Scrape-Off-Layer Flow Studies in Tokamaks PDF Download

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Languages : en
Pages : 10

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Book Description
A summary is given of the work carried out under the LDRD project 09-ERD-025 entitled Scrape-Off-Layer Flow Studies in Tokamaks. This project has lead to implementation of the new prototype Fourier Transform Spectrometer edge plasma flow diagnostic on the DIII-D National Fusion Facility at General Atomics, acquisition of carbon impurity concentration and flow data, and demonstration that the resulting data compare reasonably well with LLNL's edge plasma transport code UEDGE. Details of the work are contained in attached published papers, while the most recent results that are being written-up for publication are summarized in the report. Boundary plasma flows in tokamak fusion devices are key in determining the distribution of fuel and impurity ions, with tritium build-up in the walls an especially critical operational issue. The intrusion of impurity ions to the hot plasma core region can result in serious energy-loss owing to line radiation. However, flow diagnostic capability has been severely limited in fusion-relevant hot edge plasmas where Langmuir-type probes cannot withstand the high heat flux and traditional Doppler spectroscopy has limited resolution and signal strength. Thus, new edge plasma flow diagnostic capabilities need to be developed that can be used in existing and future devices such as ITER. The understanding of such flows requires simulation with 2-dimensional transport codes owing to the geometrical complexity of the edge region in contact with material surfaces and the large number of interaction physical processes including plasma flow along and across the magnetic field, and coupling between impurity and neutral species. The characteristics of edge plasma flows are substantially affected by cross-magnetic-field drifts (ExB/B2 and BxVB/B2), which are known to introduce substantial convergence difficulty for some cases. It is important that these difficulties be overcome so that drifts can be included in transport models, both for validation with existing data and for projection to future devices.