Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ELMs”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Pellet triggering of edge localized modes in low collisionality pedestals at DIII-D

Edge localized modes (ELMs) are triggered using deuterium pellets injected into plasmas with ITER-relevant low collisionality pedestals, and the resulting peak ELM energy fluence is reduced by approximately 25%–50% relative to natural ELMs destabilized at similar pedestal pressures. Cryogenically frozen deuterium pellets are injected from the low-field side of the DIII-D tokamak at frequencies lower than the natural ELM frequency, and heat flux is measured by infrared cameras. Ideal MHD pedestal stability calculations show that without pellet injection, these low collisionality pedestals were limited by their current density (peeling-limited) rather than their pressure gradient (ballooning-limited). ELM triggering success correlates strongly with pellet mass, consistent with the theory that a large pressure perturbation is required to trigger an ELM in low collisionality discharges that are far from the ballooning stability boundary. For sufficiently large pellets, both instantaneous and time-integrated ELM energy deposition measured by infrared cameras is reduced with respect to naturally occurring ELMs at the inner strike point, which is the position where it is largest for natural ELMs. Energy fluence at the outer strike point is less effected. Cameras observing both heat flux and D-alpha emission often find significant toroidally asymmetric striations in the outboard far scrape-off layer resulting from ELMs that are triggered by pellets. Toroidal asymmetries at the inner strike point are similar between natural and pellet-triggered ELMs, indicating that the reduction in peak heat flux and total fluence at that location is robust for the conditions reported here.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Edge-localized-mode simulation in CFETR steady-state scenario

We report the EPED1 model and self-consistent core-pedestal coupling in integrated modeling are used to design the pedestal structure of the China Fusion Engineering Testing Reactor (CFETR) steady-state scenario. The key parameters, such as β p and q 95 , are based on the grassy edge-localized-mode (ELM) experimental database. In this work, we use the BOUT++ six-field two-fluid code to simulate the onset of the ELM in the CFETR steady-state scenario. The ELM size is around 0.2% in nonlinear simulations, which is in the experimental range of the grassy ELM discharges, 0.1%–1% observed in multiple tokamak devices. Linear and nonlinear simulations show that the dominant high-n ballooning modes peak around n = 40. Compared to type-I ELM crashing dynamics, grassy ELM crashing has a smaller initial crash and is then followed by three phases of turbulence spreading, which are dominated by multi-modes, a high-n mode of n = 45 and low-n mode of n = 5, respectively. In contras to type-I ELM, the perturbation of the high-n mode has a narrow width around ψ = 0.95, and magnetic island formation and reconnection occur only beyond ψ = 0.95, leading to a small initial crash. Mode–mode interaction in the multi-mode coexistence stage stops the growth of individual modes and reduces the transport of particles and heat, and these are the two reasons why the ELM size is small. In–out asymmetry of transient heat flux with a ratio of E out /E in = 3.5 is found during grassy ELM crash. The rise and delay times of the heat flux match the calculation from the free-streaming model. To evaluate the erosion of the divertor target, the energy fluence at the outer divertor target is calculated, which is 0.029 MJ m -2 , 5.5 times smaller than the tungsten melting limit 0.16 MJ m -2 . The calculated energy fluency still follows the experimental scaling law from type-I ELM experiments. The fluctuation eddies in the toroidal direction show a filament structure at the outer mid-plane. Parallel heat flux patterns with a toroidal mode number n = 10 are found at the outer divertor with an amplitude of 680 MW m -2 .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Understanding tungsten erosion during inter/intra-ELM periods in He-dominated JET-ILW plasmas

