arXiv subjects

the DIII-D team

Publications and source records attributed to the DIII-D team.

3 recordsLinked to original sources

arXiv2026

First step toward multi machine ELM energy scalings and extrapolations to SPARC and ITER

It is shown that the ELM energy loss normalized by the plasma stored energy (ΔEELM/Wplasma) for high-density small/QCE ELM regimes scales inversely with the separatrix turbulence parameter a_t. In contrast, the neoclassical electron collisionality at the pedestal top, nu*e,neo, expected to regulate ΔEELM/Wplasma according to the Loarte scaling (Plasma Phys. Control. Fusion 2003 45 1549), does not adequately capture ΔEELM/Wplasma data for peeling-ballooning-limited type-I ELMs and ballooning-limited small/QCE ELMs, limiting its applicability for extrapolation to one scenario window. A multi-machine database including seven tokamaks and with ΔEELM/Wplasma ranging from 0.5% to 14%, has been analyzed. A regression analysis on only type-I ELMs yields ((ΔE_ELM)/W_plasma )_(Type-I ) [%]=6.8*T_(e,ped)^0.03 n_(e,ped)^(-0.4) \k{appa}^(-0.4) R_major^0.4, corresponding to ΔEELM/Wplasma =4.5% for nominal SPARC pedestal parameters and 12% for the ITER D-T Q=10 scenario. For the small/QCE ELM class, however, as a_t increases, the pedestal moves toward a ballooning-limited boundary, the toroidal mode number increases, the ELM frequency rises following the scaling f_ELM=46e^((2.25*a_t)), and ΔEELM/Wplasma decreases via the relation (ΔE_ELM)/W_plasma [%]=1.6e^(-(α_t/2)). For SPARC QCE-relevant a_t=0.86 and ITER high-fueling scenario a_t = 0.64, the scaling favorably predicts ΔEELM/Wplasma of 1.0% and 1.2%, respectively, with values below 1% if the small/QCE ELM regime is pushed beyond a_t >1. The small/QCE ELM-fitted results represent an initial step toward future analysis on broader datasets, which will be necessary to improve the accuracy of projections for future reactor-relevant scenarios.

physics.plasm-ph↗

arXiv2025

Correlation of the L-mode density limit with edge collisionality

The "density limit" is one of the fundamental bounds on tokamak operating space, and is commonly estimated via the empirical Greenwald scaling. This limit has garnered renewed interest in recent years as it has become clear that ITER and many tokamak pilot plant concepts must operate near or above the Greenwald limit to achieve their objectives. Evidence has also grown that the Greenwald scaling - in its remarkable simplicity - may not capture the full complexity of the density limit. In this study, we assemble a multi-machine database to quantify the effectiveness of the Greenwald limit as a predictor of the L-mode density limit and compare it with data-driven approaches. We find that a boundary in the plasma edge involving dimensionless collisionality and pressure, $ν_{*\rm, edge}^{\rm limit} = 3.5 β_{T,{\rm edge}}^{-0.40}$, achieves significantly higher accuracy (false positive rate of 2.3% at a true positive rate of 95%) of predicting density limit disruptions than the Greenwald limit (false positive rate of 13.4% at a true positive rate of 95%) across a multi-machine dataset including metal- and carbon-wall tokamaks (AUG, C-Mod, DIII-D, and TCV). This two-parameter boundary succeeds at predicting L-mode density limits by robustly identifying the radiative state preceding the terminal MHD instability. This boundary can be applied for density limit avoidance in current devices and in ITER, where it can be measured and responded to in real time.

physics.plasm-ph↗

arXiv2019

Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices

The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series generated by diagnostic instruments observing fusion plasmas. Here we apply this neural network architecture to the popular problem of disruption prediction in fusion tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. ECEi measures a fundamental plasma quantity (electron temperature) with high temporal resolution over the entire plasma discharge, making it sensitive to a number of potential pre-disruptions markers with different temporal and spatial scales. Promising, initial disruption prediction results are obtained training a deep CNN with large receptive field (~30k), achieving an $F_1$-score of ~91% on individual time-slices using only the ECEi data.

physics.plasm-ph↗