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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Effect of aspect ratio on plasma response to resonant magnetic perturbations in tokamak devices

A systematic numerical study is carried out, computing and comparing the plasma response to the resonant magnetic perturbation (RMP) field, applied for controlling edge localized modes (ELMs), in a series of tokamak plasmas with varying aspect ratio and utilizing the MARS-F code. The aspect ratio is scanned either by varying the plasma major radius at a fixed minor radius or by varying the latter while fixing the former. Both approaches yield similar results when compared in terms of quantities with proper normalizations. In general, a non-monotonic dependence of the resonant response field (normalized by the vacuum counterpart) near the plasma edge is found with varying aspect ratio, indicating that a given ELM control coil current configuration strongly favors plasmas with a certain aspect ratio. This optimal aspect ratio, on the other hand, depends on the toroidal as well as poloidal (i.e., coil phasing) spectra of the applied RMP field. The equilibrium (edge) safety factor, the plasma shape, and the plasma toroidal flow are all fixed to ensure that the effects identified here are predominantly due to the plasma aspect ratio.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Development of a neural network model for peeling–ballooning stability analysis in the KSTAR tokamak pedestals

The neural network model, MISHKA-NN is developed to mitigate the computational burden associated with the linear ideal magnetohydrodynamic (MHD) stability analysis of the pedestal based on the peeling–ballooning (P–B) model. By utilizing both 1D plasma profiles (current density, pressure gradient, and safety factor) and 0D parameters (plasma geometry, total current, and toroidal mode number), the model predicts linear growth rate of edge-localized ideal MHD instability in a given equilibrium state. By enabling the prediction of each instability within a second, the model reduces the time required for plotting a pedestal P–B stability diagram (j - α diagram) from approximately 100 CPU hours to a few CPU minutes. Notably, even with the utilization of parametric pressure and current profiles and plasma boundary shapes for the training dataset, the model shows a satisfactory level of performance in benchmarking the j - α diagram for the reconstructed equilibrium from a KSTAR tokamak experiment. We anticipate the model to serve as a versatile alternative to 2D linear MHD stability codes, alleviating numerical costs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The role of shear flow collapse and enhanced turbulence spreading in edge cooling approaching the density limit

Abstract Experimental studies of the dynamics of shear flow and turbulence spreading at the edge of tokamak plasmas are reported. Scans of line-averaged density and plasma current are carried out while approaching the Greenwald density limit on the J-TEXT tokamak. In all scans, when the Greenwald fraction f G = n ¯ / n G = n ¯ / ( I p / π a 2 ) increases, a common feature of enhanced turbulence spreading and edge cooling is found. The result suggests that turbulence spreading is a good indicator of edge cooling, indeed better than turbulent particle transport is. The normalized turbulence spreading power increases significantly when the normalized E × B shearing rate decreases. This indicates that turbulence spreading becomes prominent when the shearing rate is weaker than the turbulence scattering rate. The asymmetry between positive/negative (blobs/holes) spreading events, turbulence spreading power and shear flow are discussed. These results elucidate the important effects of interaction between shear flow and turbulence spreading on plasma edge cooling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Plasma self-driven current in tokamaks with magnetic islands

Magnetic island perturbations may cause a reduction in plasma self-driven current that is needed for tokamak operation. A novel effect on tokamak self-driven current revealed by global gyrokinetic simulations is due to magnetic-island-induced 3D electric potential structures, which have the same dominant mode numbers as that of the magnetic island, whereas centered at both the inner and outer edge of the island. The non-resonant potential islands are shown to drive a current through an efficient nonlinear parallel acceleration of electrons. In large aspect ratio (large-A) tokamak devices, this new effect can result in a significant global reduction of the electron bootstrap current when the island size is sufficiently large, in addition to the local current loss across the island region due to the pressure profile flattening. It is shown that there exists a critical magnetic island width for large-A tokamaks beyond which the electron bootstrap current loss is global and increases rapidly with the island size. As such, this process may introduce a size limit for tolerable magnetic islands in large-A tokamak devices in the context of steady state operation. On the other hand, the current loss caused by magnetic islands in low-A tokamaks such as spherical tokamak (ST) NSTX/U is minor. The reduction of the axisymmetric current by magnetic islands scales with the square of island width. However, the loss of the current is mainly local to the island region, and the pace of current loss as the island size increases is substantially slower compared to large-A tokamaks. In particular, the bootstrap current reduction in STs is even smaller in the reactor-relevant high-β p regime where neoclassical tearing modes are more likely to develop.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Computationally efficient Bayesian estimation of graphical networks for omics data

