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.

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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.

In recent years, Artificial Neural Networks (ANNs) and Deep Learning have become increasingly popular across a wide range of scientific and technical fields, including Fluid Mechanics. While it will take time to fully grasp the potentialities as well as the limitations of these methods, evidence is starting to accumulate that point to their potential in helping solve problems for which no theoretically optimal solution method is known. This is particularly true in Fluid Mechanics, where problems involving optimal control and optimal design are involved. Indeed, such problems are famously difficult to solve effectively with traditional methods due to the combination of non linearity, non convexity, and high dimensionality they involve. By contrast, Deep Reinforcement Learning (DRL), a method of optimization based on teaching empirical strategies to an ANN through trial and error, is well adapted to solving such problems. In this short review, we offer an insight into the current state of the art of the use of DRL within fluid mechanics, focusing on control and optimal design problems.

We carried out a 6-year study to assess the effect of conventional, organic, and mixed cultivation practices on bioactive compounds (flavonoids, anthocyanins) and antioxidant capacity in onion. Total flavonoids, total anthocyanins, individual flavonols, individual anthocyanins, and antioxidant activity were measured in two varieties ('Hyskin' and 'Red Baron') grown in a long-term split-plot factorial systems comparison trial. This is the first report of repeated measurements of bioactive content over an extensive time period in a single crop type within the same trial. Antioxidant activity (DPPH and FRAP), total flavonol content, and levels of Q 3,4' D and Q 3 G were higher in both varieties under fully organic compared to fully conventional management. Total flavonoids were higher in 'Red Baron' and when onions were grown under organic soil treatment. Differences were primarily due to different soil management practices used in organic agriculture rather than pesticide/ herbicide application.

Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines, especially those involving big data. Successes in these areas also attract researchers from the community of fluid mechanics, especially in the field of active flow control (AFC). This article surveys recent successful applications of machine learning in AFC, highlights general ideas, and aims at offering a basic outline for those who are interested in this specific topic. In this short review, we focus on two methodologies, i.e., genetic programming (GP) and deep reinforcement learning (DRL), both having been proven effective, efficient, and robust in certain AFC problems, and outline some future prospects that might shed some light for relevant studies.

We demonstrate the use of high-fidelity computational fluid dynamics simulations in machine-learning based active flow control. More specifically, for the first time, we adopt the genetic programming (GP) to select explicit control laws, in a data-driven and unsupervised manner, for the suppression of vortex-induced vibration (VIV) of a circular cylinder in a low-Reynolds-number flow (Re = 100), using blowing/suction at fixed locations. A cost function that balances both VIV suppression and energy consumption for the control is carefully chosen according to the knowledge obtained from pure blowing/suction open-loop controls. By implementing reasonable constraints to VIV amplitude and actuation strength during the GP evolution, the GP-selected best ten control laws all point to suction-type actuation. The best control law suggests that the suction strength should be nonzero when the cylinder is at its equilibrium position and should increase nonlinearly with the cylinder's transverse displacement. Applying this control law suppresses 94.2% of the VIV amplitude and achieves 21.4% better overall performance than the best open-loop controls. Furthermore, it is found that the GP-selected control law is robust, being effective in flows ranging from Re = 100 to 400. On the contrary, although the P-control can achieve similar performance as the GP-selected control at Re = 100, it deteriorates in higher Reynolds number flows. Although for demonstration purpose the chosen control problem is relatively simple, the training experience and insights obtained from this study can shed some light on future GP-based control of more complicated problems.