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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Numerical simulation study of impurity B transport during real-time B powder injection in EAST

To understand the beneficial effects of impurity boron (B) on the plasma, the transport of B species in both atomic and ionic states in the edge plasma and the ensuing evolution of the background plasma are simulated during real-time B powder injection by using two-dimensional fluid code SOLPS-ITER. The initial background plasma profiles for the simulation are reconstructed based on an EAST upper-single null discharge (shot No. 70601). With the injection rate of B atoms, 4.0 × 10 21 atoms/s, the simulation shows that effective charge number is less than 1.1 at the inner boundary R - R sep = -5.2 cm (background plasma density, 5.1 × 10 19 m -3 ) at the outer mid-plane indicating that the B impurity does not have much influence on the energy confinement. The present work finds that the impurity B exhibits very specific features in the edge plasma, mostly present in the narrow region just outside the separatrix. The particular distribution pattern can reduce the heat flux greatly, leading to the divertor detachment. The divertor detachment can be achieved easily under an injection rate of B powder in a reasonable range. This study suggests that the impurity B has a promising potential application in future tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Sensitivity of synchrotron radiation to the superthermal electron population in mildly relativistic plasma

Synchrotron radiation has markedly different behavior in ~10 keV and in ~100 keV plasma. We show that high-energy electrons that occupy the tail of velocity distribution function have disproportionate impact on power loss of ~100 keV plasma. If electrons with energy more than cutoff energy are redistributed while keeping the Maxwellian distribution function below cutoff energy intact, both emission and absorption of synchrotron radiation act to decrease the lost power. Furthermore, these novel radiation transport effects in non-equilibrium plasma suggest large utility in the deconfinement of high-energy electrons to reduce synchrotron radiation in applications where the radiation is deleterious.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Prediction of electron density and pressure profile shapes on NSTX-U using neural networks

A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U. By projecting profiles onto empirically derived basis functions, the model is able to efficiently and accurately reproduce profile shapes. In order to project the performance of the model to upcoming NSTX-U operations, a large database of profiles from the operation of NSTX is used to test performance as a function of available data. The rapid execution time of the model is well suited to the planned applications, including optimization during scenario development activities, and real-time plasma control. Finally, a potential application of the model to real-time profile estimation is demonstrated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Gaussian Mixture Model Solvers for the Boltzmann Equation

This report documents our experience constructing a numerical method for the collisional Boltzmann equation that is capable of accurately capturing the collisionless through strongly collisional limits. We explore three different functional representations and present a detailed account of a numerical method based on a spatially dependent Gaussian mixture model (GMM). The Kullback-Leibler divergence is used as a closeness measure and various expectation maximization (EM) solution algorithms are implemented to find a compact representation in velocity space for distribution functions that exhibit significant non-Maxwellian character. We discuss issues that appear with this representation over a range of Knudsen numbers for a prototypical test problem and demonstrate that the strongly collisional limit recovers a solution to Euler's equations. Looking forward, this approach is broadly applicable to the non-relativistic and relativistic collisional Vlasov equations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Collinear expansion for color singlet cross sections

We demonstrate how to efficiently expand cross sections for color-singlet production at hadron colliders around the kinematic limit of all final state radiation being collinear to one of the incoming hadrons. This expansion is systematically improvable and applicable to a large class of physical observables. We demonstrate the viability of this technique by obtaining the first two terms in the collinear expansion of the rapidity distribution of the gluon fusion Higgs boson production cross section at next-to-next-to leading order (NNLO) in QCD perturbation theory. Furthermore, we illustrate how this technique is used to extract universal building blocks of scattering cross section like the N-jettiness and transverse momentum beam function at NNLO.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Laser-based ultrasound interrogation of surface and sub-surface features in advanced manufacturing materials

Abstract Structures formed by advanced manufacturing methods increasingly require nondestructive characterization to enable efficient fabrication and to ensure performance targets are met. This is especially important for aerospace, military, and high precision applications. Surface acoustic waves (SAW) generated by laser-based ultrasound can detect surface and sub-surface defects relevant for a broad range of advanced manufacturing processes, including laser powder bed fusion (LPBF). In particular, an all-optical SAW generation and detection configuration can effectively interrogate laser melt lines. Here we report on scattered acoustic energy from melt lines, voids, and surface features. Sub-surface voids are also characterized using X-ray Computed Tomography (CT). High resolution CT results are presented and compared with SAW measurements. Finite difference simulations inform experimental measurements and analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards Geospatial Knowledge Graph Infused Neuro-Symbolic AI for Remote Sensing Scene Understanding

