na 3 V 2 (PO 4 ) 2 F 3 Cathodes

Na 3 V 2 (PO 4 ) 2 F 3 (NVPF) has been considered an up-and-coming cathode material candidate for sodium (Na) ion batteries in light of its high specific capacity and working voltage. However, an erratic cathode/electrolyte interface layer is inevitably formed, accompanied by continuous electrolyte decomposition on the NVPF surface, when the voltage exceeds 4.2 V vs Na + /Na. Herein, the interphase features of NVPF are obviously enhanced owing to the ameliorated electronic conductivity obtained by combining it with carbon nanotubes (CNT). The NVPF with 3 wt % CNT (NVPF@3% CNT) reduces the Na + diffusion kinetic energy barrier and electron transport resistance. Furthermore, the conducting network formed by CNT with sturdy structure strength can promptly accommodate the volumetric changes during sequential Na + extraction/insertion and thus effectively improve the long-term cyclic performance of NVPF/hard carbon full cells. The initial discharge capacity approaches 105 mA h g –1 at 0.5C, and it retains 94% capacity retention after 200 cycles at the temperature of –10 °C. The cathode/electrolyte interphase characterization results further demonstrate that the interphase layer on the NVPF@3% CNT cathode is thinner and more compact compared with pristine samples. Here, this research provides a competitive strategy to facilitate the interfacial compatibility between the NVPF and electrolytes and accelerate the commercialization of high-performance Na-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗
DOE OSTI2024

Integrated photonic structures for photon-mediated entanglement of trapped ions

Trapped atomic ions are natural candidates for quantum information processing and have the potential to realize or improve quantum computing, sensing, and networking. These applications often require the collection of individual photons emitted from ions into guided optical modes, in some cases for the production of entanglement between separated ions. Proof-of-principle demonstrations of such photon collection from trapped ions have been performed using high-numerical-aperture lenses or cavities and single-mode fibers, but integrated photonic elements in ion-trap structures offer advantages in scalability and manufacturability over traditional optics. In this paper we analyze structures monolithically fabricated with an ion trap for collecting ion-emitted photons, coupling them into waveguides, and manipulating them via interference. We calculate geometric limitations on collection efficiency for this scheme, simulate a single-layer grating that shows performance comparable to demonstrated free-space optics, and discuss practical fabrication and fidelity considerations. Based on this analysis, we conclude that integrated photonics can support scalable systems of trapped ions that can distribute quantum information via photon-mediated entanglement.

Knollmann, Felix W.↗
DOE OSTI2024

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2021

Design principles of tandem cascade photoelectrochemical devices

Cascade photoelectrocatalysis (PEC) is a possible method to improve the selectivity of solar-driven CO 2 reduction (CO 2 R). This concept can be realized by coupling different CO 2 R catalysts to different subcells in a multijunction photovoltaic (PV) stack. Efficient implementation will require careful tuning of the photocurrents and design of the photovoltages provided by the subcells to the CO 2 R catalysts in such a way as to facilitate the target reaction. Here, we outline the design principles of the tandem PEC approach using two-step conversion of CO 2 to ethylene in aqueous electrolyte, via a CO intermediate, as a model system. To perform this reaction, the first coupled PV-catalyst component should provide 4 electrons to reduce 2 molecules of CO 2 to CO; the second component should provide 8 electrons to reduce 2 CO molecules to C 2 H 4 . Based on known CO 2 R catalysts, the overpotential required to produce CO can be less than that required to reduce it to ethylene, creating the opportunity for improved efficiency. Cascade PEC can be realized in a three-terminal tandem (3TT) configuration using III–V-semiconductor based subcells coupled to Au (produces CO intermediate) and Cu (converts CO to ethylene). The current to each catalyst can be controlled by the area of the subcell exposed to the electrolyte, and the photovoltage is determined by the materials selected and device configuration. Operating conditions are found by simulating the coupled system using the open-source circuit simulator SPICE (Simulation Program with Integrated Circuits Emphasis). We identify conditions under which a 3TT configuration can have a higher solar to chemical conversion efficiency compared to a two-terminal two-junction tandem (2T 2J) with the same absorbers and a Cu catalyst only. We also show that 3TT PEC devices can be less sensitive to variations in catalyst activity compared to 2T devices. Lastly, we discuss the applications of cascade PEC to CO 2 reduction, using different intermediates, and to other chemical networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1985

High-power diode lasers for optical communications applications

High-power, single-mode, double-heterojunction AlGaAs diode lasers are being developed to meet source requirements for both fiber optic local area network and free space communications systems. An individual device, based on the channeled-substrate-planar (CSP) structure, has yielded single spatial and longitudinal mode outputs of up to 90 mW CW, and has maintained a single spatial mode to 150 mW CW. Phase-locked arrays of closely spaced index-guided lasers have been designed and fabricated with the aim of multiplying the outputs of the individual devices to even higher power levels in a stable, single-lobe, anastigmatic beam. The optical modes of the lasers in such arrays can couple together in such a way that they appear to be emanating from a single source, and can therefore be efficiently coupled into optical communications systems. This paper will review the state of high-power laser technology and discuss the communication system implications of these devices.

