In computational fluid dynamics simulations of industrial flows, models based on the
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …

The accuracy and reliability of 3D steady RANS CFD simulations of wind flow in urban
environments can be affected by numerical settings including the turbulence model and the …

借助于高性能计算机和数据共享平台的发展, 研究者可以获取大量的高分辨率湍流计算数据.
近年来, 随着深度神经网络等人工智能技术的发展, 数据驱动的机器学习方法也开始应用于湍流 …

The possible ill conditioning of the Reynolds average Navier–Stokes (RANS) equations
when an explicit data-driven Reynolds stress tensor closure is employed is a discussion of …

As early as at the end of the 19th century, shortly after mathematical rules describing fluid
flow—such as the Navier–Stokes equations—were developed, the idea of using them for …

Abstract A novel Sparse Bayesian Learning (SBL) framework is introduced for generating
stochastic Explicit Algebraic Reynolds Stress (EARSM) closures for the Reynolds-Averaged …

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

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

Direct numerical simulations (DNS) provide useful information for the understanding and the
modeling of turbulence phenomena. In particular, new methodologies recently allowed the …

Abstract The Reynolds-averaged Navier–Stokes (RANS) equations are primarily used to
describe turbulent flows, and hence are essential for fluid simulation in Naval Architecture …