Tungsten erosion was quantified during inter/intra-ELM periods in He-dominated JET-ILW plasmas by optical emission spectroscopy. The intra-ELM tungsten sputtering in helium plasmas, which dominates the total W source, prevails by a factor of about 4 over inter-ELM sputtering in the investigated ELM frequency range from 90 Hz–120 Hz. He ions are mainly responsible for the W erosion during the ELMs in He plasmas. The strong in/out asymmetry of the ELM-induced W erosion is observed in He plasmas even at high ELM frequencies beyond 100 Hz. In Ohmic/L-mode plasmas and during the H-mode inter-ELM plasma phases both He 2+ and Be 2+ ionic species are major contributors to the W erosion. Their contribution depends on the electron temperature in the divertor: for T e > 15 eV both species cause significant W sputtering, for T e < 15 eV, B e 2+ ions are solely responsible for the W erosion. Tungsten erosion during in both inter and intra-ELM periods in He-dominated plasmas are significantly larger than in deuterium plasmas. It is 15–25 times larger during the inter-ELM phase and in L-mode discharges at T e = 25–30 eV. On the other hand, the ELM-induced W source is by a factor of 3 larger than in D plasmas.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Progress towards edge-localized mode suppression via magnetic perturbations in hydrogen plasmas

Abstract The suppression of edge-localized modes (ELMs) by applying resonant magnetic perturbations (RMPs) is well studied in low collisionality deuterium plasmas as a measure to reduce transient divertor heat loads. However, ELM suppression has yet to be demonstrated in non-nuclear fuels such as hydrogen and hydrogen + helium mixtures which are the main ion species to be used in the ITER pre-fusion power operation (PFPO) phase. For the first time, attempts have been made to access ELM suppression with RMPs in ITER-like low collisionality hydrogen plasmas at DIII-D and ASDEX Upgrade. The DIII-D experiments focused on operation with injected power slightly above the L–H power threshold similar to the expected conditions in the ITER PFPO phase with limited external heating power. The RMPs were found to trigger H–L backtransitions, which is shown to be avoided by reducing the L–H power threshold by diluting the plasma with helium. The additional helium combined with a larger measured neutral density of hydrogen inside the separatrix compared to ELM suppressed deuterium plasmas precluded access to a pedestal top density below the known RMP-ELM suppression threshold. At ASDEX Upgrade, RMP-ELM suppression has been achieved when the concentration of 1 H in the hydrogen isotope mix is below 40 % . While all known access criteria for RMP-ELM suppression were met above this threshold, full ELM suppression was replaced by strong mitigation. The most prominent difference between the hydrogen and deuterium plasmas was a change of turbulence characteristics in the pedestal where Doppler reflectometry measurements suggest a significant reduction of turbulence even at small hydrogen concentrations. In conclusion, these experiments not only identify issues that may prevent access to RMP-ELM suppression in the ITER PFPO phase, but also highlight missing physics in our current understanding of RMP-ELM suppression such as potentially the role of turbulence in the pedestal gradient region.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Evaluation of E3SM land model snow simulations over the western United States

Abstract. Seasonal snow has crucial impacts on climate, ecosystems, and humans, but it is vulnerable to global warming. The land component (ELM) of the Energy Exascale Earth System Model (E3SM) mechanistically simulates snow processes from accumulation, canopy interception, compaction, and snow aging to melt. Although high-quality field measurements, remote sensing snow products, and data assimilation products with high spatio-temporal resolution are available, there has been no systematic evaluation of the snow properties and phenology in ELM. This study comprehensively evaluates ELM snow simulations over the western United States at 0.125∘ resolution during 2001–2019 using the Snow Telemetry (SNOTEL) in situ networks, MODIS remote sensing products (i.e., MCD43 surface albedo product), the spatially and temporally complete (STC) snow-covered area and grain size (MODSCAG) and MODIS dust and radiative forcing in snow (MODDRFS) products (STC-MODSCAG/STC-MODDRFS), and the snow property inversion from remote sensing (SPIReS) product and two data assimilation products of snow water equivalent and snow depth – i.e., University of Arizona (UA) and SNOw Data Assimilation System (SNODAS). Overall the ELM simulations are consistent with the benchmarking datasets and reproduce the spatio-temporal patterns, interannual variability, and elevation gradients for different snow properties including snow cover fraction (fsno), surface albedo (αsur) over snow cover regions, snow water equivalent (SWE), and snow depth (Dsno). However, there are large biases of fsno with dense forest cover and αsur in the Rocky Mountains and Sierra Nevada in winter, compared to the MODIS products. There are large discrepancies of snow albedo, snow grain size, and light-absorbing particle-induced snow albedo reduction between ELM and the MODIS products, attributed to uncertainties in the aerosol forcing data, snow aging processes in ELM, and remote sensing retrievals. Against UA and SNODAS, ELM has a mean bias of −20.7 mm (−35.9 %) and −20.4 mm (−35.5 %), respectively, for spring, and −13.8 mm (−27.8 %) and −10.2 mm (−22.2 %), respectively, for winter. ELM shows a relatively high correlation with SNOTEL SWE, with mean correlation coefficients of 0.69 but negative mean biases of −122.7 mm. Compared to the snow phenology of STC-MODSCAG and SPIReS, ELM shows delayed snow accumulation onset dates by 17.3 and 12.4 d, earlier snow end dates by 35.5 and 26.8 d, and shorter snow durations by 52.9 and 39.5 d, respectively. This study underscores the need for diagnosing model biases and improving ELM representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High resolution density pedestal measurements during edge localized modes by short-pulse reflectometry in the TCV tokamak

