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22-27 October 2018
Mahatma Mandir Conference Centre
Asia/Kolkata timezone

Predicting Scrape-Off Layer profiles and filamentary transport for reactor relevant devices

26 Oct 2018, 17:40
Mahatma Mandir Conference Centre

Mahatma Mandir Conference Centre

Gandhinagar (nearest Airport: Ahmedabad), India
Oral THD - Magnetic Confinement Theory and Modelling: Plasma–material interactions; divertors, limiters, SOL EX/9-TH/7 Divertor & Exhaust Physics


Prof. Fulvio Militello (Culham Centre for Fusion Energy)


This paper discusses a statistical framework that relates the fundamental physics of Scrape-Off Layer (SOL) L-mode and inter-ELM filaments with the profiles they generate in magnetic confinement devices. This work reviews the theoretical and numerical work recently carried out at CCFE in support of the statistical framework and compares it with experimental measurements obtained with innovative techniques on MAST and JET. The emphasis will be on extrapolating the knowledge gained to future machines like ITER and to advanced divertor solutions. With a semi-analytic treatment using minimal computational resources, the framework predicts and interprets the experimental profiles and of the turbulence statistics on the basis of simple properties of the filaments, such as their radial motion and their draining towards the divertor. Filaments are described as independent events and modelled with a wave function of amplitude and width statistically distributed according to experimental observations and evolving according to fluid equations. The framework predicts that radially accelerating filaments, less efficient parallel exhaust (e.g. due to interaction with neutrals) and also a statistical distribution of the radial velocities can contribute to induce flatter profiles in the far SOL and therefore enhance plasma-wall interactions. It also suggests that profile broadening at high fueling rates, potentially harmful for ITER, can be caused by interactions with neutrals in the divertor or at the wall or by a significant radial acceleration of the filaments. The results of the framework are backed up by systematic experimental comparison with measurements taken on JET and MAST using Langmuir probes and fast visual cameras. Advanced machine learning algorithms were developed and deployed, including Bayesian analsysis of time traces and convolutional neural networks applied to filament identification in images. In all the cases treated, the theoretical prediction matched the experimental data within errorbars. In addition, 3D simulation in realistic geometry were performed with the 3D SOL turbulence code STORM, with the aim of assessing the validity of the framework assumptions. The mechanisms governing the interaction of pairs of filaments and the dynamics of high beta, inter-ELM like, filaments were investigated and employed to improve the statistical framework.
Country or International Organization United Kingdom
Paper Number TH/7-1

Primary author

Prof. Fulvio Militello (Culham Centre for Fusion Energy)


Mr Alasdair Wynn (CCFE and University of York) Prof. Alessandra Fanni (University of Cagliari) Dr Andrew Kirk (Culham Centre for Fusion Energy) Dr Augusto Montisci (University of Cagliari) Dr Barbara Cannas (University of Cagliari) Dr Benjamin Dudson (University of York) Prof. Bruce Lipshultz (University of York) Dr Fabio Pisano (University of Cagliari) Dr Fabio Riva (CCFE) Dr Giuliana Sias (University of Cagliari) Dr James Harrison (CCFE) Dr John Omotani (CCFE) Dr Lynton Appel (UKAEA) Dr Robert Akers (UKAEA) Dr Sara Carcangiu (University of Cagliari) Mr Thomas Farley (CCFE and Universiy of Liverpool) Mr Thomas Nicholas (CCFE and University of York)

Presentation Materials