Carlin, D. B.↗
NASA NTRS2008

Spaceborne Processor Array

A Spaceborne Processor Array in Multifunctional Structure (SPAMS) can lower the total mass of the electronic and structural overhead of spacecraft, resulting in reduced launch costs, while increasing the science return through dynamic onboard computing. SPAMS integrates the multifunctional structure (MFS) and the Gilgamesh Memory, Intelligence, and Network Device (MIND) multi-core in-memory computer architecture into a single-system super-architecture. This transforms every inch of a spacecraft into a sharable, interconnected, smart computing element to increase computing performance while simultaneously reducing mass. The MIND in-memory architecture provides a foundation for high-performance, low-power, and fault-tolerant computing. The MIND chip has an internal structure that includes memory, processing, and communication functionality. The Gilgamesh is a scalable system comprising multiple MIND chips interconnected to operate as a single, tightly coupled, parallel computer. The array of MIND components shares a global, virtual name space for program variables and tasks that are allocated at run time to the distributed physical memory and processing resources. Individual processor- memory nodes can be activated or powered down at run time to provide active power management and to configure around faults. A SPAMS system is comprised of a distributed Gilgamesh array built into MFS, interfaces into instrument and communication subsystems, a mass storage interface, and a radiation-hardened flight computer.

Chow, Edward T.↗
DOE OSTI2022

Reduced Order Models Generation for HTGRs Pebble Shuffling Procedure Optimization Studies

This report provides an initial study for producing reduced-order models (ROMs) of pebble-bed high temperature gas reactor (HTGR) models for the purposes of design optimization. As an initial study, this work is meant to be exploratory---identifying useful workflows and methods for ROM generation---and not meant to be a catch-all analysis of HTGR ROM generation and usage for optimization. This report summarizes three tasks performed in Fiscal Year 2022: 1) the creation of HTGR model, 2) the sensitivity analysis of model design parameters, and 3) an introduction to ROM generation techniques. The representative HTGR model created in this work is a multiphysics equilibrium-core using the BlueCRAB (comprehensive reactor analysis bundle) reactor analysis application, coupling four physical phenomena: neutronics, streamline depletion, porous flow thermal hydraulics, and pebble heat conduction. Part of the model creation was identifying some design parameters and quantities of interest that are relevant in an optimization analysis and adjustable in the model. The sensitivity analysis utilized a polynomial chaos expansion methodology to compute global sensitivity metrics. This analysis showed that thermal hydraulics parameters and quantities of interest had a relatively small impact on simulation results. Finally, the ROM generation work involved exploring three different ROM methodologies: polynomial regression, a Gaussian process, and artificial neural networks. Using a cross-validation technique to characterize ROM performance, the Gaussian process and single-layer artificial neural networks showed the most promising results. Overall, this study was insightful and the lessons learned will be invaluable for the eventual development of an HTGR design optimization workflow.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

A Microfabricated 8-40 GHz Dual-Polarized Reflector Feed

Planar antennas based on tightly coupled dipole arrays (also known as a current sheet antenna or CSA) are amenable for use as electronically scanned phased arrays. They are capable of performance nearing a decade of bandwidth. These antennas have been demonstrated in many implementations at frequencies below 18 GHz. This paper describes the implementation using a relatively new multi-layer microfabrication process resulting in a small, 6x6 element, dual-linear polarized array with beamformer that operates from 8 to 40 GHz. The beamformer includes baluns that feed the dual-polarized differential antenna elements and reactive splitter networks that also cover the full frequency range of operation. This antenna array serves as a reflector feed for a multi-band instrument designed to measure snow water equivalent (SWE) from airborne platforms. The instrument has both radar and radiome try capability at multiple frequencies. Scattering-parameter and time-domain measurements have been used to characterize the array feed. Radiation patterns of the antenna have been measured and are compared to simulation. To the best of the authors' knowledge, this work represents the most integrated multi-octave millimeter-wave antenna feed fabricated to date.

current sheet antenna↗
DOE OSTI2024

Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering

Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. Here, as a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Physics-informed latent neural operator for real-time predictions of time-dependent parametric PDEs

Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between infinite-dimensional function spaces. However, when applied to systems with high-dimensional input-output mappings arising from large numbers of spatial and temporal collocation points, these models often require heavily overparameterized networks, leading to long training times. Latent DeepONet addresses some of these challenges by introducing a two-step approach: first learning a reduced latent space using a separate model, followed by operator learning within this latent space. While efficient, this method is inherently data-driven and lacks mechanisms for incorporating physical laws, limiting its robustness and generalizability in data-scarce settings. Here, in this work, we propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process. Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space. By embedding PDE constraints into the training via automatic differentiation, our method eliminates the need for labeled training data and ensures physics-consistent predictions. The proposed framework is both memory and compute-efficient, exhibiting near-constant scaling with problem size and demonstrating significant speedups over traditional physics-informed operator models. We validate our approach on a range of parametric PDEs, showcasing its accuracy, scalability, and suitability for real-time prediction in complex physical systems.

