王灏，王爱科，杨青巍，丁玄同

核工业西南物理研究院wanghao@swip.ac.cn

HL-2A装置上关于破裂预测的一些尝试 第十三届全国等离子体科学技术会议 2007, 8, 20-22, 成都

核工业西南物理研究院

王灏，等 . 第十三届全国等离子体科学技术会议 2007, 8, 20-22, 成都

Introduction

Disruption in a tokamak is a sudden loss of confinement and subsequent transfer of plasma energy to the surrounding structure component. The force and heat loads, induced by disruption, may damage the machine walls and support structure. Thus, disruption prediction at a sufficient early time is important for taking measures to avoid the disruption or mitigate the unavoidable ones. Physically, disruptions have different causes, most of which have been identified but whose dynamics is not known in detail. For instance, the relations of various instabilities to disruptions remain an active research area. Therefore, some new methods, includes ANN method and other methods, are designed to forecast the plasma disruption. In our works, we have tried two methods, these are ANN method and MHD method.

What is ANN

ANN has a complex structure, including many neurons and layers, and Signals transmitted to the neural network through the input layer. By calculating, the output of Layer 1 has been got, and then, the output of Layer 1 became the input of Layer 2. This process can be repeated until a final output has been got.

How to train an ANN

Our ANN model and database

When we trainning, the output have only two states: 0 and 1. When we discharge, the plasma will have two different states, stable state and unstable state. When unstable, we set the standard output equals 0, and Correspondingly, when stable, the output eaquals 1. Therefore, if the output is less than 0 or close to 0, we must be carefully. Because This means that the plasma may disrupte.

13 experimental diagnostic signals Six Mirnov probes, one H-alpha line intensity, one channel of Soft X ray detector (SXR), one line integrated density ne, one main plasma impurity detector, one bremsstrahlung signal, one bolometer, and one vacuum ultraviolet spectrum signal (VUV).

Architecture 13:15:30:10:1 After compared many different architectures, we believe this architecture is the best for our database.

Our results

This figure shows the prediction for the disruptive pulse 4435. The dashed line is the plasma current, and another line is the output of ANN. It disrupts at 980ms, while the network gives the disruption alarm at 945ms, 35ms in advance.

Advantages and disadvantages of ANN method

Relax the resolution requirement If we want to detect and controll disruption directly, we need very good diagnosis and controll technology. A neural network includes biases, sigmoid activation functions and linear activation functions and, can carry out complex non-linear mapping, so we can use ANN to predict.

Two many calculations Trainning will take two many times, and sometimes, the error function will not be convergent. If we apply ANN as an online predictor, perhaps we need high performance computer.

We can try some other methods For example, MHD method.

Principle of MHD method

When plasma is in stable state, both amplitude and period of MHD perturbation are small, while before disrupt, there are strong MHD activities.(Yang Q W et al 2006 Chin. Phys. Lett. 23 891)

Prediction on the pulse 4207

The blue line meas plasma current, and the red line is output of our prediction systerm. It disrupts at 1470ms, and the prediction systerm gives an alarm at 1450ms, 20ms in advance.

Advantages and disadvantages of ANN method

Using only a few singals We use only one Mirnov signal, so it’s fast and flexible. Perhaps, we can design an online predictor according to this method.

High successful rate We can get about 80% successful rate with only a few false alarm, or near 90% successful rate with 25% false alarm.

Unable to predict short pulse In our database, all of the discharge time are more than 1000ms. If we apply this method into short pulse, the successful rate will decrease.

Summary and future plan

Artificial neural networks (ANN) are trained to forecast the plasma disruptions in HL-2A tokamak. The optimized network architecture is obtained. Relation between Mirnov signals and disruptions is used, and then a new attempt, considering periods and swings of Mirnov signals, is carried out. These two methods can successfully detect the disruptive pulses of HL-2A tokamak. The results show the possibility of developing a disruption predictor that intervenes well in advance in order to avoid plasma disruptions or mitigate their effects.

In the future, we want to do more works about disruption prediction. We want to use more Mirnov signals and then, we can predict short discharges. We want to consider more signals about period and amplitude, such as soft X ray, bolometer, density, and so on. We will unite MHD method with ANN, may be it will improve the successful rate. And at last, perhaps we can try to predict disruption on-line.