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The paper proposes a dynamic aggregation enhanced efficient global optimization algorithm (DA-EGO) for solving high-dimensional turbomachinery design problems, validated through benchmark tests and aerodynamic applications.
This paper proposes a panoramic aerodynamic performance prediction framework for turbomachinery using a transformer-enhanced neural operator (TNO), which first predicts basic physical quantities like temperature and pressure before estimating key performance parameters, significantly reducing computational cost while maintaining accuracy.