Latent representations↗
DOE OSTI2022

Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model

Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via 13 C-metabolic flux analysis ( 13 C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated 13 C labeling data were used with 13 C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

de Haas-van Alphen effect of correlated Dirac states in kagome metal Fe3Sn2

Primarily considered a medium of geometric frustration, there has been a growing recognition of the kagome network as a harbor of lattice-borne topological electronic phases. In this study we report the observation of magnetoquantum de Haas-van Alphen oscillations of the ferromagnetic kagome lattice metal Fe3Sn2. We observe a pair of quasi-two-dimensional Fermi surfaces arising from bulk massive Dirac states and show that these band areas and effective masses are systematically modulated by the rotation of the ferromagnetic moment. Combined with measurements of Berry curvature induced Hall conductivity, our observations suggest that the ferromagnetic Dirac fermions in Fe3Sn2 are subject to intrinsic spin-orbit coupling in the d electron sector which is likely of Kane-Mele type. Our results provide insights for spintronic manipulation of magnetic topological electronic states and pathways to realizing further highly correlated topological materials from the lattice perspective.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Coupling Chemical Energy with Protein Conformational Changes to Translocate Small Molecules Across Membranes

EmrE is a small, homodimeric membrane transporter that exploits the established pH gradient across the E. coli inner membrane to export polyaromatic cations that might otherwise inhibit cellular growth. While herculean efforts through experimental studies have established many fundamental facts about the specificity and rate of substrate transport in EmrE, the low resolution of the available structures have hampered efforts to tie those findings to the EmrE coupling mechanism between proton and small molecule substrates. Here we present a full three-dimensional structure of EmrE optimized against available cyro-EM data to delineate the critical interactions by which EmrE regulates its conformation. We use the generated structural model to conduct equilibrium and nonequilibrium molecular dynamics simulations to probe EmrE dynamics under different substrate loading states, representing different states in the transport cycle. The model is stable under extended simulation, and reveals that water dynamics within the EmrE lumen change substantially with the loading state. The water dynamics cause hydrogen bonding networks to shift radically when the protonation states change for a pair of solvent-exposed glutamate residues (E14) within the lumen of the transporter, which are proposed to act as proton binding sites during the transport cycle. One specific hydrogen bond from a tyrosine (Y60) of one monomer to a glutamate (E14) on the opposite monomer is especially critical, as it locks the protein conformation when the glutamate is deprotonated. Furthermore, the hydrogen bond provided by Y60 lowers the pK a of the interacting glutamate relative to its partner on the opposite monomer such that it will protonate second, establishing the need for both glutamates to be protonated for the hydrogen bond to break and a substrate-free transition to take place.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Single Photon Emitters Coupled to Photonic Wire bonds

This project will test the coupling of light emitted from silicon vacancy and nitrogen vacancy defects in diamond into additively manufactured photonic wire bonds toward integration into an "on-chip quantum photonics platform". These defects offer a room-temperature solid state solution for quantum information technologies but suffer from issues such as low activation rate and variable local environments. Photonic wire bonding will allow entanglement of pre-selected solid-state defects alleviating some of these issues and enable simplified integration with other photonic devices. These developments could prove to be key technologies to realize quantum secured networks for national security applications.

42 ENGINEERING↗
NASA NTRS1993

Some climatological variables for TOGA-COARE domains - 1985-1990

Results are presented of the Tropical Oceans Global Atmosphere-Coupled Ocean Atmosphere Response Experiment (TOGA-COARE) conducted in the western Pacific, with particular attention given to the atmospheric component of the experiment. The climatological variables explored are the mean sea level pressure, precipitable water, sea surface-air temperature difference, zonal wind component at 200 mb and 10 m, vertical motion at 500 mb, and precipitation rates. The paper provides some of the large-scale flow patterns in which other circulation features reside. Results are presented for three of the TOGA-COARE domains bounded by the following: (1) the Upper Air Network, 30 deg N-30 deg S, 90 deg E-70 deg W; (2) the Large-Scale Array, 10 deg N-10 deg S, 140 deg E-180 deg; and (3) the Equatorial Synoptic Array, 5 deg N-5 deg S, 150 deg E-180 deg.

Vincent, Dayton G.↗