Deep learning has proven its effectiveness in numerous tasks for remote sensing scene understanding. However there is an increasing interest to explore fusion of domain-specific background information to the deep neural network to further improve its performance. Remote sensing researchers are also working towards developing models that generalize and adapt to multiple applications. Generalization challenges coupled with the scarcity of large corpora of high-quality noise-free labelled data, have together fueled an interest for leveraging background information. Knowledge graphs serve as excellent choice to represent domain-specific information in a structured, standardized and extensible manner. Integrating symbolic knowledge representations in the form of Knowledge Graph Embedding (KGE) to perform neuro-symbolic reasoning is an emerging research direction promising significant impacts. This vision paper seeks to position ideas and provoke early thoughts toward advancing neuro-symbolic artificial intelligence in the context of geospatial challenges. Specifically, it conceptualizes and elaborates on an architecture for infusing geospatial knowledge from knowledge graph in a deep neural network pipeline. As guiding case studies - land-use land-cover classification, object detection and instance segmentation can benefit from infusing spatio-contextual information with remote sensing imagery. The discussion further reflects on and articulates the challenges and explainable AI opportunities anticipated when scaling and maintaining large-scale geospatial knowledge graphs.

Potnis, Abhishek↗
DOE OSTI2021

Type-I ELM mitigation by continuous lithium granule gravitational injection into the upper tungsten divertor in EAST

Large edge-localized modes (ELMs) were mitigated by gravitational injection of lithium granules into the upper X-point region of the EAST device with tungsten plasma-facing components. The maximum ELM size was reduced by ~ 70% in high βN H-mode plasmas. Large ELM stabilization was sustained for up to about 40 energy confinement times, with constant core radiated power and no evidence of high-Z or low-Z impurity accumulation. The lithium granules injection reduced the edge plasma pedestal density and temperature and their gradients, due to increased edge radiation and reduced recycling from the plasma-facing components. Ideal stability calculations using the ELITE code indicate that the stabilization of large ELMs correlates with improved stability of intermediate-n peeling-ballooning modes, due to reduced edge current resulting from the profile changes. The pedestal pressure reduction was partially offset by a core density increase, which resulted in a modest ~ 7% drop in core stored energy and normalized energy confinement time. We surmise that the remnant small ELMs are triggered by the penetration of multiple Li granules just past the separatrix, similar to small ELMs triggered by deuterium pellet [S. Futatani et al., Nucl. Fusion 54 (2014) 073008]. This study extends previous ELM elimination with Li powder injection [R. Maingi et al., Nucl. Fusion 58 (2018) 024003] in EAST because 1) use of small, dust-like powder and the related potential health hazards were eliminated, and 2) use of macroscopic granules should be more applicable to future devices, due to deeper penetration than dust particles, e.g. inside the separatrix with velocities ~ 10 m/s in EAST.

EAST↗
DOE OSTI2023

Flash Neutron Imaging [Poster]

The NNSS has extensive historical experience with high-fidelity flash neutron imaging which may be applicable to the needs of NA-22. In recent neutron imaging tests, a dense plasma focus (DPF) discharges a capacitor bank across a hydrogen-filled diode to create a flash of fusion neutrons emitted from a point, which can be used for active interrogation. Object inspection may include both radiography transmission imaging as well as emission imaging of the subject via an aperture. A scintillator screen (coupled to a low-Z converter when necessary) converts deposited energy to visible light, which is relayed through optics to an ultra-sensitive visible imager contained in a light-tight box. Flash photon imaging, utilizing bremsstrahlung emission instead of fusion neutrons, is also available and is more mature.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Self-radiography of imploded shells on OMEGA based on additive-free multi-monochromatic continuum spectral analysis

Radiographs of pure-DT cryogenic imploding shells provide critical validation of progress toward ignition-scalable performance of inertial confinement fusion implosions. Cryogenic implosions on the OMEGA Laser System can be self-radiographed by their own core spectral emission near ≈2 keV. Utilizing the distinct spectral dependences of continuum emissivity and opacity, the projected optical-thickness distribution of imploded shells, i.e., the shell radiograph, can be distinguished from the structure of the core emission distribution in images.Importantly, this can be done without relying on spectral additives (shell dopants), as in previous applications of implosion self-radiography. Furthermore, demonstrations with simulated data show that this technique is remarkably well-suited to cryogenic implosions and can also be applied to self-radiography of imploded room-temperature CH shells at higher spectral energy (hv ≈ 3–5 keV) based on the very similar continuum spectrum of carbon. Experimental demonstration of additive-free self-radiography with warm CH shell implosions on OMEGA will provide an important proof of principle for future applications to cryogenic DT implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Primary yields of protons measured using CR-39 in laser-induced deuteron–deuteron fusion reactions

The deuteron and deuteron (DD) fusion reactions is the most crucial reaction in the nuclear astrophysics and inertial confinement fusion, which is responsible for synthesizing chemical elements, and for powering the stellar evolution. The plasma environment generated by the high-intensity laser used in a DD fusion reaction is similar to the early stage of the Big Bang. Therefore, it is important to study laser-induced DD fusion reactions. Recently, we experimentally investigated such reactions using eight laser beams with the third harmonic impact on a deuterated polyethylene target at the ShenGuang-II Upgrade facility. In this work, we focused on the application of range-filter spectrometers assembled from a 70 μm aluminum filter and two CR-39 nuclear track detectors to measure the yields of primary DD protons. Based on the track diameter calibration results of 3 MeV protons used to diagnose the tracks on the range- filter spectrometers, we measured a primary DD proton yield of approximately (8:52 ± 1:7) × 10 6 , consistent with the yields from similar laser facilities worldwide, indicating that range-filter spectrometry is an effective way to measure primary DD protons. Furthermore, using other accurate detectors may help to measure the rare events of D 3 He protons.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

