Abstract In the present study, multi-objective optimization of a gas cyclone is performed using a genetic algorithm (GA) and computational fluid dynamics (CFD) techniques to minimize pressure drop and maximize its collection efficiency. The reference model is a well-optimized cyclone from a previous study. First, a screening experiment for seven factors is performed to determine the statistically significant factors. Then, to define the fitness function used in the GA, four of the factors are studied using the central composite design in the response surface methodology. The second-generation non-dominated sorting genetic algorithm is utilized to optimize the four significant factors of the cyclone according to the well-defined fitness functions, and 53 non-dominated optimum cyclone design points are suggested. The reasonable accuracy of the results from the GA is confirmed via CFD validation of five representative optimum points. The obtained Pareto front comprises important design information for the new cyclones. Finally, the performance and flow field of a representative optimal design are compared with those of the classical Stairmand model and the reference model. The optimal design reduces the pressure drop and cut-off size by 7.38% and 9.04%, respectively, compared to the reference model. In addition, compared to the Stairmand model, decreases of 19.23% and 42.09% are achieved for the pressure drop and cut-off size, respectively.

中文翻译：

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基于遗传算法和计算流体动力学的气旋分离器多目标优化

摘要 在本研究中，使用遗传算法 (GA) 和计算流体动力学 (CFD) 技术对气旋进行多目标优化，以最大限度地减少压降并最大限度地提高其收集效率。参考模型是先前研究中经过充分优化的旋风分离器。首先，对七个因素进行筛选实验以确定具有统计意义的因素。然后，为了定义 GA 中使用的适应度函数，使用响应面方法中的中心复合设计研究了四个因素。利用二代非支配排序遗传算法根据定义好的适应度函数对气旋的4个显着因子进行优化，提出53个非支配最佳气旋设计点。GA 结果的合理准确性通过五个代表性最佳点的 CFD 验证得到确认。获得的帕累托锋包含新气旋的重要设计信息。最后，将具有代表性的优化设计的性能和流场与经典 Stairmand 模型和参考模型的性能和流场进行比较。与参考模型相比，优化设计将压降和截止尺寸分别降低了 7.38% 和 9.04%。此外，与 Stairmand 模型相比，压降和截止尺寸分别降低了 19.23% 和 42.09%。将典型优化设计的性能和流场与经典 Stairmand 模型和参考模型的性能和流场进行了比较。与参考模型相比，优化设计将压降和截止尺寸分别降低了 7.38% 和 9.04%。此外，与 Stairmand 模型相比，压降和截止尺寸分别降低了 19.23% 和 42.09%。将典型优化设计的性能和流场与经典 Stairmand 模型和参考模型的性能和流场进行了比较。与参考模型相比，优化设计将压降和截止尺寸分别降低了 7.38% 和 9.04%。此外，与 Stairmand 模型相比，压降和截止尺寸分别降低了 19.23% 和 42.09%。