Results for “Hybrid Work” — Page 13

NASA NTRS2017

Highly Conductive Wire: Cu Carbon Nanotube Composite Ampacity and Metallic CNT Buckypaper Conductivity

NASA is currently working on developing motors for hybrid electric propulsion applications in aviation. To make electric power more feasible in airplanes higher power to weight ratios are sought for electric motors. One facet to these efforts is to improve (increase) the conductivity and (lower) density of the magnet wire used in motors. Carbon nanotubes (CNT) and composites containing CNT are being explored as a possible way to increase wire conductivity and lower density. Presented here are measurements of the current carrying capacity (ampacity) of a composite made from CNT and copper. The ability of CNT to improve the conductivity of such composites is hindered by the presence of semiconductive CNT (s-CNT) that exist in CNT supplies naturally, and currently, unavoidably. To solve this problem, and avoid s-CNT, various preferential growth and sorting methods are being explored. A supply of sorted 95 metallic CNT (m-CNT) was acquired in the form of thick film Buckypaper (BP) as part of this work and characterized using Raman spectroscopy, resistivity, and density measurements. The ampacity (Acm2) of the Cu-5volCNT composite was 3.8 lower than the same gauge pure Cu wire similarly tested. The lower ampacity in the composite wire is believed to be due to the presence of s-CNT in the composite and the relatively low (proper) level of longitudinal cooling employed in the test method. Although Raman spectroscopy can be used to characterize CNT, a strong relation between the ratios of the primary peaks GGand the relative amounts of m-CNT and s-CNT was not observed. The average effective conductivity of the CNT in the sorted, 95 m-CNT BP was 2.5 times higher than the CNT in the similar but un-sorted BP. This is an indication that improvements in the conductivity of CNT composites can be made by the use of sorted, highly conductive m-CNT.

wire↗

NASA NTRS2016

Carbon Nanotube Composite Ampacity and Metallic CNT Buckypaper Conductivity

NASA is currently working on developing motors for hybrid electric propulsion applications in aviation. To make electric power more feasible in airplanes higher power to weight ratios are sought for electric motors. One facet to these efforts is to improve (increase) the conductivity and (lower) density of the magnet wire used in motors. Carbon nanotubes (CNT) and composites containing CNT are being explored as a possible way to increase wire conductivity and lower density. Presented here are measurements of the current carrying capacity (ampacity) of a composite made from CNT and copper. The ability of CNT to improve the conductivity of such composites is hindered by the presence of semiconductive CNT (s-CNT) that exist in CNT supplies naturally, and currently, unavoidably. To solve this problem, and avoid s-CNT, various preferential growth and sorting methods are being explored. A supply of sorted 95 metallic CNT (m-CNT) was acquired in the form of thick film Buckypaper (BP) as part of this work and characterized using Raman spectroscopy, resistivity, and density measurements. The ampacity (Acm2) of the Cu-5volCNT composite was 3.8 lower than the same gauge pure Cu wire similarly tested. The lower ampacity in the composite wire is believed to be due to the presence of s-CNT in the composite and the relatively low (proper) level of longitudinal cooling employed in the test method. Although Raman spectroscopy can be used to characterize CNT, a strong relation between the ratios of the primary peaks GGand the relative amounts of m-CNT and s-CNT was not observed. The average effective conductivity of the CNT in the sorted, 95 m-CNT BP was 2.5 times higher than the CNT in the similar but un-sorted BP. This is an indication that improvements in the conductivity of CNT composites can be made by the use of sorted, highly conductive m-CNT.

