Machine-learning data-driven modeling of laminar-turbulent transition in compressor cascade

Abstract

Laminar-turbulent transition plays a critical role in the aerodynamics of axial-flow compressor cascades. The Reynolds-averaged Navier–Stokes method is so far the most practicable and popular means for numerical simulations of transitional flows to support aerodynamic analysis and the design of compressor cascades. However, the prediction accuracy of the conventional transport equation-based transition models has reached a plateau. In the present work, a machine-learning data-driven transition modeling method that can take full advantage of high-fidelity simulation data is proposed. The turbulence intermittency is calculated algebraically from local flow quantities through a neural network. The proposed method is then applied to construct an algebraic transition model, which is tailored for compressor cascades and coupled with the Spalart–Allmaras turbulence model. The validation results show that the constructed transition model is able to predict flows in compressor cascades with transition in both the Kelvin–Helmholtz instability-induced and bypass modes. Furthermore, the constructed transition model exhibits higher prediction accuracy for both the transition modes than the conventional intermittency factor equation-based transition model. This work demonstrates the effectiveness and promising prospect of machine-learning and data-driven methods in the modeling of complex flow physics.