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 …

Experimental measurements and numerical simulations of turbulent flows are characterized
by a tradeoff between accuracy and resolution. In this study, we combine accurate sparse …

The linearized Bhatnagar–Gross–Krook equation is widely used to describe low-speed
rarefied gas flows and can be solved numerically using deterministic methods such as the …

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-
dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization …

In this study, we present a novel integration of numerical methodologies and advanced
computational intelligence to elucidate the dynamics of cross nanofluid flow over a Riga …

Physics-informed neural networks (PINNs) have recently become a new popular method for
solving forward and inverse problems governed by partial differential equations. However, in …

We propose a novel method to reconstruct mean velocity fields of turbulent shockwave–
boundary layer interactions (SBLIs) from background-oriented schlieren (BOS) …

Due to the difficulties of precisely calculating the turbulence intensity within the separated
shear layer using traditional turbulent models, computing strongly separated flows is a key …

View Video Presentation: https://doi. org/10.2514/6.2023-2441. vid Background-oriented
schlieren (BOS) is an imaging technique that can be used to characterize the density field in …

Physics-informed neural networks (PINNs) are becoming popular in solving fluid mechanics
problems forwardly and inversely. However, under limited observations, the application of …