Keywords: fusion - Princeton Data Commons Discovery Search Results

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1. Data for "The value of fusion energy to a decarbonized United States electric grid"

Author(s):: Schwartz, Jacob A.; Ricks, Wilson; Kolemen, Egemen; Jenkins, Jesse D.
Abstract:: Fusion could be a part of future decarbonized electricity systems, but it will need to compete with other technologies. In particular, pulsed tokamaks plants have a unique operational mode, and evaluating which characteristics make them economically competitive can help select between design pathways. Using a capacity expansion and operations model, we determined cost thresholds for pulsed tokamaks to reach a range of penetration levels in a future decarbonized US Eastern Interconnection. The required capital cost to reach a fusion capacity of 100 GW varied from $3000 to $7200/kW, and the equilibrium penetration increases rapidly with decreasing cost. The value per unit power capacity depends on the variable operational cost and on cost of its competition, particularly fission, much more than on the pulse cycle parameters. These findings can therefore provide initial cost targets for fusion more generally in the United States.
Type:: Dataset
Issue Date:: 2022

2. Novel 2D velocity estimation method for large transient events in plasmas

Author(s):: Mate, Lampert; Ahmed, Diallo; Stewart, Zweben
Abstract:: The dataset includes the data shown in the figures of the publication
Type:: Dataset
Issue Date:: 31 July 2021

3. A software package for plasma facing component analysis and design: the Heat flux Engineering Analysis Toolkit (HEAT)

Author(s):: Looby, Tom; Reinke, Matthew; Wingen, Andreas; Menard, Jonathan; Gerhardt, Stefan; Gray, Travis; Donovan, David; Unterberg, Ezekial; Klabacha, Jonathan; Messineo, Mike
Abstract:: The engineering limits of plasma facing components (PFCs) constrain the allowable operational space of tokamaks. Poorly managed heat fluxes that push the PFCs beyond their limits not only degrade core plasma performance via elevated impurities, but can also result in PFC failure due to thermal stresses or melting. Simple axisymmetric assumptions fail to capture the complex interaction between 3D PFC geometry and 2D or 3D plasmas. This results in fusion systems that must either operate with increased risk or reduce PFC loads, potentially through lower core plasma performance, to maintain a nominal safety factor. High precision 3D heat flux predictions are necessary to accurately ascertain the state of a PFC given the evolution of the magnetic equilibrium. A new code, the Heat flux Engineering Analysis Toolkit (HEAT), has been developed to provide high precision 3D predictions and analysis for PFCs. HEAT couples many otherwise disparate computational tools together into a single open source python package. Magnetic equilibrium, engineering CAD, finite volume solvers, scrape off layer plasma physics, visualization, high performace computing, and more, are connected in a single web-based user interface. Linux users may use HEAT without any software prerequisites via an appImage. This manuscript introduces HEAT, discusses the software architecture, presents first HEAT results, and outlines physics modules in development.
Type:: Dataset
Issue Date:: March 2021

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

Author(s):: Churchill, R.M; the DIII-D team
Abstract:: 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 ({$\sim$}30k), achieving an $F_1$-score of {$\sim$}91\% on individual time-slices using only the ECEi data.
Type:: Dataset
Issue Date:: October 2019