Spatially and temporally resolved H and OH densities in a nanosecond pulsed plasma jet: an analysis of the radical generation, transport, recombination and memory effects
Not Available
Yue, Yuanfu (ORCID:0000000247940708)↗
SEARCH · Engineering Papers
Results for “Generators”
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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Impact of Nuclear Power Plant Energy Storage on Grid Reliability and Generation Costs with Variable Power Generation
This report examines the impact of combining integrated energy systems (IES), specifically hydrogen-based energy-storage systems, with nuclear power plants (NPPs) on grid reliability and generation flexibility in scenarios with substantial variable-power generation from solar energy. The analysis focuses on the challenges and solutions associated with high-levels of solar energy penetration, highlighting the role of advanced energy-storage solutions and flexible-generation technologies in enhancing grid stability and reliability.
29 - ENERGY PLANNING, POLICY AND ECONOMY↗
Tunable UV ∼ IR frequency comb generation via high-order sideband generation
Abstract We propose the generation of a widely tunable UV-to-IR frequency comb by high-order sideband generation (HSB) spectrum emitted from semiconductors. In our theoretical simulations, we demonstrate the high-order sideband signals of two series (2m Ω seed + (2n + 1) ω driver , and (2m + 1) Ω seed + 2 n ω driver ), wheremandnare integers of a seed pulse and a driver laser frequency, respectively. The simulations also reveal the intensity of HSB scale with the driver laser power, both perturbatively and non-perturbatively. We find that the harmonic position and spacing of the high-order sideband emission can be controlled by varying the seed pulse and driver photon energies. In the experiment, we applied a visible ( ℏ Ω seed = 3.1 eV, ∼400 nm) seed pulse and mid-infrared (MIR, ℏ ω driver = 0.4 eV, 3.1 μm) driver pulses to ZnSe target. Our experimental observations confirmed the UV (4.7 eV, 263 nm and 3.9 eV, 317 nm) HSB generation.
Physics↗
High temperature and high beam current compatible targets and methods thereof for generating noble gas/radiohalogen generators for medical isotopes
A method of providing alpha particle emitters and materials suitable for use in generating the alpha particles for medical treatment is disclosed. Metal oxide targets, preferentially Bi 2 O 3 pellets and Bi 2 O 3 coatings on metallic or metal oxide substrates are formed. The targets placed in a heated vacuum chamber subjecting to irradiation using a 6 Li beam at an elevated temperature below the melting point of the target generate a radioactive gas, such as 211 Rn, the radioactive gas is carried by an inert gas which is delivered a carrier for, such as a carbon column or oil for delivery to a treatment facility. The radioactive gas such as 211 Rn generates 211 At, which has a useable half-life of at least about 14 hours, in turn releases alpha particles which are effective for use in medical procedures.
Sampathkumaran, Uma↗
Flammable Gas Generation and Control at the Idaho Completion Project Legacy Combustible Gas Generation - 20191
On April 11, 2018, four drums containing transuranic waste at the Idaho National Laboratory underwent over-pressurization, ejecting their lids and spreading radiological waste within a facility. An investigation has found that waste in the drums generated methane gas, which contributed to the event. Subsequent to the investigation, the potential for drums to have methane and other flammable analytes whose concentrations could approach or exceed the lower flammability limit (LFL) and the adequacy of the controls to prevent or mitigate a possible deflagration was evaluated. An extensive review of the historical records was performed to determine how many drums exceed the LFL. The historical record identified a small quantity of drums that exceeded the LFL for xylene, hydrogen, and methane. The primary codified applicable code or standard for handling drums is 29 CFR 1910.120 (j) 'Handling Drums and Containers.' It is used throughout the commercial and government sector. This code is integrated throughout the Fluor Idaho safety management programs and procedures to control the Standard Industrial Hazards (SIHs) associated with drum and container handling. Example requirements include: - Drums and containers are inspected, and their integrity assured prior to being moved. - Site operations are organized to minimize the amount of drum or container movement. - When there is a reasonable possibility of flammable atmospheres being present, material handling equipment and hand tools are of the type to prevent sources of ignition. Drums and containers under pressure, as evidenced by bulging or swelling, are not moved until the cause for excess pressure is determined and appropriate containment procedures have been implemented to protect employees from explosive relief of