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Results for “Networking Data Plasma Physics”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Curling probe measurement of electron density in pulse-modulated plasma

The electron density n{sub e} of stationary plasma can be easily obtained on the basis of the resonance frequency f of a curling probe (CP) measured by a network analyzer (NWA). However, in pulsed plasma with discharge period T, the n{sub e} and f values periodically change with time. This study extends the conventional CP technique to a time-resolved measurement of the pulse-modulated electron density. The condition necessary for the measurement is revealed to be synchronization of NWA with the pulse modulation, which is expressed as (n − 1)T/T{sub SWP} = integer (1, 2, …) for a number n of data point and sweep time T{sub SWP}.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Waveform retrieval for ultrafast applications based on convolutional neural networks

Electric field waveforms of light carry rich information about dynamical events on a broad range of timescales. The insight that can be reached from their analysis, however, depends on the accuracy of retrieval from noisy data. In this article, we present a novel approach for waveform retrieval based on supervised deep learning. We demonstrate the performance of our model by comparison with conventional denoising approaches, including wavelet transform and Wiener filtering. The model leverages the enhanced precision obtained from the nonlinearity of deep learning. The results open a path toward an improved understanding of physical and chemical phenomena in field-resolved spectroscopy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Novel High-Power Microwave Circulator Employing Circularly Polarized Waves

A novel four-port circulator is presented which uses an inline ferromagnetic element to allow for transmission or reflection depending on the sense of the incident circularly polarized wave. This configuration has been shown to reduce the dependency on ferrite anisotropy and support higher power, low rf loss, operation. An analytic analysis of this device is presented here alongside corroborating cold test data of the first prototype. High-power operation was performed at 2.856 GHz, with input power levels up to 8 MW for 3.5 μs in a pressurized nitrogen environment. The results from this research not only demonstrate ability to eliminate the use of greenhouse insulators, such as SF6, but also provide conceptual groundwork for a new class of ultrahigh power (50 MW+) nonreciprocal networks including circulators, isolators, phase shifters, and rf switches.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2010

First Results of Venus Express Spacecraft Observations with Wettzell

The ESA Venus Express spacecraft was observed at X-band with the Wettzell radio telescope in October-December 2009 in the framework of an assessment study of the possible contribution of the European VLBI Network to the upcoming ESA deep space missions. A major goal of these observations was to develop and test the scheduling, data capture, transfer, processing, and analysis pipeline. Recorded data were transferred from Wettzell to Metsahovi for processing, and the processed data were sent from Mets ahovi to JIVE for analysis. A turnover time of 24 hours from observations to analysis results was achieved. The high dynamic range of the detections allowed us to achieve a milliHz level of spectral resolution accuracy and to extract the phase of the spacecraft signal carrier line. Several physical parameters can be determined from these observational results with more observational data collected. Among other important results, the measured phase fluctuations of the carrier line at different time scales can be used to determine the influence of the solar wind plasma density fluctuations on the accuracy of the astrometric VLBI observations.

Calves, Guifre Molera↗
DOE OSTI2015

Electron density dependence of impedance probe plasma potential measurements

In earlier works, we used spheres of various sizes as impedance probes in demonstrating a method of determining plasma potential, φ{sub p}, when the probe radius is much larger than the Debye length, λ{sub D}. The basis of the method in those works [Walker et al., Phys. Plasmas 13, 032108 (2006); ibid. 15, 123506 (2008); ibid. 17, 113503 (2010)] relies on applying a small amplitude signal of fixed frequency to a probe in a plasma and, through network analyzer-based measurements, determining the complex reflection coefficient, Γ, for varying probe bias, V{sub b}. The frequency range of the applied signal is restricted to avoid sheath resonant effects and ion contributions such that ω{sub pi} ≪ ω ≪ ω{sub pe}, where ω{sub pi} is the ion plasma frequency and ω{sub pe} is the electron plasma frequency. For a given frequency and applied bias, both Re(Z{sub ac}) and Im(Z{sub ac}) are available from Γ. When Re(Z{sub ac}) is plotted versus V{sub b}, a minimum predicted by theory occurs at φ{sub p} [Walker et al., Phys. Plasmas 17, 113503 (2010)]. In addition, Im(Z{sub ac}) appears at, or very near, a maximum at φ{sub p}. As n{sub e} decreases and the sheath expands, the minimum becomes harder to discern. The purpose of this work is to demonstrate that when using network analyzer-based measurements, Γ itself and Im(Z{sub ac}) and their derivatives are useful as accompanying indicators to Re(Z{sub ac}) in these difficult cases. We note the difficulties encountered by the most commonly used plasma diagnostic, the Langmuir probe. Spherical probe data is mainly used in this work, although we present limited data for a cylinder and a disk. To demonstrate the effect of lowered density as a function of probe geometry, we compare the cylinder and disk using only the indicator Re(Z{sub ac})