Graphical networks are useful, widely-used modeling approaches to represent complex biological processes with biological measurements generated by platforms such as mass spectrometry. Bayesian analyses of graphical networks for omics data have several advantages over their frequentist counterparts, such as the inclusion of prior knowledge in the estimation of models. However, Bayesian approaches to date have only been feasible for data with a couple hundred biomolecules due to prohibitive computational time, but omics data often contains tens of thousands of biomolecules. Here, we present and illustrate a more computationally efficient approach named BPlane (Bayesian PseudoLikelihood-based Algorithm for Network Estimation) to extend Bayesian modeling capabilities for larger-sized datasets, such as most untargeted proteomics data. Via simulation, we demonstrate that BPlane produces substantial computational savings over a current state-of-the-art Bayesian algorithm while maintaining competitive edge detection accuracy. On a SARS-CoV2 proteomics data with 7000 proteins, the competing algorithm takes three times as long to complete the first iteration as BPlane takes to converge after over 100 iterations.

EM algorithm↗
DOE OSTI2020

Results from the Alfvén Eigenmode Active Diagnostic during the 2019-2020 JET deuterium campaign

This paper presents results of extensive analysis of mode excitation observed during the operation of the Alfvén Eigenmode Active Diagnostic (AEAD) in the JET tokamak during the 2019-2020 deuterium campaign. Here, six of eight toroidally spaced antennas, each with independent power and phasing, were successful in actively exciting stable MHD modes in 479 plasmas. In total, magnetic resonances were detected with up to fourteen fast magnetic probes. In this work, we present the calculations of resonant frequencies $f_0$, damping rates γ < 0, and toroidal mode numbers $\textit{n}$, spanning the parameter ranges $f_0$ ≈ 30–250 kHz, –γ ≈ 0–13 kHz, and $\vert n \vert \leq {30}$. In general, good agreement is seen between the resonant and the calculated toroidal Alfvén Eigenmode frequencies, and between the toroidal mode numbers applied by the AEAD and estimated of the excited resonances. We note several trends in the database: the probability of resonance detection decreases with plasma current and external heating power; the normalized damping rate increases with edge safety factor but decreases with external heating. These results provide key information to prepare future experimental campaigns and to better understand the physics of excitation and damping of Alfvén Eigenmodes in the presence of alpha particles during the upcoming DT campaign, thereby extrapolating with confidence to future tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Understanding the Impact of Data Staging for Coupled Scientific Workflows

We report the rate of data generated by cutting-edge experimental science facilities and large-scale simulations enabled by current high-performance computing (HPC) systems has continued to grow at a far greater pace than the development of the network and storage capabilities on which these systems rely. To cope with this challenge, scientist are moving toward the creation of autonomous experiments and HPC simulations using machine learning. However, efficiently moving, storing, and processing large amounts of data away from the point of origin presents an incredible challenge. In-memory computing, in situ analysis, data staging, and data streaming are recognized viable alternatives to traditional file-based methods for transferring data between coupled workflows. However, the performance trade-offs and limitations for these methods are not fully understood when used in HPC applications. This article presents a comprehensive performance assessment of the current solutions for data staging when applied to applications that are not necessary I/O intensive which makes them not ideal candidates for these methods. Our study is based on experiments running at scale on Oak Ridge National Laboratory's Summit supercomputer using applications and simulations that cover typical computational motifs and patterns. We investigated the usability and cost/benefit trade-offs of staging algorithms for HPC applications under different scenarios and highlight opportunities for optimizing the dataflow between coupled simulation workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A starting guide to root ecology: strengthening ecological concepts and standardising root classification, sampling, processing and trait measurements

In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting-edge, meaningful and integrated knowledge. Consideration of the below-ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below-ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below-ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine-root vs coarse-root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I–VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers’ views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Model Diagnostics for Equation Oriented Models: Roadblocks and the Path Forward

This poster was presented at the Foundations of Computer Aided Process Design (FOCAPD) 2024 conference with a paper describing efforts to develop a unified workflow and toolbox for diagnosing issues in equation-oriented models as part of the IDAES modeling framework. The poster presents the work currently underway within the IDAES project to develop and integrate cutting edge model analysis techniques into a common toolbox and workflow for model developers to use.

