Design and Development of Innovative Steam Injection for High-Temperature Short-Time Liquid Foods

Jet impingement has been effective in reducing the process time and improvement of product quality in various industrial applications, such as textile and paper drying, electronic cooling, glass quenching and food processing. The current work applied innovative steam injection to liquid food continuous sterilization. The multiple impingement jets of steam and product came together in the impingement tank. The effects were investigated on the Reynolds number, steam temperature and jet-to-target spacing (H/d), sterilization temperature and heat transfer efficiency in water and pineapple juice tests. The Reynolds number was based on the nozzle configuration and liquid flow rate. The study investigated product injection plates formed using two, three or four circular holes (diameter 2 mm), steam injection plates with six, nine or twenty circular holes (diameter 1 mm), steam temperatures of 120, 125 or 130 °C and H/d values of 1, 3, 5 or 7. The different options were tested with water to determine the optimal conditions, and then tested with pineapple juice. The results showed that the optimal conditions from water testing that provided the highest heat transfer efficiency occurred with two jet nozzles, six steam injection plates, a steam temperature of 120 °C and an H/d value of 1.

Testing of logarithmic-law for the slip with friction boundary condition

Large eddy simulation (LES) seeks to predict the dynamics of the organized structures in the flow, that is, local spatial averages u ̄ $\bar{u}$ of the velocity u of the fluid. Although LES has been extensively used to model turbulent flows, very often, the model has difficulty predicting turbulence generated by interactions of a flow with a boundary. A critical problem in LES is to find appropriate boundary conditions for the flow averages, which depend on the behavior of the unknown flow near the wall. In the light of the works of Navier and Maxwell, we use boundary conditions on the wall. We compute the appropriate friction coefficient β for channel flows and investigate its asymptotic behavior as the averaging radius δ → 0 and as the Reynolds number Re → ∞. No-slip conditions are recovered in the first limit, and free-slip conditions are recovered in the second limit. This study is not intended to develop new theories of the turbulent boundary layer; we use available boundary layer theories to improve numerical boundary conditions for flow averages.

Agitation of Complex Fluids in Cylindrical Vessels by Newly Designed Anchor Impellers

The fluid flows and power consumption in a vessel stirred by anchor impellers are investigated in this paper. The case of rheologically complex fluids modeled by the Bingham-Papanastasiou model is considered. New modifications in the design of the classical anchor impeller are introduced. A horizontal blade is added to the standard geometry of the anchor, and the effect of its inclination angle (α) is explored. Four geometrical configurations are realized, namely: α = 0°, 20°, 40°, and 60°. The effects of the number of added horizontal blades, Reynolds number, and Bingham number are also examined. The obtained findings reveal that the most efficient impeller design is that with (case 4) arm blades inclined by 60°.This case allowed the most expansive cavern size with enhanced shearing in the whole vessel volume. The effect of adding second horizontal arm blades (with 60°) gave better hydrodynamic performance only with a slight increase in power consumption. A significant impact of Bingham number (Bn) was observed, where Bn = 5 allowed obtaining the lowest power input and most expansive well-stirred region.

IN-STALL COMPRESSOR PERFORMANCE AND THE EFFECTS OF REYNOLDS NUMBER

Previous research into axial compressor stall has mainly focused on stall inception and methods to extend the stable operating range. This paper considers the performance of an axial compressor beyond stall and investigates how the characteristics of stall cells depend on Reynolds number. An experimental study has been conducted using a single-stage axial compressor capable of operating across the Reynolds number range of 10,000 – 100,000. Detailed unsteady measurements have been used to measure the behaviour across a range of in-stall flow coefficients. These measurements have been used to extract the stall hysteresis and to determine the size, speed, number, and spanwise extent of the stall cells. The results show that for the stalled compressor, as Reynolds number increases, the size of the minimum stable stall cell decreases. This means that a larger change in throttle area is needed to reduce the stall cell down to a size where the compressor can recover from stall. At the design Reynolds number, the number of stall cells that form transitions from one, to two, and then to four stall cells as the flow coefficient is reduced. At lower Reynolds numbers, the two-stall-cell state becomes unstable; instead, a single stall cell transitions directly into five stall cells. As the number of stall cells increases, so do the speed of the stall cells and the total size. Further reductions in the flow coefficient cause an increase in the total size and a decrease in the stall cell speed.

Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar separation bubble and an associated drag rise. This paper presents a framework for the design of a low Reynolds number airfoil. The contributions of the proposed research are twofold. First, a convolutional neural network (CNN) is designed for the aerodynamic coefficient prediction of low Reynolds number airfoils. Data generation is discussed in detail and XFOIL is selected to obtain aerodynamic coefficients. The performance of the CNN is evaluated using different learning rate schedulers and adaptive learning rate optimizers. The trained model can predict the aerodynamic coefficients with high accuracy. Second, the trained model is used with a non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization of the low Reynolds number airfoil at a specific angle of attack. A similar optimization is performed using NSGA-II directly calling XFOIL, to obtain the aerodynamic coefficients. The Pareto fronts of both optimizations are compared, and it is concluded that the proposed CNN can replicate the actual Pareto in considerably less time.