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Transmission characteristics of microwave in a glow-discharge dusty plasma

In this study, the propagation characteristics of electromagnetic wave in a glow discharge plasma with dust particles are experimentally investigated. A helium alternating current glow discharge plasmas have been successfully generated. Measurements of the plasma parameters using Langmuir probes, in the absence of dust particles, provide plasma densities (n{sub e}) of 10{sup 17 }m{sup −3} and electron temperatures (T{sub e}) ranging from 2 to 4 eV. Dusty plasmas are made by adding 30 nm radius aluminum oxide (Al{sub 2}O{sub 3}) particles into the helium plasma. The density of the dust particle (n{sub d}) in the device is about 10{sup 11}–10{sup 12 }m{sup −3}. The propagation characteristics of electromagnetic waves are determined by a vector network analyzer with 4–6 GHz antennas. An apparent attenuation by the dust is observed, and the measured attenuation data are approximately in accordance with the theoretical calculations. The effects of gas pressure and input power on the propagation are also investigated. Results show that the transmission attenuation increases with the gas pressure and input power, the charged dust particles play a significant role in the microwave attenuation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data

The design space for inertial confinement fusion (ICF) experiments is vast, and experiments are extremely expensive. Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. However, ICF multiphysics codes must make simplifying assumptions, and thus deviate from experimental measurements for complex implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. In this work, we describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model. This method leverages a machine learning technique called “transfer learning,” the process of taking a model trained to solve one task, and partially retraining it on a sparse dataset to solve a different, but related task. In the context of ICF design, neural network models are trained on large simulation databases and partially retrained on experimental data, producing models that are far more accurate than simulations alone. Here, we demonstrate improved model performance for a range of ICF experiments at the National Ignition Facility and predict the outcome of recent experiments with less than 10% error for several key observables. We discuss how the methods might be used to carry out a data-driven experimental campaign to optimize performance, illustrating the key product—models that become increasingly accurate as data are acquired.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Spectral signatures of penumbral transients

In this work we investigate the properties of penumbral transients observed in the upper photospheric and chromospheric region above a sunspot penumbra using two-dimensional spectroscopic observations of the Ca II 854.21 nm line with a 5 s cadence. In our 30 minutes of observations, we identify several penumbral-micro jets (PMJs) with cotemporal observations from Dunn Solar Telescope/IBIS and Hinode/SOT. We find that the line profiles of these PMJ events show emission in the two wings of the line (±0.05 nm), but little modification of the line core. These are reminiscent of the line profiles of Ellerman bombs observed in plage and network regions. Furthermore, we find evidence that some PMJ events have a precursor phase starting 1 minute prior to the main brightening that might indicate initial heating of the plasma prior to an acoustic or bow shock event. With the IBIS data, we also find several other types of transient brightenings with timescales of less than 1 minute that are not clearly seen in the Hinode/SOT data. The spectral profiles and other characteristics of these events are significantly different from those of PMJs. The different appearances of all these transients are an indicator of the general complexity of the chromospheric magnetic field and underscore the highly dynamic behavior above sunspots. It also highlights the care that is needed in interpreting broadband filter images of chromospheric lines, which may conceal very different spectral profiles, and the underlying physical mechanisms at work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Deep learning based x-ray spectrometer for high repetition rate characterization of betatron radiation

Betatron radiation produced from a laser-wakefield accelerator is a broadband, hard x-ray (>1 keV) source that has been used in a variety of applications in medicine, engineering, and fundamental science. Further development and optimization of stable, high repetition rate (HRR) (>1 Hz) betatron sources will provide a means to extend their application base to include single-shot dynamical measurements of ultrafast processes or dense materials. Recent advances in laser technology used in such experiments have enabled increases in shot-rate and system stability, providing improved statistical analysis and detailed parameter scans. However, unique challenges exist at high repetition rate, where data throughput and source optimization are now limited by diagnostic acquisition rates and analysis. Here, we present the development of a machine-learning algorithm for the real-time analysis of betatron radiation. We report on the fielding of this deep learning algorithm for online source characterization at the Institut National de la Recherche Scientifique's Advanced Laser Light Source. By fine-tuning an algorithm originally trained on a fully synthetic dataset using a subset of experimental data, the algorithm can predict the betatron critical energy with a percent error of 7.2 % with a reconstruction time of 1.5 ms, providing a valuable tool for real-time, multi-objective optimization at HRR.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

