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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
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 OSTI2013

Generation of virtual monochromatic CBCT from dual kV/MV beam projections

Purpose: To develop a novel on-board imaging technique which allows generation of virtual monochromatic (VM) cone-beam CT (CBCT) with a selected energy from combined kilovoltage (kV)/megavoltage (MV) beam projections. Methods: With the current orthogonal kV/MV imaging hardware equipped in modern linear accelerators, both MV projections (from gantry angle of 0°–100°) and kV projections (90°–200°) were acquired as gantry rotated a total of 110°. A selected range of overlap projections between 90° to 100° were then decomposed into two material projections using experimentally determined parameters from orthogonally stacked aluminum and acrylic step-wedges. Given attenuation coefficients of aluminum and acrylic at a predetermined energy, one set of VM projections could be synthesized from two corresponding sets of decomposed projections. Two linear functions were generated using projection information at overlap angles to convert kV and MV projections at nonoverlap angles to approximate VM projections for CBCT reconstruction. The contrast-to-noise ratios (CNRs) were calculated for different inserts in VM CBCTs of a CatPhan phantom with various selected energies and compared with those in kV and MV CBCTs. The effect of overlap projection number on CNR was evaluated. Additionally, the effect of beam orientation was studied by scanning the CatPhan sandwiched with two 5 cm solid-water phantoms on both lateral sides and an electronic density phantom with two metal bolt inserts. Results: Proper selection of VM energy [30 and 40 keV for low-density polyethylene (LDPE), polymethylpentene, 2 MeV for Delrin] provided comparable or even better CNR results as compared with kV or MV CBCT. An increased number of overlap kV and MV projection demonstrated only marginal improvements of CNR for different inserts (with the exception of LDPE) and therefore one projection overlap was found to be sufficient for the CatPhan study. It was also evident that the optimal CBCT image quality was achieved when MV beams penetrated through the heavy attenuation direction of the object. Conclusions: A novel technique was developed to generate VM CBCTs from kV/MV projections. This technique has the potential to improve CNR at selected VM energies and to suppress artifacts at appropriate beam orientations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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↗
DOE OSTI2013

A 2nd Generation Parallel Advancing Front Grid Generator

A scalable, parallel advancing grid generation technique has been developed for complex geometries and meshes with large size variations. The key innovation compared to previous techniques is the use of a domain-defining grid that has the same fine surface triangulation as the final mesh desired, but a much coarser interior mesh. In this way, the domain to be gridded is uniquely defined, overcoming a shortcoming of previous approaches. This domain-defining grid is then partitioned according to the estimated number of elements to be generated, allowing for a balanced distribution of work among the processors. The domain defining grid is also used to redistribute the elements and points after grid generation, and during the subsequent mesh improvement. Timings show that the proposed approach is scalable and able to produce large grids of high quality in a modest amount of clocktime. With the proposed parallel grid generator, a major impediment to a completely scalable simulation pipeline (grid generation, solvers, post-processing) has been removed, opening the way for truly large-scale computations using unstructured, body-fitted grids.

Grid Generation↗
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 OSTI2016

Generating photon pairs from a silicon microring resonator using an electronic step recovery diode for pump pulse generation

Generation of photon pairs from compact, manufacturable, and inexpensive silicon (Si) photonic devices at room temperature may help develop practical applications of quantum photonics. An important characteristic of photon-pair generation is the two-photon joint spectral intensity, which describes the frequency correlations of the photon pair. Recent attempts to generate a factorizable photon-pair state suitable for heralding have used short optical pump pulses from mode-locked lasers, which are much more expensive and bigger table-top or rack-sized instruments compared with the Si microchip used for generating photon pairs, and thus dominate the cost and inhibit the miniaturization of the source. Here, we generate photon pairs from an Si microring resonator by using an electronic step-recovery diode to drive an electro-optic modulator which carves the pump light from a continuous-wave laser diode into pulses of the appropriate width, thus potentially eliminating the need for optical mode-locked lasers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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↗
DOE OSTI2016

A review of sugarcane bagasse for second-generation bioethanol and biopower production: Biorefining Sugar Bagasse

Sugarcane bagasse is a large-volume agriculture residue that is generated on a ~540 million metric tons per year basis globally with the top-three producing countries in Latin America being Brazil (~181 million metric ton yr -1 ), Mexico (15 million metric ton yr -1 ), and Colombia (7 million metric ton yr -1 ), respectively. Furthermore, given sustainability concerns and the need to maximize the utilization of bioresources, the use of sugarcane bagasse is receiving significant attention in biorefining applications, as it is a promising resource for the conversion to biofuels and biopower. Our review provides a comprehensive review of bagasse and its chemical constituents and on-going research into its utilization as a feedstock for cellulosic ethanol and electricity generation.

09 BIOMASS FUELS↗
DOE OSTI2017

Flow-induced vibration analysis of a helical coil steam generator experiment using large eddy simulation

This paper describes a numerical study of flow-induced vibration in a helical coil steam generator experiment conducted at Argonne National Laboratory in the 1980s. In the experiment, a half-scale sector model of a steam generator helical coil tube bank was subjected to still and flowing air and water, and the vibrational characteristics were recorded. The research detailed in this document utilizes the multi-physics simulation toolkit SHARP developed at Argonne National Laboratory, in cooperation with Lawrence Livermore National Laboratory, to simulate the experiment. SHARP uses the spectral element code Nek5000 for fluid dynamics analysis and the finite element code DIABLO for structural analysis. The flow around the coil tubes is modeled in Nek5000 by using a large eddy simulation turbulence model. Transient pressure data on the tube surfaces is sampled and transferred to DIABLO for the structural simulation. The structural response is simulated in DIABLO via an implicit time-marching algorithm and a combination of continuum elements and structural shells. Tube vibration data (acceleration and frequency) are sampled and compared with the experimental data. Currently, only one-way coupling is used, which means that pressure loads from the fluid simulation are transferred to the structural simulation but the resulting structural displacements are not fed back to the fluid simulation

CFD↗