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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Internal rotation of ELM filaments on NSTX

Edge localized modes (ELMs) are a threat to tokamaks due to their high heat and particle loads on plasma facing components. A significant portion of this energy is carried and deposited by the emerging ELM filaments, whose dynamics are directly connected to their impact. Therefore, understanding their underlying physics is important for the operation of future fusion reactors. Our paper extends our knowledge of ELM filaments by reporting on their internal rotation (spinning) around the magnetic field lines along which they are extended. Our analysis of gas-puff imaging data on National Spherical Torus Experiment shows that ELM filaments are characterized by internal rotation in the direction of the ion-gyromotion with ω = 15:2 krad/s median angular velocity, which is approximately three times faster than the blob rotation in the background turbulence. The characteristic size of the ELM filament was also assessed and found to be similar to the blobs. A nearly linear trend was found between the angular velocity and the radial velocity of the ELM filament. The angular velocity was found to be linearly dependent on the distance of the filament from the separatrix, as well. An analytical model called the shear-induced rotation model was identified as a candidate for explaining the physics of the observations. Our results show that the modeled mechanism could significantly influence the rotation of the ELM filament; however, it cannot be a sole contributor.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Optimization and active stabilization of a far-infrared laser for NSTX-U high poloidal wavenumber scattering diagnostics

The far-infrared (FIR) laser output beam power and profile are important parameters in the laser-aided diagnostics, directly influencing the spatial resolution and signal-to-noise ratio of measurements. Here, this work focuses on developing a systematic control method to enhance FIR laser beam quality through optimized mirror alignment and real-time feedback-based precision cavity length tuning. A 150 W CO 2 laser, aligned with the waveguide axis using a HeNe reference laser, serves as the pump source. The sensitivity of FIR beam intensity to pump gas pressure and thermal expansion is investigated, revealing that even a 1 µm cavity expansion can significantly degrade output power stability to about two-thirds of its original value. To address this, a feedback control module has been designed and implemented for active cavity length adjustment, stabilizing the output power at ∼30 mW. In addition, maintaining a high formic acid gas pressure ($>$190 mTorr) within the cavity ensures reliable operation. The optimized FIR laser will be deployed on the National Spherical Torus eXperiment-U high poloidal wavenumber scattering system for studying electron-scale turbulence in tokamak plasmas.

Xu, Xinhang [Univ. of California, Davis, CA (Unite↗
DOE OSTI2022

Neural net modeling of equilibria in NSTX-U

Neural networks (NNs) offer a path towards synthesizing and interpreting data on faster timescales than traditional physics-informed computational models. In this work we develop two NNs relevant to equilibrium and shape control modeling, which are part of a suite of tools being developed for the National Spherical Torus Experiment-Upgrade for fast prediction, optimization, and visualization of plasma scenarios. The networks include Eqnet, a free-boundary equilibrium solver trained on the EFIT01 (Equilibrium FITtting 01) reconstruction algorithm, and Pertnet, which is trained on the Gspert code and predicts the non-rigid plasma response, a nonlinear term that arises in shape control modeling. The NNs are trained with different combinations of inputs and outputs in order to offer flexibility in use cases. In particular, Eqnet can use magnetic diagnostics as inputs and act as an EFIT-like reconstruction algorithm, or, by using pressure and current profile information the NN can act as a forward Grad–Shafranov equilibrium solver. This forward-mode version is envisioned to be implemented in the suite of tools for simulation of plasma scenarios. The reconstruction-mode version gives some performance improvements compared to the online reconstruction code real-time EFIT, especially when vessel eddy currents are significant. Here, we report strong performance for all NNs indicating that the models could reliably be used within closed-loop simulations or other applications. Some limitations are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX

Abrupt large events in the Alfvenic and sub-Alfvenic frequency bands in tokamaks are typically correlated with increased fast ion loss. Here, machine learning is used to speed up the laborious process of characterizing the behaviour of magnetic perturbations from corresponding frequency spectrograms that are typically identified by humans. Analysis allows for comparison between different mode character (such as quiescent, fixedfrequency, chirping, avalanching) and plasma parameters obtained from the TRANSP code such as the ratio of the neutral beam injection (NBI) velocity and the Alfven velocity (v inj. /v A ), the q-profile, and the ratio of the neutral beam beta and the total plasma beta (β beam,i /β). In agreement with previous work by Fredrickson et al., in this work, we find correlation between β beam,i and mode character. In addition, previously unknown correlations are found between moments of the spectrograms and mode character. Character transition from quiescent to non-quiescent behaviour for magnetic fluctuations in the 50 - 200 kHz frequency band is observed along the boundary v Φ ≲ (1/4) (v inj. - 3v A ) where v Φ is the rotation

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX

Abrupt large events in the Alfvenic and sub-Alfvenic frequency bands in tokamaks are typically correlated with increased fast-ion loss. Here, machine learning is used to speed up the laborious process of characterizing the behavior of magnetic perturbations from corresponding frequency spectrograms that are typically identified by humans. The analysis allows for comparison between different mode character (such as quiescent, fixed frequency, and chirping, avalanching) and plasma parameters obtained from the TRANSP code, such as the ratio of the neutral beam injection (NBI) velocity and the Alfven velocity (v_inj./v_A), the q-profile, and the ratio of the neutral beam beta and the total plasma beta (beta_beam,i / beta). In agreement with the previous work by Fredrickson et al., we find a correlation between beta_beam,i and mode character. In addition, previously unknown correlations are found between moments of the spectrograms and mode character. Character transition from quiescent to nonquiescent behavior for magnetic fluctuations in the 50200-kHz frequency band is observed along the boundary v_phi ~ (1/4)(v_inj. - 3v_A), where v_phi is the rotation velocity.

machine learning↗
DOE OSTI2020

Deciphering the PMI Surface Chemistry of Lithium-Based PFCs and its Effects on High Performance Plasmas in NSTX

The primary aims or objectives of the proposed work here focus on the dynamic measurement (Illinois PI Allain) and atomistic-based multi-scale computational atomistic simulations (UTK PI: Wirth) of: 1) lithium coatings with re-deposited hydrogenated carbon surfaces and high-Z substrates, 2) lithium coatings on boronized ATJ graphite surfaces and high-Z substrates and 3) lithium coatings on ATJ graphite and high-Z substrates with variation in temperature. Three main primary tasks are proposed to address each of the primary objectives listed above: 1) dynamic in-situ irradiation with D+, He+ species of lithium coatings with variation in interface substrate morphology (e.g. smooth vs rough, fuzz vs nanostructured); 2) validation and coupling with multi-scale computational simulations connecting the irradiated surface to the plasma edge; 3) extrapolation to long-pulse conditions (e.g. function of flux vs fluence studies). In primary objective #3, temperature-based effects will be studied to evaluate three primary mechanisms: a) D uptake and recycling, b) erosion, and c) surface melting and evaporation as a function of substrate morphology and chemistry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