The global nature of the ITER project along with its projected ~ petabyte per day data generation presents a unique challenge, but also an opportunity for the fusion community to rethink, optimize, and enhance our scientific discovery process. Recognizing this, collaborative research with computational scientists was undertaken over the past several years to create a framework for large-scale data movement across wide-area networks (WANs), to enable global near-real time analysis of fusion data. This would broaden the available computational resources for analysis/simulation, and increase the number of researchers actively participating in experiments. An official demonstration of this framework for fast, large data transfer and real-time analysis was carried out between the KSTAR tokamak in Daejeon, Korea and PPPL in Princeton, USA. Streaming large data transfer, with near real-time movie creation and analysis of the KSTAR Electron Cyclotron Emission imaging (ECEI) data, was performed using the I/O framework ADIOS, and comparisons made at PPPL with simulation results from the XGC1 code. These demonstrations were made possible utilizing an optimized network configuration at PPPL, which achieved over 8.8 Gbps (88% utilization) in throughput tests from NFRI to PPPL. This demonstration showed the feasibility for large-scale data analysis of KSTAR data, and provides a nascent framework to enable use of globally distributed computational and personnel resources in pursuit of scientific knowledge from the ITER experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Transfer learning as a method to reproduce high-fidelity non-local thermodynamic equilibrium opacities in simulations

Simulations of high-energy density physics often need non-local thermodynamic equilibrium opacity data. These data, however, are expensive to produce at relatively low fidelity. It is even more so at high fidelity such that the opacity calculations can contribute 95 % of the total computation time. This proportion can even reach large proportions. Neural networks can be used to replace the standard calculations of low-fidelity data, and the neural networks can be trained to reproduce artificial, high-fidelity opacity spectra. In this work, it is demonstrated that a novel neural network architecture trained to reproduce high-fidelity krypton spectra through transfer learning can be used in simulations. Further, it is demonstrated that this can be done while achieving a relative per cent error of the peak radiative temperature of the hohlraum of approximately 1 % to 4 % while achieving a 19.4 $\times$ speed up.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Accelerated version of NUBEAM capabilities in DIII-D using neural networks

A neural network model of the effects of neutral beam injection on DIII-D has been developed. The training and testing data used by the model have been generated by the NUBEAM module of TRANSP for experimental discharges from the 2018 DIII-D campaign. Using a principle component analysis to reduce the dimensionality of profile data, the model has been shown to reproduce the results of the Monte Carlo code NUBEAM with a high level of accuracy and an execution time orders of magnitude faster than the execution time of NUBEAM. Furthermore, this makes the neural network model uniquely suited to applications in model-based scenario planning (off-line) and active control (on-line), where a large number of simulation runs are required by the associated optimization tasks that need to be performed before and during the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A dimensionality reduction algorithm for mapping tokamak operational regimes using a variational autoencoder (VAE) neural network

Abstract A variational autoencoder (VAE) is a type of unsupervised neural network which is able to learn meaningful data representations in a reduced dimensional space. We present an application of VAE in identifying the operational stability boundary of tokamak plasma discharges. This model was implemented using a dataset of over 3000 discharges from the high beta tokamak-extended pulse (HBT-EP) device. We found the VAE model to be capable of forming a continuous low-dimensional operational space map and identifying the operational boundaries using a specified warning time window. By projecting the operational parameters onto the same reduced space, this provides an intuitive way for the machine operator or an automated control system to perform disruption avoidance using a relevant control actuator as a discharge approaches a boundary. Pre-programmed GPU control experiments were conducted to demonstrate this control technique using HBT-EP’s saddle control coils as a horizontal position actuator, showing the ability to avoid the oncoming disruptive event and extend the duration of the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2013

Observations of Global and Regional Ionospheric Irregularities and Scintillation Using GNSS Tracking Networks

The rate of TEC index (ROTI) is a measurement that characterizes ionospheric irregularities. It can be obtained from standard GNSS dual-frequency phase data collected using a geodetic type of GNSS receiver. By processing GPS data from ground-based networks of International GNSS Service and Continuously Operating Reference Station (CORS), ROTI maps have been produced to observe global and regional scintillation activities. A major mid-latitude scintillation event in the contiguous United States is reported here that was captured in ROTI maps produced using CORS GPS data collected during a space weather storm. The analyses conducted in this work and previously by another group indicate that ROTI is a good occurrence indicator of both amplitude and phase scintillations of GPS L-band signals, even though the magnitudes of ROTI, S4, and sigma(sub phi) can be different. For example, our analysis indicates that prominent ROTI and the L1 phase scintillation (sigma(sub phi)) are well correlated temporally in the polar region while L1 amplitude scintillation rarely occurs. The differences are partially attributed to physics processes in different latitude regions, such as high-speed plasma convection in the polar region that can suppress the amplitude scintillation. An analysis of the impact of ionospheric scintillation on precise positioning, which requires use of dual-frequency phase data, is also conducted. The results indicate that significant (more than an order of magnitude) positioning errors can occur under phase scintillation conditions.

