Multi-objective optimization of the cavitation generation unit structure of an advanced rotational hydrodynamic cavitation reactor |
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Affiliation: | 1. Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China;2. National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China;3. College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China;4. Department of Process Engineering and Chemical Technology, Faculty of Chemistry, Gdańsk University of Technology, Gdańsk 80-233, Poland;5. Department of Mechanical Engineering, Hanyang University, Ansan 15588, Republic of Korea |
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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, , and minimizing the total torque of the rotor wall, . 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 and 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 by 156% and decreased the 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. |
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Keywords: | Hydrodynamic cavitation reactor Multi-objective optimization CGU structure Genetic algorithm Process intensification Computational fluid dynamics |
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