yarns↗

NASA NTRS2018

Demonstration of a 600 Watt Hybrid Iodine-Xenon Electric Propulsion System

This paper reviews recent iodine compatible electric propulsion technologies evaluated at the NASA Glenn Research Center. The work culminated in a 1,174-hour hybrid iodine-xenon propulsion system durability demonstration (iodine fed thruster with xenon fed cathode). The test demonstrated that (i) a Hall-effect thruster operates with nearly identical performance whether employing iodine or xenon propellant, (ii) careful selection of propulsion system materials and coatings can result in durable iodine-compatible hardware, and (iii) implementation of appropriate facility improvements and procedures can limit negative impacts of iodine on test hardware and ground support equipment. The work was motivated by advancements in capability of very dense small-spacecraft (<500 kg), but spacecraft still lacking very dense in-space propulsion to provide the large delta-v capabilities (3 to 10 km/s) required for many high-value NASA exploration and science missions. Volume limitations of small-spacecraft not only require dense propulsion systems, but more so dense propellants. NASA identifies xenon and iodine as having both favorable storage densities and propulsive properties to enable many NASA small-spacecraft mission scenarios. While xenon is inert and well-proven in spaceflight applications, iodine has triple the storage density of xenon and stores at low pressures, permitting use of conformal tank designs. Unlike xenon, iodine does raise concerns related to its reactivity with most materials, potential spacecraft-propellant interactions, impact on ground test facilities, and challenges to acceptance test iodine propulsion systems prior to flight. This work begins to address these challenges. This work was conducted under the Advanced In-Space Propulsion (AISP) project funded through the Game Changing Development (GCD) program within NASA's Science Technology Mission Directorate (STMD).

Benavides, G. F.↗

NASA NTRS2013

The Mechanical Properties of Candidate Superalloys for a Hybrid Turbine Disk

The mechanical properties of several cast blade superalloys and one powder metallurgy disk superalloy were assessed for potential use in a dual alloy hybrid disk concept of joined dissimilar bore and web materials. Grain size was varied for each superalloy class. Tensile, creep, fatigue, and notch fatigue tests were performed at 704 to 815 degC. Typical microstructures and failure modes were determined. Preferred materials were then selected for future study as the bore and rim alloys in this hybrid disk concept. Powder metallurgy superalloy LSHR at 15 micron grain size and single crystal superalloy LDS-1101+Hf were selected for further study, and future work is recommended to develop the hybrid disk concept.

Gabb, Timothy P.↗

DOE OSTI2021

Interlayer Triplet-Sensitized Luminescence in Layered Two-Dimensional Hybrid Metal-Halide Perovskites

In this work, we review an emerging class of 2D layered metal-halide perovskites that exhibit important photophysical behavior involving inorganic exciton-to-molecular triplet energy flow. These novel systems have potential to impact several energy-related fields and processes, including optoelectronics, photon upconversion strategies, and triplet-based photocatalysis. Early studies provided initial guidance and revealed several intriguing questions that more recent developments, in terms of both expanded and diversified compositional tuning and more sophisticated characterization, have begun to answer. These questions center around the delicate interplay between physical and electronic structure that is perturbed by choice of perovskite structural motif and organic spacer properties. In addition to their distinct natures, the energy offsets between the metal-halide and molecular excitons can be leveraged for a variety of unique effects and emergent properties, including fast interlayer energy transfer. We review the key advances with an eye toward strategies for controlling photophysical outcomes based on structure-energy-function relationships.

14 SOLAR ENERGY↗

NASA NTRS2023

The Gmao Hybrid 4d-Envar Observing System Simulation Experiment Framework.

This work describes the extension of the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework to use a hybrid 4D-EnVar scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short inter-comparison of upgrading from a 3D- to a 4D-OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.

Data assimilation↗

DOE OSTI2024

Parallel hybrid quantum-classical machine learning for kernelized time-series classification

