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 long lasting demand for better turbulence models and the still prohibitively
computational cost of high‐fidelity fluid dynamics simulations, like direct numerical …

This chapter provides an introduction to data-driven techniques for the development and
calibration of closure models for the Reynolds-Averaged Navier--Stokes (RANS) equations …

Despite the emerging field of data-driven turbulence models, there is a lack of systematic
high-fidelity datasets at flow configurations changing continuously with respect to …

Unsteady cavitation, as observed in phenomena like underwater explosions, entails
dynamically evolving boundaries and developing dimensions of cavitation before collapse …

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

In order to improve the performance of $ k-\omega $ SST model in turbomachinery, previous
studies have used the machine-learning (ML) technique to obtain turbulence models (for …