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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Cytosolic Acetyl-CoA Generated by ATP-Citrate Lyase Is Essential for Acetylation of Cell Wall Polysaccharides

Abstract Plant cell wall polysaccharides, including xylan, glucomannan, xyloglucan and pectin, are often acetylated. Although a number of acetyltransferases responsible for the acetylation of some of these polysaccharides have been biochemically characterized, little is known about the source of acetyl donors and how acetyl donors are translocated into the Golgi, where these polysaccharides are synthesized. In this report, we investigated roles of ATP-citrate lyase (ACL) that generates cytosolic acetyl-CoA in cell wall polysaccharide acetylation and effects of simultaneous mutations of four Reduced Wall Acetylation (RWA) genes on acetyl-CoA transport into the Golgi in Arabidopsis thaliana. Expression analyses of genes involved in the generation of acetyl-CoA in different subcellular compartments showed that the expression of several ACL genes responsible for cytosolic acetyl-CoA synthesis was elevated in interfascicular fiber cells and induced by secondary wall-associated transcriptional activators. Simultaneous downregulation of the expression of ACL genes was demonstrated to result in a substantial decrease in the degree of xylan acetylation and a severe alteration in secondary wall structure in xylem vessels. In addition, the degree of acetylation of other cell wall polysaccharides, including glucomannan, xyloglucan and pectin, was also reduced. Moreover, Golgi-enriched membrane vesicles isolated from the rwa1/2/3/4 quadruple mutant were found to exhibit a drastic reduction in acetyl-CoA transport activity compared with the wild type. These findings indicate that cytosolic acetyl-CoA generated by ACL is essential for cell wall polysaccharide acetylation and RWAs are required for its transport from the cytosol into the Golgi.

Cell Biology↗
DOE OSTI2019

Search for pair production of first-generation scalar leptoquarks at s =13 TeV

A search for the pair production of first-generation scalar leptoquarks is performed using proton-proton collision data recorded at 13 TeV center-of-mass energy with the CMS detector at the LHC. The data correspond to an integrated luminosity of 35.9 fb-1. The leptoquarks are assumed to decay promptly to a quark and either an electron or a neutrino, with branching fractions β and 1-β, respectively. The search targets the decay final states comprising two electrons, or one electron and large missing transverse momentum, along with two quarks that are detected as hadronic jets. First-generation scalar leptoquarks with masses below 1435 (1270) GeV are excluded for β=1.0(0.5). These are the most stringent limits on the mass of first-generation scalar leptoquarks to date. The data are also interpreted to set exclusion limits in the context of an R-parity violating supersymmetric model, predicting promptly decaying top squarks with a similar dielectron final state. © 2019 2019 CERN, for the CMS Collaboration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Generation of High-Power, Reversed-Cherenkov Wakefield Radiation in a Metamaterial Structure

Here, we present the first demonstration of high-power, reversed-Cherenkov wakefield radiation by electron bunches passing through a metamaterial structure. The structure supports a fundamental transverse magnetic mode with a negative group velocity leading to reversed-Cherenkov radiation, which was clearly verified in the experiments. Single 45 nC electron bunches of 65 MeV traversing the structure generated up to 25 MW in 2 ns pulses at 11.4 GHz, in excellent agreement with theory. Two bunches of 85 nC with appropriate temporal spacing generated up to 80 MW by coherent wakefield superposition, the highest rf power that metamaterial structures ever experienced without damage. These results demonstrate the unique features of metamaterial structures that are very attractive for future high-gradient wakefield accelerators, including two-beam and collinear accelerators. Advantages include the high shunt impedance for high-power generation and high-gradient acceleration, the simple and rugged structure, and a large parameter space for optimization.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Self-Generation of Chaotic Microwave Signal in Spin Wave Optoelectronic Generator

Abstract—The self-generation of a chaotic microwave signal in a spin-wave optoelectronic generator has been experimentally investigated for the first time. The generator was a ring circuit, in the microwave path of which there was a spin-wave delay line on the film of an yttrium iron garnet (YIG) and a microwave amplifier. The optical path contained a fiber optic delay line. The nonlinear element of the generator was a film of YIG, which worked under the conditions of four-wave parametric interaction of spin waves. It has been shown that it is possible to generate monochromatic, periodic, quasi-periodic, and chaotic microwave signals by controlling the gain coefficient of the ring. The parameters characterizing the chaotic signal that are generated by the ring have been determined.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2009

