Fast and accurate predictions of turbulent flows are of great importance in the science and
engineering field. In this paper, we investigate the implicit U-Net enhanced Fourier neural …

This paper addresses the issue of predicting separated flows with Reynolds-averaged
Navier–Stokes (RANS) turbulence models, which are essential for many engineering tasks …

Turbulence closure modeling using (ML) is at an early crossroads. The extraordinary
success of ML in a variety of challenging fields had given rise to an expectation of similar …

The baseline two-equation Reynolds-averaged Navier–Stokes (RANS) models include fluid
density but lack calibration for compressible flows, making them inadequate for high Mach …

Abstract The baseline Launder–Spalding k− ε model cannot be integrated to the wall. This
paper seeks to incorporate the entire law of the wall into the model while preserving the …

This study presents a methodology focusing on the use of computational model and
experimental data fusion to improve the Spalart-Allmaras (SA) closure model for Reynolds …

Complex turbulent flows with large-scale instabilities and coherent structures pose
challenges to both traditional and data-driven Reynolds-averaged Navier–Stokes methods …

Field inversion machine learning (FIML) has the advantages of model consistency and low
data dependency and has been used to augment imperfect turbulence models. However …

The accurate and fast prediction of long-term dynamics of turbulence presents a significant
challenge for both traditional numerical simulations and machine learning methods. In …

Abstract Machine learning (ML) techniques have emerged as powerful tools for improving
the predictive capabilities of Reynolds-averaged Navier-Stokes (RANS) turbulence models …