面向托卡马克破裂预测的实时数据采集系统设计

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

[目的]等离子破裂是托卡马克核聚变装置运行中的重大威胁。破裂缓解系统可以降低等离子体破裂对装置的危害,其动作时机高度依赖于实时运行的等离子体破裂预测系统对等离子体破裂时刻的预测。基于深度学习的神经网络已被用于训练等离子体破裂预测模型,其实时运行需要来自多种诊断的大量实时数据。[方法]文中提出一种实时数据采集系统的设计方案。该系统基于模块化结构进行设计,分为多通道采集模块、ADC转换控制和数据读取模块、数据分组与封装模块和数据传输网络模块。数据传输网络模块基于运行在FPGA上的10 G速率的硬件UDP网络协议栈进行构建。该硬件UDP协议栈具有确定性的数据传输过程,使得系统具有很低的传输时延。[结果]该实时数据采集系统的采样率可以达到每通道2 MSa/s,数据吞吐速率超过9.3 Gb/s,数据传输延迟小于10μs。[结论]该实时数据采集系统可以为破裂预测程序提供快速传输的实时诊断数据流。高采样率使得系统可以对辐射、电子温度等1维诊断进行实时传输,提高数据时间分辨率。高数据吞吐率可以提高诊断数据的传输数据量,低数据传输时延可以减少破裂预测模型获取诊断数据的时间。

[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 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 10μs.[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.