Supervised time-series classification garners widespread interest because of its applicability throughout a broad application domain including finance, astronomy, biosensors, and many others. Here, in this work, we tackle this problem with hybrid quantum-classical machine learning, deducing pairwise temporal relationships between time-series instances using a timeseries Hamiltonian kernel (TSHK). A TSHK is constructed with a sum of inner products generated by quantum states evolved using a parameterized time evolution operator. This sum is then optimally weighted using techniques derived from multiple kernel learning. Because we treat the kernel weighting step as a differentiable convex optimization problem, our method can be regarded as an end-to-end learnable hybrid quantum-classical-convex neural network, or QCC-net, whose output is a data set-generalized kernel function suitable for use in any kernelized machine learning technique such as the support vector machine (SVM). Using our TSHK as input to a SVM, we classify univariate and multivariate time-series using quantum circuit simulators and demonstrate the efficient parallel deployment of the algorithm to 127-qubit superconducting quantum processors using quantum multi-programming.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Observation of quasi-coherent density fluctuation in scrape-off layer enhancing boundary transport in high-βN hybrid plasmas on DIII-D

In this work, we report the observation of a quasi-coherent density fluctuation (QCF) by the Doppler backscattering system in the scrape-off layer (SOL) region of the DIII-D tokamak. This QCF is observed in high power, high performance hybrid plasmas with near double-null divertor (DND) shape during the electron cyclotron heating period. This mode is correlated with a steepened SOL density profile, and leads to significantly elevated particle and heat fluxes between ELMs. The SOL QCF is a long-wavelength ion-scale fluctuation (k θ ρ s ≈0.2-0.4 and k r ρ s ≈0.03), and propagates in the ion diamagnetic direction in the plasma frame. Its radial expanse is about 1.5–2 cm, well beyond the typical width of heat flux λ q on DIII-D. Also, the SOL QCF does not show any clear dependence on the effective SOL collisionality, and thus may raise issues on the control of plasma-material interactions in low collisionality plasmas in which the blob-induced transport is reduced. A linear simulation using BOUT++ with a 5-field reduced model is performed and compared with experimental observations. In simulation results, an interchange-like density perturbation can be driven by the SOL density gradient, and its peak location and the radial width of the density perturbation are in agreement with the experimental observations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Modeling the spectral modification of lower hybrid wave in the presence of drift-wave type density fluctuation in the scrape-off-layer of the EAST tokamak

In this work, the spectrum change of the lower hybrid (LH) waves caused by a low-frequency density fluctuation in the scrape-off-layer is studied by applying the wave scattering model developed by Bonoli and Ott [Phys. Fluids 25, 359 (1982)] via a Monte Carlo method. A ray-tracing model specific to this purpose is developed to evaluate the probability distribution of both the poloidal refractive index (N θ ) and the parallel refractive index (N ∥ ) of the LH wave at the last closed flux surface (LCFS).The probability distributions of N ∥ and N θ at the LCFS are studied using the EAST parameters as a function of wave frequency, the initial N ∥ , and the polar injection position, which may influence the lower hybrid current drive efficiency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2019

Hybrid Fuel Coupling in a Pulsed Z-Pinch Rocket Engine

The work presented here sought to explore a portion of the parameter space of a hybrid nuclear fuel in regards to ignition and burn by analyzing the effect of initial geometry and thermodynamic conditions. The authors performed 0D power balance and 1D burn wave calculations to determine temperature progression and energy production for defined initial conditions. Geometries examined are representative of concept fuels for a Pulsed Fission-Fusion (PuFF) engine. This work focuses on lithium deuteride and uranium 235 for the fuel since these are seen as leading candidates for PuFF. Presented below is a power balance illustrating a reduction in the energy and density required to breakeven of hybrid fuels in comparison with fusion fuels. Also the impact of fusion and fissile fuel quantities upon initial energies is presented. One can see that the initial energy required to breakeven in a hybrid cylindrical nuclear fuel decreases with decreasing fissile liner thickness, decreasing fusion fuel core radius, and increasing compression ratio of the fusion fuel.

