Engineering topics

Saarelma, Samuli

Publications and source records attributed to Saarelma, Samuli.

5 records

DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Recent progress in L-H transition studies at JET: Tritium, Helium, Hydrogen and Deuterium

In this report we present an overview of results from a series of L-H transition experiments undertaken at JET since the installation of the ITER-like-wall (JET-ILW), with Beryllium wall tiles and a Tungsten divertor. Tritium, Helium and Deuterium plasmas have been investigated. Initial results in Tritium show ohmic L-H transitions at low density and the power threshold for the L-H transition (P LH ) is lower in Tritium plasmas than in Deuterium ones at low densities, while we still lack contrasted data to provide a scaling at high densities. In Helium plasmas there is a notable shift of the density at which the power threshold is minimum (n e,min ) to higher values relative to Deuterium and Hydrogen references. Above n e,min (He) the L-H power threshold at high densities is similar for D and He plasmas. Transport modelling in slab geometry shows that in Helium neoclassical transport competes with interchange-driven transport, unlike in Hydrogen isotopes. Measurements of the radial electric field in Deuterium plasmas show that E r shear is not a good indicator of proximity to the L-H transition. Transport analysis of ion heat flux in Deuterium plasmas show a non-linearity as density is decreased below n e,min . Lastly, a regression of the JET-ILW Deuterium data is compared to the 2008 ITPA scaling law.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Diverted Negative Triangularity plasmas on DIII–D: The benefit of high confinement without the liability of an edge pedestal

Diverted discharges at negative triangularity on the DIII-D tokamak sustain normalized confinement and pressure levels typical of standard H-mode scenarios (H 98y,2 ≅1, β N ≅3) without developing an edge pressure pedestal, despite the auxiliary power far exceeding the L → H power threshold expected from conventional scaling laws. The power degradation of confinement is substantially weaker than the ITER-89P scaling, resulting in a confinement factor that improves with increasing auxiliary power. Furthermore, the absence of the edge pedestal is beneficial in several aspects, such as eliminating the need for active mitigation or suppression of edge localized modes, low impurity retention and a reconstructed scrape-off layer heat flux width at the mid-plane that exceeds the ITPA multi-machine scaling law by up to 50%. Together with technological advantages granted by placing the divertor at larger radii, plasmas at Negative Triangularity without an edge pedestal feature both core confinement and power handling characteristics that are potentially suitable for operation in future fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Ballooning instability preventing the H-mode access in plasmas with negative triangularity shape on the DIII-D tokamak

Infinite toroidal mode number (n=∞ ) ballooning mode analysis of negative triangularity discharges on DIII-D shows that the access to 2nd stability becomes strongly restricted when the top triangularity decreases even modestly from -0.18 to -0.36. This is observed in experiment to coincide with the suppression of the L-H-transition. Further theoretical analysis with ballooning mode limited pedestals shows that the threshold for opening the 2nd stability access rises from a pedestal temperature of 0.3 keV to 1 keV when the top triangularity is decreased from -0.18 to -0.36 indicating that to access the 2nd stability with the more negative triangular shape would require unrealistically high pedestal temperature. Furthermore, the pedestal predicted by the EPED code agrees with the experimental H-mode profile for the negative triangularity case but in contrast to positive triangular shapes the prediction shows no increase in pedestal height with increasing core pressure when triangularity is negative. This work provides a first model to predict when negative triangularity plasmas can be expected to access the H-mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