Accurate simulation of transition from the laminar to the turbulent flow is of great importance
in industrial applications. In the present work, the framework of field inversion and machine …

Due to the significant improvement in computing power and the rapid advancement of data
processing technologies, artificial intelligence (AI) has introduced new tools and …

Data-driven turbulence modeling is receiving considerable attention specially when Direct
Numerical Simulations (DNS) are the physics-informed learning environment and Reynolds …

The Bayesian method has been increasingly applied to the inversion of seepage
parameters owing to its superiority of considering the uncertainty in the inversion process …

Abstract The Reynolds Averaged Navier-Stokes (RANS) models are still the workhorse in
current engineering applications due to its high efficiency and robustness. However, the …

Shock wave–boundary layer interaction (SWBLI) is one of the most prevalent challenges in
the field of fluid mechanics. Excessive interaction may lead to strong flow separation, which …

The Bayesian uncertainty quantification technique has become well-established in
turbulence modeling over the past few years. However, it is computationally expensive to …

Transonic flow simulation is a common but complex task in engineering, and poses a great
challenge to computational fluid dynamics using the Reynolds-averaged Navier–Stokes …

In whirlpool, a separator used in the brewing industry, the deposits accumulate in the central
zone at the bottom of the separator. The deposit assumes a shape that resembles a flattened …

In this paper we study the efficacy of combining machine-learning methods with projection-
based model reduction techniques for creating data-driven surrogate models of …