Likelihood Identification of High-Beta Disruption in JT-60U

Abstract

Prediction and likelihood identification of high-beta disruption in JT-60U has been discussed by means of feature extraction based on sparse modeling. In disruption prediction studies using machine learning, the selection of input parameters is an essential issue. A disruption predictor has been developed by using a linear support vector machine with input parameters selected through an exhaustive search, which is one idea of sparse modeling. The investigated dataset includes not only global plasma parameters but also local parameters such as ion temperature and plasma rotation. As a result of the exhaustive search, five physical parameters, i.e., normalized beta β N , plasma elongation κ, ion temperature T i and magnetic shear s at the q = 2 rational surface, have been extracted as key parameters of high-beta disruption. The boundary between the disruptive and the non-disruptive zones in multidimensional space has been defined as the power law expression with these key parameters. Consequently, the disruption likelihood has been quantified in terms of probability based on this boundary expression. Careful deliberation of the expression of the disruption likelihood, which is derived with machine learning, could lead to the elucidation of the underlying physics behind disruptions.

A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors

“…So far, all the main families of existing real-time compatible classifier technologies have been explored, including support vector machines, artificial neural networks, generative topographic mapping, fuzzy logic and deep learning. They have also been applied to the data of many tokamaks of various generations: ADITYA (India) 47 , ASDEX Upgrade (Germany) 48 , DIII-D (US) 49 – 51 , J-TEXT (China) 52 , NSTX (US) 53 , ALCATOR C-MOD (US) 54 , JT-60U (Japan) 55 , EAST (China) 56 – 58 , HL-2A (China) 59 and JET (UK) 7 . Up to now only three machine-learning-based predictors have been implemented in JET’s real-time network, APODIS 60 , SPAD 61 , and the centroid-based method 21 .…”

A Control Oriented Strategy of Disruption Prediction to Avoid the Configuration Collapse of Tokamak Reactors

“…So far, all the main families of existing real-time compatible classifier technologies have been explored, including support vector machines, artificial neural networks, generative topographic mapping, fuzzy logic and deep learning. They have also been applied to the data of many tokamaks of various generations: ADITYA (India) [47], ASDEX Upgrade (Germany) [48], DIII-D (US) [49][50][51], J-TEXT (China) [52], NSTX (US) [53], ALCATOR C-MOD (US) [54], JT-60U (Japan) [55], EAST (China) [56][57][58], HL-2A (China) [59] and JET (UK) [7]. Up to now only three machinelearning based predictors have been implemented in JET's real-time network, APODIS [60], SPAD [61], and the centroid based method [62].…”

Disruption prediction with artificial intelligence techniques in tokamak plasmas

“…Approaches relying on a mixture of time/frequency domains, including wavelet decompositions, have also been pursued [17][18][19] . With regard to classifier technologies, real-time compatible predictors have typically been based on artificial neural networks, support vector machines, fuzzy logic, generative topographic mapping and deep learning and have been studied on a broad range of tokamaks, including ADITYA (India) 20 , ASDEX Upgrade (Germany) 21 , DIII-D (United States) [22][23][24] , J-TEXT (China) 25 , NSTX (United States) 26 , ALCATOR C-MOD (United States) 27 , JT-60U (Japan) 28 , EAST (China) [29][30][31] , HL-2A (China) 32 and JET (United Kingdom) [33][34][35] .…”

“…The classifiers employed in the studies on tokamaks mentioned above [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] were developed using real-time valid solutions, which guarantee response times within a specified time window. The predictors discussed in the remainder of this work have been tested offline with real-time compatible technologies and using only real-time available signals.…”