ionospheric measurements↗
DOE OSTI2021

Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science

We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science issue that must be resolved for advanced tokamak plasmas such as the $25B burning plasma international thermonuclear experimental reactor (ITER) experiment. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite, which delivered cross-machine disruption predictions with unprecedented accuracy using a large database of experimental signals from two major tokamaks. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the "temporal convolutional neural network (TCN)" architecture to the time-dependent input signals. Furthermore, this allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of high-performance computing resources for hyperparameter tuning. At the same time, the TCN-based architecture achieves better predictive performance when compared with the LSTM architecture for various tasks for a representative fusion database.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Bridging the gap: Deploying AI-based Models in Real-Time Fusion Plasma Control Systems

Achieving reliable real-time control in fusion plasma experiments requires strict timing guarantees across entire control algorithms. In earlier work by Abbate et al. (2023), we demonstrated the feasibility of neural-network-based control algorithms on the DIII-D tokamak using the internally developed open-source Keras2C library for model conversion into C (Conlin et al. (2021)). However, the initial implementations relied on data buffering and branching logic outside the neural network code, causing variability in execution times. Subsequent deployments on DIII-D and KSTAR—including the RTCAKENN algorithm for kinetic profile reconstruction—proved that minimizing branching and buffering throughout the pipeline yields consistent millisecond-level cycle times under real experimental conditions (Shousha et al. (2023)). However, keeping pace with rapidly evolving AI frameworks (e.g. PyTorch) is challenging. Finally, we, therefore, propose a community-driven open-source effort to expand the tool, enabling real-time deployment across diverse systems that require strictly bounded execution times.

AI-based models↗
DOE OSTI2025

Spatiotemporal forecasting of the edge localized modes in tokamak plasmas using neural networks

Artificial intelligence techniques have been increasingly adopted by the plasma and fusion science to address problems like plasma reconstruction, surrogate modeling, and tokamak/stellarator optimization. A key focus in sustained fusion research is the prediction and mitigation of edge-localized-modes (ELMs), instabilities that occur in short, periodic bursts and can cause erosion to the tokamak vessel wall. Recent research has demonstrated the power of neural networks in approximating continuous functions. In this work, we build spatiotemporal forecasting models that can predict the onset of ELMs and their evolution at early stages. We leverage recent advances in generative modeling, sequence-to-sequence modeling, and Fourier neural operators to propose architectures and training strategies that can learn to forecast short to long term dynamics of the noisy signals due to ELMs. We benchmark the developed model against a state-of-the-art foundation model using the beam emission spectroscopy (BES) data that captures the plasma fluctuations due to ELMs over a 8 x 8 spatial grid. Our models demonstrate high accuracy, outperforming the baselines, in predicting the evolution of BES signals during ELM events. Furthermore, the developed models exhibit high accuracy in predicting the rapid rise and relaxation of the signals due to ELMs within 30–80 µs.

edge localized modes↗
DOE OSTI2023

Iterative sampling of expensive simulations for faster deep surrogate training

Deep neural network (DNN) surrogates of expensive physics simulations are enabling a rapid change in the way that common experimental design and analysis tasks are approached. Surrogate models allow simulations to be performed in parallel and separately from downstream tasks, thereby enabling analyses that would be impossible with the simulation in-the-loop; surrogates based on DNNs can effectively emulate diverse non-scalar data of the types collected in fusion and laboratory-astrophysics experiments. The challenge is in training the surrogate model, for which large ensembles of physics simulations must be run, preferably without wasting computational effort on uninteresting simulations. Here, in this paper, we present an iterative sampling scheme that can preferentially propose simulations in interesting regions of parameter space without neglecting unexplored regions, allowing high-quality and wide-ranging surrogate models to be trained using 2–3 times fewer simulations compare to space-filling designs. Our approach uses an explicit importance function defined on the simulation output space, balanced against a measure of simulation density which serves as a proxy for surrogate accuracy. It is easy to implement and can be tuned to find interesting simulations early in the study, allowing surrogates to be trained quickly and refined as new simulations become available; this represents an important step towards the routine generation of deep surrogate models quickly enough to be truly relevant to experimental work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