Effect of the cavitation generation unit structure on the performance of an advanced hydrodynamic cavitation reactor for process intensifications

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

“…As a result, vortex cavitation and sheet cavitation regions are induced. The detailed description on the computational domain, solver setup, boundary conditions, mesh, validation, and flow mechanism can be found in the studies by Sun, et al [43] and Sun, et al [44] , and is not repeated here.…”

“…In the present study, the hemisphere-shaped CGU in interaction mode was selected as the reference model, due to its superior performance compared with other common designs, e.g., cone-cylinder, cone, or cylinder shapes [44] . Four geometrical factors with important influences, i.e., diameter ( D ), interaction distance ( s ), height ( h ), and inclination angle ( θ ), were selected as the design variables, as presented in Fig.…”

“…2 . Based on the experience derived from our previous experimental and numerical studies [26] , [38] , [43] , [44] , [15] , [45] , [46] , [40] , the basic values and suitable ranges utilized in the CCD for the four design variables were shown in Table 1 .

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“…Moreover, the effect of structure on their performance is rarely focused on, let alone structural optimization. Recently, for the first time, we investigated the effect of the cavitation generation unit (CGU) structure (i.e., five key factors including shape, diameter, interaction distance, height, and inclination angle) of a representative ARHCR on its cavitation generation and required shaft power [44] . The results indicate that the CGU structure determines the performance, and the variation trend of the cavitation generation and power for one single factor was contradictious.…”

“…The chosen objectives were to minimize the shaft power (standing for energy consumption) and to maximize the cavitation generation. Four important geometrical factors of the CGU, namely, diameter, interaction distance, height, and inclination angle, as confirmed in our previous study [44] , were selected as the design variables. The central composite design (CCD) was performed to establish the response surface models for the two objectives as the fitness functions utilized in the GA. A total of 100 generations with a population of 100 was simulated by Non-Dominated Sorting Genetic Algorithm II (NSGA-II).…”