Measurements of multiple heat flux components at the divertor target by using surface eroding thermocouples (invited)

The Surface Eroding Thermocouple (SETC) is a robust diagnostic utilized in DIII-D to provide fast, edge-localized modes (ELMs) resolved heat flux measurements, in particular in geometric regions that are too shadowed for traditional infrared thermography. In order to further investigate the power dissipation in the divertor region, a combination of flush-mounted and recessed SETCs was developed to assess the effect on surface heating from non-charged particles at the divertor target. Utilizing the Divertor Materials Evaluation System sample exposure platform, the first demonstration of the feasibility of using this new method to distinguish between the heat flux from charged particles and that from neutrals and radiative heating was achieved. This paper details the process of using the combination of flush SETCs and recessed SETCs to measure the multiple heat flux components at the divertor target and further discusses how to determine two important ratios, α (ratio of heat flux from charged particles deposit on recessed SETC to that deposit on flush SETC) and β (ratio of heat flux from non-charged particles deposit on recessed SETC to that deposit on flush SETC), in the estimation of the heat flux from non-charged particle sources. Using a time dependent ratio α, it was found that ∼50% of the total incident heat flux is attributable to the non-charged particles in the fully detached open divertor in DIII-D. Finally, the new application of similar SETC diagnostics in the Small Angle Slot divertor with a V-like configuration and partial tungsten coated surface (SAS-VW) is also introduced.

Advancing Fusion with Machine Learning Research Needs Workshop Report

Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

Observation of edge instability limiting the pedestal growth in tokamak plasmas

With fusion device performance hinging on the edge pedestal pressure, it is imperative to experimentally understand the physical mechanism dictating the pedestal characteristics and to validate and improve pedestal predictive models. This Letter reports direct evidence of density and magnetic fluctuations showing the stiff onset of an edge instability leading to the saturation of the pedestal on the Alcator C-Mod tokamak. Edge stability analyses indicate that the pedestal is unstable to both ballooning mode and kinetic ballooning mode in agreement with observations.