Lee, Andrew↗
DOE OSTI2022

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

17 WIND ENERGY↗
DOE OSTI2024

Model Diagnostics for Equation Oriented Models: Roadblocks and the Path Forward

Conference paper for FOCAPD24 describing efforts to develop a unified workflow and toolbox for diagnosing issues in equation-oriented models as part of the IDAES modeling framework. The paper presents the work currently underway within the IDAES project to develop and integrate cutting edge model analysis techniques into a common toolbox and workflow for model developers to use.

Lee, Andrew↗
DOE OSTI2021

Results from the Alfven Eigenmode Active Diagnostic during the 2019-2020 JET deuterium campaign

This paper presents results of extensive analysis of mode excitation observed during the operation of the Alfven Eigenmode Active Diagnostic (AEAD) in the JET tokamak during the 2019-2020 deuterium campaign. Six of eight toroidally spaced antennas, each with independent power and phasing, were successful in actively exciting stable MHD modes in 479 plasmas. In total, 4768 magnetic resonances were detected with up to fourteen fast magnetic probes. In this work, we present the calculations of resonant frequencies f0, damping rates \gamma < 0, and toroidal mode numbers n, spanning the parameter range f0 = 30 - 250 kHz, -\gamma = 0 - 13 kHz, and |n| < 30. In general, good agreement is seen between the resonant and the calculated toroidal Alfven Eigenmode frequencies, and between the toroidal mode numbers applied by the AEAD and estimated of the excited resonances. We note several trends in the database: the probability of resonance detection decreases with plasma current and external heating power; the normalized damping rate increases with edge safety factor but decreases with external heating. These results provide key information to prepare future experimental campaigns and to better understand the physics of excitation and damping of Alfven Eigenmodes in the presence of alpha particles during the upcoming DT campaign, thereby extrapolating with confidence to future tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

The Pulsating Helium-atmosphere White Dwarfs. I. New DBVs from the Sloan Digital Sky Survey

Abstract We present a dedicated search for new pulsating helium-atmosphere (DBV) white dwarfs from the Sloan Digital Sky Survey using the McDonald 2.1 m Otto Struve Telescope. In total we observed 55 DB and DBA white dwarfs with spectroscopic temperatures between 19,000 and 35,000 K. We find 19 new DBVs and place upper limits on variability for the remaining 36 objects. In combination with previously known DBVs, we use these objects to provide an update to the empirical extent of the DB instability strip. With our sample of new DBVs, the red edge is better constrained, as we nearly double the number of DBVs known between 20,000 and 24,000 K. We do not find any new DBVs hotter than PG 0112+104, the current hottest DBV is at T eff ≈ 31,000 K, but do find pulsations in four DBVs with temperatures between 27,000 and 30,000 K, improving empirical constraints on the poorly defined blue edge. We investigate the ensemble pulsation properties of all currently known DBVs, finding that the weighted mean period and total pulsation power exhibit trends with effective temperature that are qualitatively similar to the pulsating hydrogen-atmosphere white dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Engineering Edge States of Graphene Nanoribbons for Narrow-Band Photoluminescence

Solid-state narrow-band light emitters are on-demand for quantum optoelectronics. Current approaches based on defect engineering in low-dimensional materials usually introduce a broad range of emission centers. Here, we report narrow-band light emission from covalent heterostructures fused to the edges of graphene nanoribbons (GNRs) by controllable on-surface reactions from molecular precursors. Two types of heterojunction (HJ) states are realized by sequentially synthesizing GNRs and graphene nanodots (GNDs) and then coupling them together. HJs between armchair GNDs and armchair edges of the GNR are coherent and give rise to narrow-band photoluminescence. In contrast, HJs between the armchair GNDs and the zigzag ends of GNRs are defective and give rise to nonradiative states near the Fermi level. At low temperatures, sharp photoluminescence emissions with peak energy range from 2.03 to 2.08 eV and line widths of 2–5 meV are observed. Furthermore, the radiative HJ states are uniform, and the optical transition energy is controlled by the band gaps of GNRs and GNDs. As these HJs can be synthesized in a large quantity with atomic precision, this finding highlights a route to programmable and deterministic creation of quantum light emitters.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Achieving Flat-on Primary Crystals by Nanoconfined Crystallization in High-Temperature Polycarbonate/Poly(vinylidene fluoride) Multilayer Films and Its Effect on Dielectric Insulation

