Design of Real-Time Data Acquisition System for Tokamak Disruption Prediction

[Introduction] Plasma disruption poses a significant threat to the tokamak nuclear device during its running and can cause damage to the device. Such damage can be reduced by adopting the disruption mitigation system, which has an action time highly dependent on the real-time running plasma disruption prediction system for predicting the plasma disruption moment. The deep-learning-based neural network has been used to train plasma disruption prediction models, and the real-time running of the deep-learning-based disruption prediction models requires a huge amount of real-time data from multiple diagnostics. [Method] The article proposed a design scheme for a real-time data acquisition system. The real-time data acquisition and transmission system was designed based on the modular structure and divided into the multiple channels acquisition module, ADC converting control and data reading module, data grouping and packing module and data transmission network module. The data transmission network module was developed on the hardware UDP network stack running on the FPGA at a speed of 10 G. This hardware UDP network stack featured a deterministic data transmission process, enabling a very low transmission latency of the system. [Result] The real-time data acquisition system has a sampling rate reaching 2 MSa/s, a data throughput rate exceeding 9.3 Gb/s, and a data transmission latency of less than 10 μs. [Conclusion] This data acquisition system facilitates the fast transmission of diagnostic data streams to disruption prediction models. The high sampling rates enable the system to perform real-time transmission of one-dimensional diagnostics such as radiation and electron temperature, improving the temporal resolution of data. The high data throughput rate can increase the transmission volume of diagnostic data, and the low data transmission latency can reduce the time required for disruption prediction models to obtain diagnostics data.