Multi-objective optimization of the cavitation generation unit structure of an advanced rotational hydrodynamic cavitation reactor

Abstract

Hydrodynamic cavitation (HC) has been widely considered a promising technique for industrial-scale process intensifications. The effectiveness of HC is determined by the performance of hydrodynamic cavitation reactors (HCRs). The advanced rotational HCRs (ARHCRs) proposed recently have shown superior performance in various applications, while the research on the structural optimization is still absent. The present study, for the first time, identifies optimal structures of the cavitation generation units of a representative ARHCR by combining genetic algorithm (GA) and computational fluid dynamics, with the objectives of maximizing the total vapor volume, V_vapor , and minimizing the total torque of the rotor wall, M→_z . Four important geometrical factors, namely, diameter (D), interaction distance (s), height (h), and inclination angle (θ), were specified as the design variables. Two high-performance fitness functions for V_vapor and M→_z were established from a central composite design with 25 cases. After performing 10,001 simulations of GA, a Pareto front with 1630 non-dominated points was obtained. The results reveal that the values of s and θ of the Pareto front concentrated on their lower (i.e., 1.5 mm) and upper limits (i.e., 18.75°), respectively, while the values of D and h were scattered in their variation regions. In comparison to the original model, a representative global optimal point increased the V_vapor by 156% and decreased the M→_z by 14%. The corresponding improved mechanism was revealed by analyzing the flow field. The findings of this work can strongly support the fundamental understanding, design, and application of ARHCRs for process intensifications.

Keywords: CGU structure; Computational fluid dynamics; Genetic algorithm; Hydrodynamic cavitation reactor; Multi-objective optimization; Process intensification.

Graphical abstract

Fig. 1

Illustration of the flow chart for the optimization process.

Fig. 2

Schematic diagram of the ARHCR (a) and the design variables of the CGU (b). D: diameter, s: interaction distance, h: height, θ: inclination angle.

Fig. 3

Solutions of the multi-objective GA optimization.

Fig. 4

Parallel coordinates showing the relationship between the design variables and the objectives for (a) all points and (b) Pareto front.

Fig. 5

Individual distributions of the design variables and objectives for (a) all points and (b) Pareto front.

Fig. 6

Point number distributions of the design variables and objectives for (a) all points and (b) Pareto Front.

Fig. 7

Comparison between the original (left) and selected optimal model B (right). The iso-surfaces in grey color in the middle and right represent cavitation patterns with the volume fraction of gas phase at 0.3. Velocity and vector distributions in relative reference frame on the middle plane (a), static pressure distribution and cavitation regions on the middle plane (b), and bird’s-eye view with the static pressure distribution of the rotor surface (c). The rotational direction of the rotor (lower part) in (c) is clockwise.