Here, this publication presents high spatio-temporal resolution (mm µs -1 ) density profile measurements of the pedestal top during type I, III, and small edge localized mode (ELM) H-mode plasmas in the tokamak à configuration variable (TCV). These measurements were performed using a novel short-pulse reflectometer. Average inter-ELM density profiles are obtained via conditional averaging using the D α trace as ELM indicator. Changes to the pedestal density profile gradients prior to type-III ELMs reveal unique pedestal dynamics leading to the ELM crash which can provide important experimental data for validation of non-linear MHD ELM simulations. The small-ELM scenario is found to feature a ~25–35 kHz quasi-coherent density fluctuation near the separatrix ρ ψ 0.993–1.05 not observed during a similar type-I ELM discharge. This oscillation is also found in low-field-side magnetic pick-up probes displaying a ballooning character and n = +1 toroidal mode number. This oscillation could help explain the markedly different pedestal dynamics observed in the small-ELM regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Turbulence simulations of drift-Alfvén wave effects on edge-localized modes and divertor target heat flux width

A series of BOUT++ turbulence simulations are performed for two Pre-Fusion Power Operation (PFPO-1,2) phases of the ITER Research plan proclaimed in 2019. Linear simulation results show that PFPO-1 is unstable to Peeling-ballooning modes (PB), while the PFPO-2 is unstable to the coupling of PB and Drift-Alfvén wave (DAW) instabilities. The linear results are qualitatively consistent with the dispersion relation of DAW. To study the influence of the DAW on the edge-localized mode (ELM) crash, the PFPO-2 is adopted in this section. Different from the grassy ELM in [Li et al 2022 Nucl. Fusion 62, 096030], nonlinear simulations show that the ELM size of PFPO-2 is almost one-third of the grassy ELM, representing a distinct small ELM. However, simulations then show that if the PB instability is removed, the fluctuation amplitude drops by an order of magnitude and the ELM crash disappears, which is in accord with the theory in [Xu et al 2010 Physical Review Letters, Vol. 105, 175005] and the results in [Li et al 2022 Nucl. Fusion 62, 096030], confirming that the PB instability is a necessary condition for an ELM crash. Furthermore, removing the DAW drive also suppresses ELM crashes, implying that PB instability is necessary but insufficient for PFPO-2 ELM and that DAW could amplify PB-driven turbulence. In addition, by integrating results of both PFPO phases by BOUT++ turbulence analysis with heat flux width (λ q ) and electron thermal diffusivity (χ e ), these simulations establish a predictive framework for heat flux width and its dominant mechanisms, thereby providing a comprehensive scheme for the prediction of the heat flux width. Moreover, simulations indicate that DAW driving can increase the transport coefficient by enhancing the turbulent transport, leading to a broadened heat flux width once the transport coefficient exceeds

Drift-Alfvén wave↗
DOE OSTI2024

Using convolutional neural networks to detect edge localized modes in DIII-D from Doppler backscattering measurements