In this paper we discuss recent advances in deep convolutional neural networks (CNN) for sequence learning, which allow identifying long-range, multi-scale phenomena in long sequences, such as those found in fusion plasmas. We point out several benefits of these deep CNN architectures, such as not requiring experts such as physicists to hand-craft input data features, the ability to capture longer range dependencies compared to the more common sequence neural networks (recurrent neural networks like long short-term memory (LSTM) networks), and the comparative computational efficiency. We apply this neural network architecture to the popular problem of disruption prediction in fusion energy tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. Initial results trained on a large ECEi dataset show promise, achieving an F 1 -score of ~91% on individual time-slices using only the ECEi data. This indicates the ECEi diagnostic by itself can be sensitive to a number of pre-disruption markers useful for predicting disruptions on timescales not only for mitigation but also avoidance. Future opportunities for utilizing these deep CNN architectures with fusion data are outlined, including impact of recent upgrades to the ECEi diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Automated ICRF heating surrogate modeling via machine learning

This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.

Sanchez-Villar, Alvaro [Princeton Plasma Physics L↗
DOE OSTI2024

Vertical instability forecasting and controllability assessment of multi-device tokamak plasmas in DECAF with data-driven optimization

Abstract Reliable vertical position control will be an essential element of any future tokamak-based fusion power plant in order to reduce disruptions and maximize performance. We investigate methods to improve vertical controllability boundary determination in plasma operational space and demonstrate a data-driven approach based on direct pseudoinversion of operational space data that is rigorously quantitative, applicable in real-time plasma control systems, and physically intuitive to interpret. Applied to historical shot data from entire run campaigns on the MAST-U, KSTAR, and NSTX tokamaks, this approach, implemented in DECAF, improves vertical displacement event identification accuracy to 98.9%–100%. Further, we explore the application of a physics-based vertical stability metric as an early warning forecaster for vertical displacement events. The development of a linear surrogate model for the plasma current density profile, with a coefficient of determination of 0.992 on the training dataset, enables potential employment of this forecaster in real-time. The application of this approach on historical data from the MAST-U MU02 campaign yields a forecaster with 62.6% accuracy, indicating promise for this method when further refined and potentially coupled with other stability metrics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

What Machine Learning Can and Cannot Do for Inertial Confinement Fusion

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Study of the Effect of Reflections on High-Power, 110-GHz Pulsed Gyrotron Operation

The effect of reflection is studied experimentally and theoretically on a high-power 110-GHz gyrotron operating in the TE 22,6 mode in 3 μs pulses at 96 kV, 40 A. The experimental setup allows variation of the reflected power from 0 to 33% over a range of gyrotron operating conditions. The phase of the reflection is varied by translating the reflector along the axis. Operating at a higher efficiency point, at 4.40 T with 940kW of output power, reflected power exceeding 11% causes a switch from operation in the TE 22,6 to simultaneous operation in the TE 22,6 and TE 21,6 modes with a large decrease of the total gyrotron output power. This switching effect is in good agreement with simulations using the MAGY code. Operating at a more stable point, 4.44 T with 580kW of output power, when the reflection is increased, the output power remains in the TE 22,6 mode but it decreases monotonically with increasing reflection, dropping to 200kW at 33% reflection. Furthermore, at a reflection above 22%, a power modulation at 25 to 30MHz is observed, independent of the phase of the reflected wave. Such a modulated signal may be useful in spectroscopic and other applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Fast modulating electron cyclotron emission (FMECE) diagnostic for tokamaks

Utilizing variable-frequency channels, e.g., yttrium iron garnet (YIG) bandpass filters, in the intermediate frequency (IF) section of an electron cyclotron emission (ECE) radiometer facilitates flexibility in the volume viewed by the ECE channels as well as high resolution electron temperature and temperature fluctuation measurements in tokamaks. Fast modulating electron cyclotron emission (FMECE), a stand-alone IF section with eight channels, is a novel application of YIG filters for real-time electron temperature gradient and gradient scale length measurements. Key to FMECE is a simultaneous input/output data acquisition unit, as well as a modified type of YIG filters, which is capable of fast switching of their center (set) frequencies with a frequency slew rate of 600 μs/GHz. A new FMECE has been implemented and tested on the DIII-D tokamak, demonstrating its capability in real-time gradient measurements. Here, the data presented here shows that FMECE can identify flattening in the electron temperature profile; the latter can be used as a sensor for real time monitoring and control of plasma instabilities. Implementation and application are planned for the EAST tokamak.

47 OTHER INSTRUMENTATION↗