To meet the stringent requirements of next-generation film capacitors for power electronics, multilayer films (MLFs) are fabricated with the advantage of achieving high temperature rating, high energy density, and reasonably low loss simultaneously. In this study, a high permittivity polar polymer, poly(vinylidene fluoride) (PVDF), is multilayered with a linear, low loss dielectric polymer such as high-temperature polycarbonate (HTPC). However, the dielectric loss of these MLFs was still high as compared with current state-of-the-art biaxially oriented polypropylene (BOPP) films. The goal of this work is to decrease the dielectric loss and enhance dielectric insulation by achieving flat-on primary PVDF crystals in MLFs via nanoconfined melt-recrystallization. Based on simultaneous small- and wide-angle X-ray scattering experiments, edge-on lamellar crystals were observed for all as-extruded MLFs, regardless of different PVDF layer thicknesses. However, after melting at 180 °C followed by recrystallization, flat-on primary crystals were successfully achieved when the PVDF layer thickness was below 39 nm. Above 78 nm for the PVDF layer, major edge-on primary crystals with minor flat-on secondary crystals were observed. Furthermore, from leakage current, breakdown, lifetime, and electric displacement-electric field loop studies, MLFs with the flat-on primary crystals exhibited reduced loss and enhanced dielectric insulation as compared to as-extruded MLFs and those with edge-on primary/flat-on secondary crystals. This was attributed to the effective blockage of charge carriers by the flat-on PVDF primary crystals and their reduced ferroelectric switching.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Scattering of lower hybrid radio frequency waves by cylindrical turbulent structures in the plasma edge in tokamaks

Lower hybrid (LH) radio frequency (RF) waves are used in fusion tokamak devices to generate non-inductively toroidal currents. LH waves are effective in imparting toroidal momentum to electrons in the core of the confined plasma. Since the LH waves are generated by wave guide structures near the wall of a tokamak, the waves have to propagate through a turbulent plasma in the edge region before coupling to the core. It is especially important to quantify the effect of this plasma region on the propagation characteristics of the LH waves, since fusion reactors like ITER will have an extended edge region. Structures in the plasma edge like filaments and blobs which have highly varying density fluctuations compared to the background density, span radial spatial scales that are comparable to the LH wavelength. We study the scattering of the LH waves by the filamentary structures using Maxwell's equations, in which the plasma permittivity is given by the cold plasma dispersion tensor. The collisional absorption of LH waves in the edge region is included by a modification to the elements of the dispersion tensor. The filaments are assumed to be cylindrical with the axis predominantly aligned along the direction of the toroidal magnetic field. Our studies are both analytical and numerical [1,2] and show that these structures can lead to reflection, refraction, diffraction, and side-scattering of both an incident LH plane wave and a Gaussian beam. We will present a variety of different density variations and collisional absorption rates in plasmas with filamentary structures of varying sizes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Advanced Module Architecture for Reduced Costs, High Durability and Significantly Improved Manufacturability (Final Report)

This project demonstrated a new module architecture (referred to herein as Glass/Glass) using silicone edge seals and an interlayer polymer that reduces manufacturing costs, significantly streamlines manufacturing processes, and reduces cap-ex costs while improving module reliability. A prototype manufacturing process to fabricate this new module architecture was demonstrated with a significantly improved process cycle time for the lamination step from the current industry standard of 13.5 minutes to approximately 30 seconds for each of the individual edge seal and internal polymer application steps. Samples are fabricated, strenuously stressed, and characterized to quantify the cost benefits, hardware capacities and accelerated stress performance of the new architecture. These results are being compared to a standard, laminated baseline architecture using a Glass/EVA/Glass or Glass/Thermoplastic/Glass design that traditionally has high moisture vapor transmission rates (MVTR).

14 SOLAR ENERGY↗