In H-mode tokamak plasmas, the plasma is sometimes ejected beyond the edge transport barrier. These events are known as edge localized modes (ELMs). ELMs cause a loss of energy and damage the vessel walls. Understanding the physics of ELMs, and by extension, how to detect and mitigate them, is an important challenge. In this paper, we focus on two diagnostic methods—deuterium-alpha (D α ) spectroscopy and Doppler backscattering (DBS). The former detects ELMs by measuring Balmer alpha emission, while the latter uses microwave radiation to probe the plasma. DBS has the advantages of having a higher temporal resolution and robustness to damage. These advantages of DBS diagnostic may be beneficial for future operational tokamaks, and thus, data processing techniques for DBS should be developed in preparation. In sight of this, we explore the training of neural networks to detect ELMs from DBS data, using D α data as the ground truth. With shots found in the DIII-D database, the model is trained to classify each time step based on the occurrence of an ELM event. The results are promising. When tested on shots similar to those used for training, the model is capable of consistently achieving a high f1-score of 0.93. Furthermore, this score is a performance metric for imbalanced datasets that ranges between 0 and 1. We evaluate the performance of our neural network on a variety of ELMs in different high confinement regimes (grassy ELM, RMP mitigated, and wide-pedestal), finding broad applicability. Beyond ELMs, our work demonstrates the wider feasibility of applying neural networks to data from DBS diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Pedestal particle balance studies in JET-ILW H-mode plasmas

JET-ILW type I ELMy H-modes at 2.5 MA/2.8 T with constant NBI heating (23 MW) and gas fuelling rate were performed, utilising edge localised mode (ELM) pacing by vertical kicks and plasma shaping (triangularity, δ) as tools to disentangle the effects of ELMs, inter-ELM transport and edge stability on the pedestal particle balance. In agreement with previous studies, the pedestal confinement improves with increasing δ, mostly due to a significant increase in pedestal density while the ELM frequency ($f_{\mathrm{ELM}}$) is decreased. Improved pedestal confinement with increasing δ was observed even when the pedestal MHD stability was degraded artificially by vertical kicks, implying that increased triangularity may favourably affect the inter-ELM pedestal recovery. The workflow developed to quantify the pedestal particle balance uses high time-resolution profile reflectometry to characterise the inter-ELM evolution of the plasma particle content ($\mathrm{d} N/\mathrm{d}t$), the NEO drift-kinetic solver to evaluate the neoclassical fluxes and interpretative EDGE2D-EIRENE simulations to estimate the edge particle source. The edge particle source is then constrained by deuterium Balmer-α line intensity measurements in the main chamber, which are, however, strongly affected by reflections from the metal walls. The reflections are accounted for by the CHERAB code taking the divertor emission (the brightest light source in the torus) distribution from imaging spectroscopy measurements as input. Our analysis shows that in the second half of the ELM cycle, the volume-integrated particle source is larger than $\mathrm{d} N/\mathrm{d}t$, indicating that transport plays a key role in the inter-ELM pedestal recovery.

