Towards robust prediction of material properties for nuclear reactor design under scarce data: a study in creep rupture property

Yu Chen, Edoardo Patelli, Zhen Yang, Adolphus Lye

Research output: Working paperWorking Paper/Preprint

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Abstract

Advances in Deep Learning bring further investigation into credibility and robustness, especially for safety-critical engineering applications such as the nuclear industry. The key challenges include the availability of data set (often scarce and sparse) and insufficient consideration of the uncertainty in the data, model, and prediction. This paper therefore presents a meta-learning based approach that is both uncertainty- and prior knowledge-informed, aiming at trustful predictions of material properties for the nuclear reactor design. It is suited for robust learning under limited data. Uncertainty has been accounted for where a distribution of predictor functions are produced for extrapolation. Results suggest it achieves superior performance than existing empirical methods in rupture life prediction, a case which is typically under a small data regime. While demonstrated herein with rupture properties, this learning approach is transferable to solve similar problems of data scarcity across the nuclear industry. It is of great importance to boosting the AI analytics in the nuclear industry by proving the applicability and robustness while providing tools that can be trusted.
Original languageEnglish
Place of PublicationIthaca, NY
Number of pages8
DOIs
Publication statusPublished - 28 May 2024

Keywords

  • meta learning
  • uncertainty quantification
  • creep rupture
  • nuclear design
  • material property

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