the drum. Drums which exceed the WIPP FGA limit have NCRs associated with them. Once a drum fails flammable gas sampling, an NCR is initiated. The NCR drives placement into NCR dense pack rows. A standing order drives placement of the FGA failures to a single planer segregation location. These drums are not allowed for further processing or shipment until the NCR has been cleared. Once in the single planer rows, follow-up FGA testing is performed by CCP. The NCR is dispositioned by follow-up sampling occurring after adequate time has been provided for the gas to diffuse through the filter assembly, or, in some cases, the drums are repackaged into additional drums to reduce the source term. To date, the ICP project has performed approximately 1.5 million drums moves without a deflagration event due to combustible gas generation. Flammable gas generation in a transuranic waste drum is not unique to the ICP but is common across the DOE complex. Based on the experience at Idaho, application of industry standards is sufficient to control the risk of drum deflagration due to drum movements. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Transient Stability Preventive Control via Tuning the Parameters of Virtual Synchronous Generators
This paper presents an optimal preventive control (OPC) method to improve the power system transient stability via tuning the transient parameter of virtual synchronous generators. The novelty of this work is that we formulate the preventive control as an optimization problem so that inverter parameters can be adjusted at the pre-contingency stage. A reinforcement learning (RL)-driven method is proposed to solve the OPC problem with the fault energy-based reward function. An ANDES-based RL environment is also developed. Versatile functions included in the proposed environment have been presented in this paper. The proposed OPC formulation, the RLdriven method, and the fault energy-based reward function are verified on several standard test systems.
Inverter-based resources, virtual synchronous gene↗
ProfileSR-GAN: A GAN Based Super-Resolution Method for Generating High-Resolution Load Profiles
Not provided.
Engineering↗
CoordNet: Data Generation and Visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural Network
Although deep learning has demonstrated its capability in solving diverse scientific visualization problems, it still lacks generalization power across different tasks. To address this challenge, we propose CoordNet, a single coordinate-based framework that tackles various tasks relevant to time-varying volumetric data visualization without modifying the network architecture. The core idea of our approach is to decompose diverse task inputs and outputs into a unified representation (i.e., coordinates and values) and learn a function from coordinates to their corresponding values. We achieve this goal using a residual block-based implicit neural representation architecture with periodic activation functions. We evaluate CoordNet on data generation (i.e., temporal super-resolution and spatial super-resolution) and visualization generation (i.e., view synthesis and ambient occlusion prediction) tasks using time-varying volumetric data sets of various characteristics. Here, the experimental results indicate that CoordNet achieves better quantitative and qualitative results than the state-of-the-art approaches across all the evaluated tasks. Source code and pre-trained models are available at https://github.com/stevenhan1991/CoordNet.
97 MATHEMATICS AND COMPUTING↗
Electrical Validation Testing for ORPC MHK Generator, Modification 6: ORPC SBV for MHK Generator System (CRADA Final Report)
For the U.S. Department of Energy’s (DOE) 2016 Small Business Voucher for Marine and Hydrokinetic (MHK) System, Second Round 2016, ORPC intends to work with the National Renewable Energy Laboratory (NREL) to perform dynamometer testing of the MHK generator systems and its associated controls and inverters. ORPC will provide the generator, variable frequency drives (VFD), controls, and inverter for this testing. NREL will utilize the NREL Energy Systems Integration Facility (ESIF) and dynamometer facilities at the National Wind Technology Center (NWTC) for this work. Modification 6: Additionally, NREL will conduct a feasibility study for implementing passive DC rectification at the turbine.
17 WIND ENERGY↗
Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets
This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables corresponding to independent physical factors. Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with minimal modifications to the standard VAE loss function by leveraging prior statistical knowledge through auxiliary variables. These variables guide the shaping of the latent space by aligning latent factors with learned auxiliary variables. We validate the efficacy of Aux-VAE through comparative assessments on multiple datasets, including astronomical simulations.