fuelling↗
DOE OSTI2021

Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification

Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau

With the highest albedo of the land surface, snow plays a vital role in Earth's surface energy budget and water cycle. Snow albedo is primarily controlled by snow grain properties (e.g., size and shape) and light-absorbing particles (LAPs) such as black carbon (BC) and dust. The mixing state of LAPs in snow also has impacts on LAP-induced snow albedo reduction and surface radiative forcing (RF). However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with the snow grains. This study improves the snow radiative transfer model in the Energy Exascale Earth System Model version 2.0 (E3SM v2.0) Land Model (ELM v2.0) by considering non-spherical snow grain shapes (i.e., spheroid, hexagonal plate, and Koch snowflake) and internal mixing of dust–snow, and it systematically evaluates the impacts on the surface energy budget and water cycle over the Tibetan Plateau (TP). A series of ELM simulations with different treatments of snow grain shape, mixing state of BC–snow and dust–snow, and sub-grid topographic effects (TOP) on solar radiation are performed. Compared with two remote sensing snow products derived from the Moderate Resolution Imaging Spectroradiometer, the control ELM simulation (ELM_Control) with the default configurations of spherical snow grain shape, internal mixing of BC–snow, external mixing of dust–snow, and without TOP as well as the ELM simulation with new model features (ELM_New) can both capture the overall snow distribution reasonably. Additionally, ELM_New overall shows smaller biases in snow cover fraction than ELM_Control in spring when snowmelt is important for water management. The estimated LAP-induced RF in ELM_New ranges from 0 to 19.3 W m -2 with the area-weighted average value of 1.5 W m -2 that is comparable to the reported values in existing studies. The Koch snowflake shape, among other non-spherical shapes, shows the largest difference from the spherical shape in spring when snow processes related to the surface energy budget and water cycle have high importance. The impacts of the mixing state of LAP in snow are smaller than the shape effects and depend on snow grain shape. Compared to external mixing, internal mixing of LAP–snow can lead to larger snow albedo reduction and snowmelt, which further affect the surface energy budget and water cycle. The individual contributions of non-spherical snow shape, mixing state of LAP–snow, and local topography impacts on the snow and surface fluxes have different signs and magnitudes, and their combined effects may be negative or positive due to complex and nonlinear interactions among the factors. Overall, the changes in net solar radiation in spring due to individual and combined effects range from -28.6 to 16.9 W m -2 and -29.7 to 12.2 W m -2 , respectively. This study advances understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offers guidance for improving snow simulations and RF estimates in Earth system models under climate change.

58 GEOSCIENCES↗
DOE OSTI2022

High resolution density pedestal measurements during edge localized modes by short-pulse reflectometry in the TCV tokamak

This publication presents high spatio-temporal resolution (mm/μs) density profile measurements of the pedestal top during type I, III, and small edge localized mode (ELM) H-mode plasmas in the Tokamak à Configuration Variable (TCV). These measurements were performed using a novel short-pulse reflectometer. Average inter-ELM density profiles are obtained via conditional averaging using the Dα trace as ELM indicator. Changes to the pedestal density profile gradients prior to type-III ELMs reveal unique pedestal dynamics leading to the ELM crash which can provide important experimental data for validation of non-linear MHD ELM simulations. The small-ELM scenario is found to feature a ∼25-35 kHz quasi-coherent density fluctuation near the separatrix rho_psi ∼0.993-1.05 not observed during a similar type-I ELM discharge. This oscillation is also found in low-field-side magnetic pick- up probes displaying a ballooning character and n=+1 toroidal mode number. This oscillation could help explain the markedly different pedestal dynamics observed in the small-ELM regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Projections of H-mode access and edge pedestal in the SPARC tokamak

In order to inform core performance projections and divertor design, the baseline SPARC tokamak plasma discharge is evaluated for its expected H-mode access, pedestal pressure and edge-localized mode (ELM) characteristics. A clear window for H-mode access is predicted for full field DT plasmas, with the available 25 MW of design auxiliary power. Additional alpha heating is likely needed for H-mode sustainment. Pressure pedestal predictions in the developed H-mode are surveyed using the EPED model. The projected SPARC pedestal would be limited dominantly by peeling modes and may achieve pressures in excess of 0.3 MPa at a density of approximately 3 × 10 20 m -3 . High pedestal pressure is partially enabled by strong equilibrium shaping, which has been increased as part of recent design iterations. Edge-localized modes (ELMs) with >1 MJ of energy are projected, and approaches for reducing the ELM size, and thus the peak energy fluence to divertor surfaces, are under consideration. The high pedestal predicted for SPARC provides ample margin to satisfy its high fusion gain (Q) mission, so that even if ELM mitigation techniques result in a 2x reduction of the pedestal pressure, Q > 2 is still predicted.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Impacts of Topography-Based Subgrid Scheme and Downscaling of Atmospheric Forcing on Modeling Land Surface Processes in the Conterminous US