Taylor, Brian↗

NASA NTRS2019

Hybrid Fuel Coupling in a Pulsed Z-Pinch Rocket Engine

The work presented here sought to explore a portion of the parameter space of a hybrid nuclear fuel in regards to ignition and burn by analyzing the effect of initial geometry and thermodynamic conditions. The authors performed 0D power balance and 1D burn wave calculations to determine temperature progression and energy production for defined initial conditions. Geometries examined are representative of concept fuels for a Pulsed Fission-Fusion (PuFF) engine. This work focuses on lithium deuteride and uranium 235 for the fuel since these are seen as leading candidates for PuFF. Presented below is a power balance illustrating a reduction in the energy and density required to breakeven of hybrid fuels in comparison with fusion fuels. Also the impact of fusion and fissile fuel quantities upon initial energies is presented. One can see that the initial energy required to breakeven in a hybrid cylindrical nuclear fuel decreases with decreasing fissile liner thickness, decreasing fusion fuel core radius, and increasing compression ratio of the fusion fuel.

Taylor, Brian↗

DOE OSTI2025

Direct NeTS sampling of nuclear graphite $S(α, β, T)$ in Serpent

For advanced reactor applications, Neural Thermal Scattering (NeTS) modules were developed to predict the thermal scattering law (TSL or $S(α, β, T)$) of a nuclear graphite neutron moderator. NeTS are multi-layer, feedforward artificial neural networks, which act as universal function approximators designed for TSL datasets. In this case, a 4-layer neural network with 164 neurons per layer is trained using FLASSH evaluated data in PyTorch and serialized as a torchscript dictionary to predict $S(α, β, T)$ on-the-fly. Relative, absolute and maximum percent deviations of NeTS from File 7 data generated using the FLASSH code are on the order of 0.01%, 0.1% and 1%, respectively, with low inference latencies of 0.000172 s per $S(α, β, T)$ at a given temperature. Capturing the full dimensionality of possible inelastic neutron-lattice interactions, NeTS functionality is embedded in the Serpent Monte Carlo code, where $S(α, β, T)_{NeTS}$ sampling is conducted on-the-fly and compared to ACE look-up-tables for predicting TREAT criticality. k-eff differences between sampling algorithms of 6 pcm are observed and are within the order of Monte Carlo uncertainty. Compared to discrete and continuous-energy ACE files (30 MB and 131 MB per temperature), the NeTS format is on the order of 200–300 kB for a continuous-temperature, interpolation-free representation of $S(α, β, T)$ and cross sections. NeTS-in-Serpent runtimes comparable with ACE look-up tables are achieved by scaling NeTS for high performance computing architectures with hybrid OpenMP + MPI parallelization. This work validates a novel, self-contained reactor physics framework for predictive cross sections, and demonstrates a general methodology for embedding modern machine learning libraries within existing neutronic analysis frameworks.

Nuclear Criticality Safety Program (NCSP)↗

DOE OSTI2023

α -cluster microscopic study of C 12 + C 12 fusion toward the zero energy limit

The carbon burning process is a fundamental step of stellar evolution and governs the synthesis of chemical elements important for the formation of life. In this work, we utilize the microscopic hybrid α cluster (HαC) model and an analytical approach, both in the framework of the Imaginary Time Method (ITM), to study the carbon fusion reaction towards zero energy. We obtain the values of the cross sections, astrophysical factors and correlate our results to collective motion. We also include a calculation for the 2 + carbon fusion and discuss a possible experimental investigation. Our results confirm direct experimental and theoretical results close to the barrier, while suggest possible 2 + mixtures in the indirect experimental data. Furthermore, our study offers an accurate view of the burning process in the somewhat unexplored low energy region.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Iodine Vacancies do not Cause Nonradiative Recombination in Halide Perovskites

The iodine vacancy (V I ) has frequently been discussed as a strong nonradiative recombination center in halide perovskites. This proposition was mainly based on the presence of charge-state transition levels in the band gap, as found in early first-principles calculations. In this work, we perform accurate hybrid-density-functional calculations for V I in CsPbI 3 , CsSnI 3 , and CsGeI 3 and find that V I does not have any transition levels in the band gap in CsPbI 3 , in contrast to the results from calculations based on semilocal functionals. The iodine vacancy V I does introduce levels in the band gap in CsSnI 3 and CsGeI 3 , but our explicitly computed nonradiative capture coefficients demonstrate that V I has a negligible impact on nonradiative recombination. Our study corrects a misunderstanding of the role of V I in the iodide-based perovskites, and shifts the focus toward identifying and mitigating actual recombination centers in order to further improve the optoelectronic performance.

