摘要
Accelerating the design of Ni-based single crystal(SX)superalloys with superior creep resistance at ultrahigh temperatures is a desirable goal but extremely challenging task.In the present work,a deep transfer learning neural network with physical constraints for creep rupture life prediction at ultrahigh temperatures is constructed.Transfer learning enables deep learning model breaks through the generalization performance barrier in the extrapolation space of ultrahigh temperature creep properties in the case of a very small dataset,which is the key to achieving the above design goal.
基金
the National Natural Science Foundation of China(NSFC)under grant No.92360302 and Beijing Nova Program No.20230484318 for carrying out the present research work is gratefully acknowledged
This work was supported by the Science Center for Gas Turbine Project(No.P2021-A-IV-001-003)
Science Center for Gas Turbine Project(No.P2022-B-IV-005-001)
Key R&D Program of Zhejiang(Nos.2024SSYS0085,2024SSYS0076)
This research was also supported by the high-performance computing(IIPC)resources at Beihang University and Chengdu Supercomputing Center.