The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). In this study, a new topography-based subgrid structure referred to as topographic units (TGU) designed to better capture subgrid topographic effects, and methods to downscale atmospheric forcing to the land TGUs have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Effects of the subgrid scheme and downscaling methods on ELM simulated land surface processes are evaluated over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A reduced model for edge localized mode control by supersonic molecular beam injection and pellet injection

We develop a diffusive, bistable, tri-unstable cellular automata (CA) model to study the dynamics of H-mode pedestal with edge localized modes (ELMs) and their control by supersonic molecular beam injection (SMBI) and pellet injection (PI). It is shown that the new CA model can reproduce the key features of H-mode pedestals with various types of ELM, including Type-I ELM. SMBI and PI are modeled as additional grain injections into pedestal with varying degrees of injected materials and profiles. It is found that H-mode pedestal responds to SMBI differently depending on the baseline fueling. If the baseline fueling is large enough to allow Type-I ELM, SMBI enhances large transport avalanches caused by ballooning instabilities. These avalanches prevent the total pedestal current from reaching the boundary for peeling instability. On the other hand, if the baseline fueling is low to avoid Type-I ELM, SMBI enhances small scale avalanches, which prevent the pedestal from growing to profiles globally vulnerable to ballooning instabilities. These imply that SMBI can mitigate different types of ELM by converting them to more benign types. From CA modeling of pellet injection, it is shown that Type-I ELM can be triggered by pellet injection with sufficient strength and depth. Scanning the frequency of pellet injection, it is found that a maximum efficiency of pellet pacing is achieved when the injection frequency is approximately ten times the natural frequency of Type-I ELM.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Effects beyond ideal MHD on stability of wide and enhanced pedestal regimes in NSTX

Stability of edge-localized modes (ELMs) in spherical tokamaks is explored using the extended MHD model. Linear NIMROD simulations have been performed for three NSTX discharges 132543, 132588, and 141133, to investigate the role of resistivity, diffusivity, and shear flows on the onset of ELMs. The first discharge represents the wide pedestal regime and the later two discharges represent ELM-free enhanced pedestal H-mode. We first present the effect of toroidal rotation shear and find a flow shear destabilizing effect in these NSTX discharges. Simulations are also extended to include the two-fluid and ion gyroviscosity effects. Simulations show that the flow shear can shift the mode spectrum and alter the critical condition of ELM onset. We also uncover that ELM onset prediction in spherical tokamaks requires effects beyond MHD, in particular gyroviscosity and diamagnetic terms could stabilize Peeling-Ballooning modes consistent with the experimental observation of ELM-free regimes in NSTX. The findings give new insight into the nature of the interplay between resistivity, flows, and diamagnetic stabilization in ELM suppression and have potential applications to ELM control schemes in NSTX-U and next-generation spherical tokamaks. This study identifies the essential physical effects that must be included in future predictive and validation simulations.

NSTX↗
DOE OSTI2024

Tokamak edge localized mode onset prediction with deep neural network and pedestal turbulence

Abstract A neural network, BES-ELMnet, predicting a quasi-periodic disruptive eruption of the plasma energy and particles known as edge localized mode (ELM) onset is developed with observed pedestal turbulence from the beam emission spectroscopy system in DIII-D. BES-ELMnet has convolutional and fully-connected layers, taking two-dimensional plasma fluctuations with a temporal window of size 128 µ s and generating a scalar output which can be interpreted as a probability of the upcoming ELM onset. As approximately labeled inter-ELM broadband ( 15 kHz ⩽ f ⩽ 150 kHz ) fluctuations are given to the network, BES-ELMnet learns by itself ELM-related precursors arising before the onsets through supervised learning scheme. BES-ELMnet achieves the gradually increasing ELM onset probabilities between two consecutive ELMs during the inter-ELM phases and can forecast the first ELM onsets which occur after the high confinement mode transition. We further investigate the network generality in terms of the selected frequency band to ensure the use of BES-ELMnet for various operation regimes without changing the trained architecture. Therefore, our novel prediction method will enhance a proactive high confinement mode control of fusion-grade plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

NGEE Arctic Authorship Guidelines

Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

Iversen, Colleen [ORNL] (ORCID:0000000182933450)↗