Prediction of turbulent energy based on low-rank resolvent modes and machine learning
Journal of Physics: Conference Series(2024)
摘要
A modelling framework based on the resolvent analysis and machine learning is
proposed to predict the turbulent energy in incompressible channel flows. In
the framework, the optimal resolvent response modes are selected as the basis
functions modelling the low-rank behaviour of high-dimensional nonlinear
turbulent flow-fields, and the corresponding weight functions are determined by
data-driven neural networks. Turbulent-energy distribution in space and scales,
at the friction Reynolds number 1000, is predicted and compared to the data of
direct numerical simulation. Close agreement is observed, suggesting the
feasibility and reliability of the proposed framework for turbulence
prediction.
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