• Cavitation flow in an ARHCR is studied combining flow visualization and CFD. • Cavitation is characterized by the periodic motion of CGUs. • Cavitation development process shows high periodicity from flow visualization. • Vortex cavitation is generated at the vortex center of CGUs. • Sheet cavitation is induced in the flow separation region of downstream CGUs.

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, $V_{\mathit{vapor}}$ , and minimizing the total torque of the rotor wall, ${\overrightarrow{M}}_z$ . 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 $V_{\mathit{vapor}}$ and ${\overrightarrow{M}}_z$ 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 $V_{\mathit{vapor}}$ by 156% and decreased the ${\overrightarrow{M}}_z$ 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.