Generating Code Review Documentation for Auto-Generated Mission-Critical Software

Model-based design and automated code generation are increasingly used at NASA to produce actual flight code, particularly in the Guidance, Navigation, and Control domain. However, since code generators are typically not qualified, there is no guarantee that their output is correct, and consequently auto-generated code still needs to be fully tested and certified. We have thus developed AUTOCERT, a generator-independent plug-in that supports the certification of auto-generated code. AUTOCERT takes a set of mission safety requirements, and formally verifies that the autogenerated code satisfies these requirements. It generates a natural language report that explains why and how the code complies with the specified requirements. The report is hyper-linked to both the program and the verification conditions and thus provides a high-level structured argument containing tracing information for use in code reviews.

Denney, Ewen↗
DOE OSTI2018

Additive Manufacturing: A New Paradigm for the Next Generation of High-Power-Density Direct-Drive Electric Generators

In recent years, there has been a growing demand for high-power-density direct-drive generators in the wind industry owing to their high reliability, torque per unit volume, and conversion efficiencies. However, direct-drive wind turbine generators are very large, low-speed electric machines, which pose remarkable design and manufacturing issues that challenge their upscaling potential and cost of implementation. With air-gap tolerance as the main design driver, the need for high stiffness shifts the focus toward support-structure design that forms a significant portion of the generator's total mass. Existing manufacturing processes allow the use of segmented-steel-weldment disk or spoke-arm assemblies that yield stiffer structures per unit mass but tend to be heavier and more expensive to build. As a result, there is a need for a transformative approach to realize lightweight designs that can also facilitate series production at competitive costs. Inspired by recent developments in metal additive manufacturing (AM), we explore a new freedom in the structural design space with a high potential for weight savings in direct-drive generators. This includes the feasibility of using nonconventional complex geometries, such as lattice-based structures as structurally efficient options. Powder-binder jetting of a sand-cast mold was identified as the most feasible AM technology to produce large-scale generator rotor structures with complex geometry. A parametric optimization study was performed and optimized results within deformation and mass constraints were found for each design. The response to the maximum Maxwell stress due to unbalanced magnetic pull was also explored for each design. Further, a topology optimization was applied for each parameter-optimized design to validate results and provide insights into further mass reduction. These novel designs catered for AM are compared in both deflection and mass to conventional rotor designs using NREL's systems engineering design tool, GeneratorSE. The optimized lattice design with a U-beam truss resulted in a 24% reduction in structural mass of the rotor and 60% reduction in radial deflection. It is demonstrated that additive manufacturing shifts the focus from manufacturability constraints toward lower mass.

17 WIND ENERGY↗
DOE OSTI2018

Additive Manufacturing: A New Paradigm for the Next Generation of High-Power-Density Direct-Drive Electric Generators: Preprint

In recent years, there has been a growing demand for high-power-density direct-drive generators in the wind industry owing to their high reliability, torque per-unit volume, and conversion efficiencies. However, direct-drive wind turbine generators are very large, low-speed electric machines, which pose remarkable design and manufacturing issues that challenge their upscaling potential and cost of implementation. With air-gap tolerance as the main design driver, the need for high stiffness shifts the focus toward support-structure design that forms a significant portion of the generator's total mass. Existing manufacturing processes allow the use of segmented-steel-weldment disk or spoke-arm assemblies that yield stiffer structures per unit mass but tend to be heavier and more expensive to build. As a result, there is a need for a transformative approach to realize lightweight designs that can also facilitate series production at competitive costs. Inspired by recent developments in metal additive manufacturing (AM), we explore a new freedom in the structural design space with a high potential for weight savings in direct-drive generators. This includes the feasibility of using nonconventional complex geometries, such as lattice-based structures as structurally efficient options. Powder-binder jetting of a sand-cast mold was identified as the most feasible AM technology to produce large-scale generator rotor structures with complex geometry. A parametric optimization study was performed and optimized results within deformation and mass constraints were found for each design. The response to the maximum Maxwell stress due to unbalanced magnetic pull was also explored for each design. Further, a topology optimization was applied for each parameter-optimized design to validate results and provide insights into further mass reduction. These novel designs catered for AM are compared in both deflection and mass to conventional rotor designs using NREL's systems engineering design tool, GeneratorSE. The optimized lattice design with a Ubeam truss resulted in a 24% reduction in structural mass of the rotor and 60% reduction in radial deflection. It is demonstrated that additive manufacturing shifts the focus from manufacturability constraints toward lower mass.