14 SOLAR ENERGY↗

DOE OSTI2025

Estimating Eigenenergies from Quantum Dynamics: A Unified Noise-Resilient Measurement-Driven Approach

Ground state energy estimation in physical, chemical, and materials sciences is one of the most promising applications of quantum computing. In this work, we introduce a new hybrid approach that finds the eigenenergies by collecting real-time measurements and post-processing them using the machinery of dynamic mode decomposition (DMD). From the perspective of quantum dynamics, we establish that our approach can be formally understood as a stable variational method on the function space of observables available from a quantum many-body system. We also provide strong theoretical and numerical evidence that our method converges rapidly even in the presence of a large degree of perturbative noise, and show that the method bears an isomorphism to robust matrix factorization methods developed independently across various scientific communities. Our numerical benchmarks on spin and molecular systems demonstrate an accelerated convergence and a favorable resource reduction over state-of-the-art algorithms. The DMD-centric strategy can systematically mitigate noise and stands out as a leading hybrid quantum-classical eigensolver.

Shen, Yizhi↗

DOE OSTI2021

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2001

Baseline Testing of The EV Global E-Bike

The NASA John H. Glenn Research Center initiated baseline testing of the EV Global E-Bike as a way to reduce pollution in urban areas, reduce fossil fuel consumption and reduce Operating costs for transportation systems. The work was done Linder the Hybrid Power Management (HPM) Program, which includes the Hybrid Electric Transit Bus (HETB). The E-Bike is a state of the art, ground up, hybrid electric bicycle. Unique features of the vehicle's power system include the use of an efficient, 400 W. electric hub motor and a 7-speed derailleur system that permits operation as fully electric, fully pedal, or a combination of the two. Other innovative features, such as regenerative braking through ultracapacitor energy storage are planned. Regenerative braking recovers much of the kinetic energy of the vehicle during deceleration. The E-Bike is an inexpensive approach to advance the state of the art in hybrid technology in a practical application. The project transfers space technology to terrestrial use via nontraditional partners, and provides power system data valuable for future space applications. A description of the E-bike, the results of performance testing, and future vehicle development plans is the subject of this report. The report concludes that the E-Bike provides excellent performance, and that the implementation of ultracapacitors in the power system can provide significant performance improvements.

Eichenberg, Dennis J.↗

NASA NTRS2001

Baseline Testing of the Ultracapacitor Enhanced Photovoltaic Power Station

The NASA John H. Glenn Research Center is developing an advanced ultracapacitor enhanced photovoltaic power station. Goals of this effort include maximizing photovoltaic power generation efficiency and extending the life of photovoltaic energy storage systems. Unique aspects of the power station include the use of a solar tracker, and ultracapacitors for energy storage. The photovoltaic power station is seen as a way to provide electric power in remote locations that would otherwise not have electric power, provide independence form utility systems, reduce pollution, reduce fossil fuel consumption, and reduce operating costs. The work was done under the Hybrid Power Management (HPM) Program, which includes the Hybrid Electric Transit Bus (HETB), and the E-Bike. The power station complements the E-Bike extremely well in that it permits the charging of the vehicle batteries in remote locations. Other applications include scientific research and medical power sources in isolated regions. The power station is an inexpensive approach to advance the state of the art in power technology in a practical application. The project transfers space technology to terrestrial use via nontraditional partners, and provides power system data valuable for future space applications. A description of the ultracapacitor enhanced power station, the results of performance testing and future power station development plans is the subject of this report. The report concludes that the ultracapacitor enhanced power station provides excellent performance, and that the implementation of ultracapacitors in the power system can provide significant performance improvements.

Eichenberg, Dennis J.↗