In recent years, the data-driven turbulence model has attracted widespread concern in fluid
mechanics. The existing approaches modify or supplement the original turbulence model by …

Engineering and scientific applications are frequently affected by turbulent phenomena,
which are associated with a great deal of uncertainty and complexity. Therefore, proper …

Reynolds-averaged Navier–Stokes simulations represent a cost-effective option for practical
engineering applications, but are facing ever-growing demands for more accurate …

In recent years, machine learning methods represented by deep neural networks (DNNs)
have been a new paradigm of turbulence modeling. However, in the scenario of high …

We present a mathematically well-founded approach for the synthetic modeling of turbulent
flows using generative adversarial networks (GAN). Based on the analysis of chaotic …

This study establishes a continuous adjoint data assimilation model (CADA) for the
reproduction of global turbulent mean flow from a limited number of wall pressure …

Turbulent flow simulations based on the Reynolds-averaged Navier–Stokes (RANS)
equations continue to be the workhorse approach for industrial flow problems. However, due …

This paper focuses on the optimal sensor placement (OSP) strategy based on a deep neural
network (DNN) for turbulent flow recovery within the data assimilation framework of the …

Physics-informed neural networks (PINNs) can be used to solve partial differential equations
(PDEs) and identify hidden variables by incorporating the governing equations into neural …

We present an approach to sensor placement for turbulent mean flow data assimilation in
the context of Reynolds-averaged Navier–Stokes (RANS) simulations. It entails generating a …