Prediction of turbulent energy based on low-rank resolvent modes and machine learning

2024/03/08 by Yitong Fan, Bo Chen, Fan, Yitong +3
Earth and Planetary Sciences · Engineering · #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Meteorological Phenomena and Simulations

paper · pdf · doi:10.48550/arxiv.2403.05067

openalex publication_date 2024/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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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