97 MATHEMATICS AND COMPUTING↗
InDEEP, DEEC-Tec, and Direct Generation Synergies for Ocean Wave Energy: Wave Energy Scotland's Direct Generation Programme Review
The presentation highlights: (i) how DEEC-Tec, advanced through the U.S. DOE's InDEEP Prize, holds the potential to redefine ocean wave energy conversion through modular, embedded energy conversion; (ii) a global scan that identified 158 participants and 35 promising concepts, showcasing strong international momentum; (iii) InDEEP's innovation-first approach and how it complements Wave Energy Scotland's structured Direct Generation research; and (iv) the ongoing and possible future collaborative pathways for co-funded trials, joint metric/standards developments, and maturation of these technologies toward and real-world deployment.
16 TIDAL AND WAVE POWER↗
Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts
Not provided.
Energy & Fuels↗
Oxidative Deboronation of Boronic Acids by Hydrogen Peroxide in Planta Generates Borate for Cross-Linking of Rhamnogalacturonan II
Vascular plants require boron to cross-link the rhamnogalacturonan-II (RG-II) domain of pectin to form functional cell walls. Boronic acids, which form reversible esters with cis-diols like borate, have been proposed to influence RG-II cross-linking, though the mechanism remains unclear. We used suspension-cultured rose cells adapted to grow without boron to investigate the effect of boronic acids on RG-II dimerization. When grown with phenylboronic acid (PBA) as the sole boron source, nearly all RG-II was crosslinked, whereas methylboronic acid (MBA) only partially restored cross-linking. In contrast, in vitro assays showed that homogeneous RG-II monomers did not dimerize with alkyl or aryl boronic acids unless supplemented with hydrogen peroxide (H2O2), which oxidatively converts boronic acids to boric acid. Real-time NMR spectroscopy and density functional theory calculations provided insight into the reaction mechanism and energetics of oxidation respectively. Together, our data show that exogenous boronic acids are a source of boric acid for plants, and that the deboronation reaction generates aryl or alkyl alcohol byproducts that can undergo further chemical modification in planta. The fate and potential roles of these byproducts in planta remain to be determined.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A novel approach for adaptive skeleton toolpath generation
Industry 4.0 is revolutionizing manufacturing through the integration of automation and real-time data sharing in cyber-physical systems. At the forefront of this revolution is large-format additive manufacturing. In large-format printing, parts are often designed to be an even number of beads wide to produce a completely dense part. However, voids can still arise. This is often due to the part not being an even number of bead widths wide in some areas, or in geometry containing acute angles, as the process of generating closed contours cannot completely fill the space. Voids can be tolerated in smaller models, but in large-format additive manufacturing they may cause mechanical defects. To fill these voids, open loop paths called skeletons are often used, but they are typically limited by the physical constraints defined in the slicing software. To address this, researchers at Oak Ridge National Laboratory have extended skeleton toolpaths via an adaptive methodology. These adaptive skeletons were found to better fill void spaces through manipulation of physical parameters of the build process and were calculated as part of the slicing process.
42 ENGINEERING↗
Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation
The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.
ChemBERTa↗
Automated Symbolic Upscaling: 2. Model Generation for Extended Applicability Regimes
Abstract In this second part of the two paper series, we detail an algorithmic procedure for systematically implementing the generalized closure form strategy presented in Part 1. This strategy extends the applicability of homogenized models with respect to classical homogenization theory, as demonstrated in Part 1 where upscaled models are rigorously derived in moderately reactive physical regimes. After encoding the algorithm into Symbolica, an automated upscaling framework, we upscale two reactive mass transport problems and numerically validate the resulting nonlinear homogenized models by showing the absolute error estimates predicted by homogenization theory are satisfied. In both problems, nontrivial closure forms and closure problems are automatically formulated using the encoded strategy with no human interaction, nor prior knowledge regarding the closure required for the systems. We hope these demonstrations spark further interest in automated analytical frameworks for multiscale modeling, as such capabilities are invaluable for generating rigorous multiscale models of complex phenomena in porous media.
Pietrzyk, Kyle↗
A systematic approach to generating accurate neural network potentials: the case of carbon
Abstract Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.
Chemistry↗