Physics of fluids 

About: Physics of fluids is an academic journal published by American Institute of Physics. The journal publishes majorly in the area(s): Turbulence & Computer science. It has an ISSN identifier of 0899-8213. Over the lifetime, 3420 publications have been published receiving 8152 citations. The journal is also known as: Fluid dynamics.

Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network

Abstract: The velocities measured by particle image velocimetry (PIV) and particle tracking velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity field with high resolution is indispensable for data visualization and analysis. In the present work, a physics-informed neural network (PINN) is proposed to reconstruct the dense velocity field from sparse experimental data. A PINN is a network-based data assimilation method. Within the PINN, both the velocity and pressure are approximated by minimizing a loss function consisting of the residuals of the data and the Navier–Stokes equations. Therefore, the PINN can not only improve the velocity resolution but also predict the pressure field. The performance of the PINN is investigated using two-dimensional (2D) Taylor's decaying vortices and turbulent channel flow with and without measurement noise. For the case of 2D Taylor's decaying vortices, the activation functions, optimization algorithms, and some parameters of the proposed method are assessed. For the case of turbulent channel flow, the ability of the PINN to reconstruct wall-bounded turbulence is explored. Finally, the PINN is applied to reconstruct dense velocity fields from the experimental tomographic PIV (Tomo-PIV) velocity in the three-dimensional wake flow of a hemisphere. The results indicate that the proposed PINN has great potential for extending the capabilities of PIV/PTV.

Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations

Abstract: Physics-informed neural networks (PINNs) are successful machine-learning methods for the solution and identification of partial differential equations. We employ PINNs for solving the Reynolds-averaged Navier–Stokes equations for incompressible turbulent flows without any specific model or assumption for turbulence and by taking only the data on the domain boundaries. We first show the applicability of PINNs for solving the Navier–Stokes equations for laminar flows by solving the Falkner–Skan boundary layer. We then apply PINNs for the simulation of four turbulent-flow cases, i.e., zero-pressure-gradient boundary layer, adverse-pressure-gradient boundary layer, and turbulent flows over a NACA4412 airfoil and the periodic hill. Our results show the excellent applicability of PINNs for laminar flows with strong pressure gradients, where predictions with less than 1% error can be obtained. For turbulent flows, we also obtain very good accuracy on simulation results even for the Reynolds-stress components.

A unified theory for bubble dynamics

Abstract: In this work, we established a novel theory for the dynamics of oscillating bubbles such as cavitation bubbles, underwater explosion bubbles, and air bubbles. For the first time, we proposed bubble dynamics equations that can simultaneously take into consideration the effects of boundaries, bubble interaction, ambient flow field, gravity, bubble migration, fluid compressibility, viscosity, and surface tension while maintaining a unified and elegant mathematical form. The present theory unifies different classical bubble equations such as the Rayleigh-Plesset equation, the Gilmore equation, and the Keller-Miksis equation. Furthermore, we validated the theory with experimental data of bubbles with a variety in scales, sources, boundaries, and ambient conditions and showed the advantages of our theory over the classical theoretical models, followed by a discussion on the applicability of the present theory based on a comparison to simulation results with different numerical methods. Finally, as a demonstration of the potential of our theory, we modeled the complex multi-cycle bubble interaction with wide ranges of energy and phase differences and gained new physical insights into inter-bubble energy transfer and coupling of bubble-induced pressure waves.

Investigation of counter-rotating shock wave and wave direction control of hollow rotating detonation engine with Laval nozzle

Abstract: The counter-rotating shock wave and wave direction control of the hollow rotating detonation combustor with Laval nozzle are studied. The in-house solver BYRFoam, developed on the OpenFOAM platform, is used. The phenomenon and spatial distribution of the counter-rotating shock wave in the combustor are revealed. The result suggests that the closer the location is to the outer wall, the stronger the counter-rotating shock wave is. A method of controlling the wave direction is proposed. It's shown that the intensity of the counter-rotating shock wave is controlled by reducing the total pressure of inlet, and then the direction of the detonation wave is controlled. The process of detonation wave reversing is divided into four steps, namely, counter-rotating shock waves evolve into detonation waves, several detonation waves are extinguished, detonation waves form again, and detonation waves propagate stably. The mechanism of wave direction control is investigated. The result shows that the fluctuation of the total pressure of inlet stimulates the positive feedback interaction between the counter-rotating shock wave and the fresh gas, which causes initial detonation waves to be extinguished and the intensity of counter-rotating shock waves to become stronger and stronger, and eventually counter-rotating shock waves evolve into reverse detonation waves.

Fully coupled modeling of two-phase fluid flow and geomechanics in ultra-deep natural gas reservoirs

Abstract: Efficiently and accurately understanding the fluid flow behavior in ultra-deep natural gas reservoirs is very challenging due to the complex geological environment and the intricate gas properties at high pressure. In this study, a fully coupled fluid flow and geomechanical model was developed to simulate complex production phenomena in ultra-deep natural gas reservoirs. Stress-dependent porosity and permeability models were applied, and then the governing equations of the model were incorporated into COMSOL Multiphysics. Furthermore, the model was verified by the reservoir depletion from the Keshen gas field in China, and the effects of reservoir properties and geomechanics on gas production were discussed. The results showed that the reservoir pressure and water saturation exhibited a significant funnel-shaped decline during the reservoir depletion. The higher relative permeability of the gas phase results in more methane gas production, thereby reducing the average pore pressure and gas saturation near the wellhead. When considering geomechanical effects, the production behavior significantly changes. The predictive value of gas production was higher when the reservoir rock deformation was ignored. The gas production exhibited strong positive correlations with reservoir porosity, fracture permeability, elastic modulus, and Poisson's ratio. Larger porosity, elastic modulus, and Poisson's ratio resulted in smaller deformation, while a smaller fracture permeability leads to larger deformation in ultra-deep natural gas reservoirs.