17 WIND ENERGY↗
DOE OSTI2022

Evaluating Grid Strength under Uncertain Renewable Generation

The increasing displacement of synchronous generators with renewable resources such as wind and solar via power electronic interfaces causes a reduction in short-circuit strength and weak grid issues. The variation and uncertainty of renewable energy increase challenges for identifying weak grid conditions. This paper proposes an efficient method to analyze the impact of uncertain renewable energy on grid strength. The proposed method uses the probabilistic collocation method (PCM) to approximate the results of grid strength assessment under uncertain renewable generation, in order to reduce computational burden without compromising result accuracy when compared with traditional Monte Carlo simulation (MCS). To improve the accuracy of the approximation results, the proposed method integrates the K-means clustering technique with PCM to select the approximation samples of input variables. The efficacy of the proposed method is demonstrated by comparison with MCS on the modified IEEE 9-bus system and modified IEEE 39-bus system with multiple renewable generators.

grid strength↗
DOE OSTI2021

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation

In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. Most notably, RMG can now generate heterogeneous catalysis models in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release includes parallelization for faster model generation and a new molecule isomorphism approach to improve computational performance. RMG has also been updated to use Python 3, ensuring compatibility with the latest cheminformatics and machine learning packages. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Neutron generator based on intense lithium beam driver

We are proposing a compact neutron generator based on a Li beam driver. The proposed neutron generator comprises a laser ion source, a radio-frequency quadrupole linear accelerator (RFQ linac), a drift tube linac, and a target containing protons. In the generator, the lithium ion is used as a projectile instead of protons to utilize the kinematic focusing technique. The technique enables us to enhance the neutron flux without increasing the beam energy, which is important to develop a clean compact neutron generator. Moreover, the combination of a laser ion source and a RFQ linac with the direct plasma injection scheme will provide several tens of mA of a fully ionized lithium beam, which is much higher than that of conventional heavy ion sources comparable with proton drivers. Neutrons are generated by the nuclear reaction of the lithium ions and protons in the beam target. In this paper, we reported the current status of the development. For RFQ, we designed the RFQ rods to accelerate 40 mA of 7Li3+. We fabricated and installed the rods into a cavity, and, as a first test, accelerated 10 mA of C6+ successfully.

Ikeda, Shunsuke (ORCID:0000000339800577)↗
DOE OSTI2022

Toward fully coherent soft x-ray free-electron laser via echo-enabled harmonic generation in fourth generation synchrotron light sources

Having previously reported on bunching via echo-enabled harmonic generation (EEHG) as an effective way to improve the longitudinal coherence in the NSLS-II storage ring [X. Yang et al., Sci. Rep. 12, 9437 (2022)], we demonstrate that this EEHG scheme can be easily adopted to any fourth generation diffraction-limited synchrotron light source with significant benefits. The advantage of the scheme is that it requires no change of the lattice and is fully compatible with other beamlines. Since the EEHG performance is mainly determined by the momentum compaction, beam emittances, and beta functions of a SR lattice, we have identified these crucial parameters and successfully built a generalized model, which can predict the performance of nearly any SLS. Regarding the fourth generation SLSs, momentum compactions are often significantly smaller; thus, to cover the x rays with a photon energy of up to 1 keV, we utilize a specific design, including a 250 nm seed-laser wavelength. Our model predicts that for most of the current and future fourth generation SLSs, the EEHG scheme can produce significant prebunching up to harmonic 200 and, thus, generate a few MW scale peak power at 1.25 nm wavelength.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve forward, inverse, and mixed stochastic problems in a unified manner based on a limited number of scattered measurements. Unlike standard GANs relying solely on data for training, here we encoded into the architecture of GANs the governing physical laws in the form of stochastic differential equations (SDEs) using automatic differentiation. In particular, we applied Wasserstein GANs with gradient penalty (WGAN-GP) for its enhanced stability compared to vanilla GANs. We first tested WGAN-GP in approximating Gaussian processes of different correlation lengths based on data realizations collected from simultaneous reads at sparsely placed sensors. We obtained good approximation of the generated stochastic processes to the target ones even if there is a mismatch between the input noise dimensionality and the effective dimensionality of the target stochastic processes. We also studied the overfitting issue for both the discriminator and the generator, and we found that overfitting occurs also in the generator in addition to the discriminator as previously reported. Subsequently, we considered the solution of elliptic SDEs requiring approximations of three stochastic processes, namely the solution, the forcing, and the diffusion coefficient. Here again, we assumed data collected from simultaneous reads at a limited number of sensors for the multiple stochastic processes. Furthermore, three generators were used for the PI-GANs: two of them were feed forward deep neural networks (DNNs), while the other one was the neural network induced by the SDE. For the case where we have one group of data, we employed one feed forward DNN as the discriminator, while for the case of multiple groups of data we employed multiple discriminators in PI-GANs. We solved forward, inverse, and mixed problems without changing the framework of PI-GANs, obtaining both the means and the standard deviations of the stochastic solution and the diffusion coefficient in good agreement with benchmarks. In this work, we have demonstrated the effectiveness of PI-GANs in solving SDEs for about 120 dimensions. In principle, PI-GANs could tackle very high dimensional problems given more sensor data with low-polynomial growth in computational cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation

While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used with Rosetta to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Generative network-based approaches to generate stochastic realizations

Generative Adversarial Network (GAN) – based models have been successfully applied in generating different geological models in the literature. However, it is still challenging to use GAN to generate geological realizations with extremely sparse conditioning data (e.g. several well data), which may be regarded as local noise by GAN during the training process. In this work, we propose a novel conditional Generative Adversarial Neural Operator (cGANO) to tackle this challenge. In cGANO, the mapping between conditioning data and output is established through the U-shaped neural operators (UNO), which better preserves local information. Another advantage of using UNO comes from its grid-independent property, which makes the generation of downscaling stochastic geologic realizations possible. We tested the model performance on the IBDP geostatistical dataset with 100 realizations.

58 GEOSCIENCES↗
NASA NTRS1983

Wind turbine generator interaction with diesel generators on an isolated power system

The results of a dynamic interaction investigation to characterize any disturbances caused by interfacing the Mod 0A wind turbine (150 kW configuration) with the Block Island utility diesel generator grid are reported. The tests were run when only two diesel generators were on line, and attention was given to power, frequency, and voltage time profiles. The interconnected system was examined in the start-up and synchronization phase, normal shutdown and cut-out of the wind turbine, during fixed pitch generation, and during variable pitch operation. Governors were installed on the diesel generators to accommodate the presence of wind-derived electricity. The blade pitch control was set to maintain power at 150 kW or below. Power and voltage transients were insignificant during start-up and shutdown, and frequency aberrations were within the range caused by load fluctuations. It is concluded that wind turbine generation can be successfully implemented by an isolated utility, even with a significant penetration to the total grid output.

Scott, G. W.↗
NASA NTRS2010

Route Generation for a Synthetic Character (BOT) Using a Partial or Incomplete Knowledge Route Generation Algorithm in UT2004 Virtual Environment

This paper presents a new Route Generation Algorithm that accurately and realistically represents human route planning and navigation for Military Operations in Urban Terrain (MOUT). The accuracy of this algorithm in representing human behavior is measured using the Unreal Tournament(Trademark) 2004 (UT2004) Game Engine to provide the simulation environment in which the differences between the routes taken by the human player and those of a Synthetic Agent (BOT) executing the A-star algorithm and the new Route Generation Algorithm can be compared. The new Route Generation Algorithm computes the BOT route based on partial or incomplete knowledge received from the UT2004 game engine during game play. To allow BOT navigation to occur continuously throughout the game play with incomplete knowledge of the terrain, a spatial network model of the UT2004 MOUT terrain is captured and stored in an Oracle 11 9 Spatial Data Object (SOO). The SOO allows a partial data query to be executed to generate continuous route updates based on the terrain knowledge, and stored dynamic BOT, Player and environmental parameters returned by the query. The partial data query permits the dynamic adjustment of the planned routes by the Route Generation Algorithm based on the current state of the environment during a simulation. The dynamic nature of this algorithm more accurately allows the BOT to mimic the routes taken by the human executing under the same conditions thereby improving the realism of the BOT in a MOUT simulation environment.

Hanold, Gregg T.↗