The first wall (FW) is key component for ITER and the Chinese DEMO-the China Fusion Engineering Test Reactor (CFETR). It faces burning plasma and has a high heat flux (HHF) surface load. Critical issues and key technologies for manufacturing the ITER and CFETR FW are discussed with regards to thermal fatigue performance, Be/CuCrZr and W/reduced activation ferritic/martensitic (RAFM) steel bonding, material properties and failure mechanisms. Design improvement of the hypervapotron cooling channel and the joint interface structure was done, which increases the thermal fatigue lifetime of the enhanced heat flux (EHF) ITER FW by more than one order of magnitude as indicated by thermo-mechanical analysis. Small mock-ups and full-size EHF FW fingers were manufactured by qualified technologies in sequence. An HHF test of the small mock-ups showed that Be tile size and defects at the Be/CuCrZr interface have a great effect on the fatigue lifetime. Manufacturing tests showed a thick oxygen-free Cu interlayer could provide a good solution for the interface cracking issue. An ITER EHF FW semi-prototype was manufactured with additional two fullsize finger pairs that successfully survived the demanding HHF test at 4.7 and 5.9 MW m −2 . Various manufacturing technologies for joining W/RAFM steel for the CFETR FW have been studied, including a TiN coating at the interface as a tritium permeation barrier. Hot iso-static pressed W/RAFM steel joints showed a higher bonding strength than brazing joints but varied a lot with the interlayer metals. Further studies are required to optimize the technologies.

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Disruption prediction and mitigation is a crucial topic, especially for future large-scale tokamaks, due to disruption’s concomitant harmful effects on the devices. On this topic, disruption prediction algorithm takes the responsibility to give accurate trigger signal in advance of disruptions, therefore the disruption mitigation system can effectively alleviate the harmful effects. In the past 5 years, a deep learning-based algorithm is developed in HL-2A tokamak. It reaches a true positive rate of 92.2%, a false positive rate of 2.5% and a total accuracy of 96.1%. Further research is implemented on the basis of this algorithm to solve three key problems, i.e., the algorithm’s interpretability, real-time capability and transferability. For the interpretability, HL-2A’s algorithm gives saliency maps indicating the correlation between the algorithm’s input and output by perturbation analysis. The distribution of correlations shows good coherence with the disruption causes. For the transferability, a preliminary disruption predictor is successfully developed in HL-2M, a newly built tokamak in China. Although only 44 shots are used as the training set of this algorithm, it gives reasonable outputs with the help of data from HL-2A and J-TEXT. For the real-time capacity, the algorithm is accelerated to deal with an input slice within 0.3ms with the help of some adjustments on it and TFLite framework. It is also implemented into the plasma control system and gets an accuracy of 89.0% during online test. This paper gives a global perspective on these results and discusses the possible pathways to make HL-2A’s algorithm a more comprehensive solution for future tokamaks.

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