The primary and key task of binary fluid flow modeling is to track the interface with good accuracy, which is usually challenging due to the sharp-interface limit and numerical dispersion. This article concentrates on further development of the conservative Allen-Cahn equation (ACE) [Geier et al., Phys. Rev. E 91, 063309 (2015)10.1103/PhysRevE.91.063309] under the framework of the lattice Boltzmann method (LBM), with incorporation of the incompressible hydrodynamic equations [Liang et al., Phys. Rev. E 89, 053320 (2014)10.1103/PhysRevE.89.053320]. Utilizing a modified equilibrium distribution function and an additional source term, this model is capable of correctly recovering the conservative ACE through the Chapman-Enskog analysis. We also simulate four phase-tracking benchmark cases, including one three-dimensional case; all show good accuracy as well as low numerical dispersion. By coupling the incompressible hydrodynamic equations, we also simulate layered Poiseuille flow and the Rayleigh-Taylor instability, illustrating satisfying performance in dealing with complex flow problems, e.g., high viscosity ratio, high density ratio, and high Reynolds number situations. The present work provides a reliable and efficient solution for binary flow modeling.

The aim of this study was to investigate the effect of ultrasonic treatment and blanching prior to hot-air drying and freeze drying of onions on the retention of bioactive compounds (total phenolics, total flavonoids, and quercetin). Onion slices were treated either with ultrasound at 20 kHz and different amplitude levels (24.4-61 µm) for 1, 3 and 5 min or with blanching using hot water at 70 o C for 1, 3 and 5 min. The ultrasound treatment improved the retention of bioactive compounds (especially quercetin) and accordingly the antioxidant activity in onion slices dried either by freeze drying or hot-air drying. This is ascribed to the destruction of the original tissue structure by ultrasound and thus higher extraction ability of the studied phytochemicals. Comparing ultrasound treated samples, freeze dried onions had a higher retention of bioactive compounds than hot-air dried ones. Blanched and ultrasound treated dried onions exhibited similar colour change. Therefore, ultrasound treatment is a potential alternative to conventional blanching before drying of onion slices.

Machine learning has recently become a promising technique in fluid mechanics, especially for active flow control (AFC) applications. A recent work [Rabault et al., J. Fluid Mech. 865, 281-302 (2019)] has demonstrated the feasibility and effectiveness of deep reinforcement learning (DRL) in performing AFC over a circular cylinder at Re ¼ 100, i.e., in the laminar flow regime. As a follow-up study, we investigate the same AFC problem at an intermediate Reynolds number, i.e., Re ¼ 1000, where the weak turbulence in the flow poses great challenges to the control. The results show that the DRL agent can still find effective control strategies, but requires much more episodes in the learning. A remarkable drag reduction of around 30% is achieved, which is accompanied by elongation of the recirculation bubble and reduction of turbulent fluctuations in the cylinder wake. Furthermore, we also perform a sensitivity analysis on the learnt control strategies to explore the optimal layout of sensor network. To our best knowledge, this study is the first successful application of DRL to AFC in weakly turbulent conditions. It therefore sets a new milestone in progressing toward AFC in strong turbulent flows.