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Turbomachinery optimization based on the inverse design method (IDM) has been investigated in several previous studies, however, due to head constraints, most of these studies have adopted the constant impeller outlet angular momentum (IOAM) distribution, namely the free vortex design, which in turn reduces the optimization effect. To overcome these drawbacks, forced and compound vortex designs are proposed here by parameterizing the IOAM using a parabola, and the effects of different vortex designs on the optimization results of a mixed-flow pump impeller are investigated. First, a baseline mixed-flow pump is simulated and experimentally verified. Second, based on the IDM, the impeller is parameterized for the three vortex designs. Finally, it is optimized by artificial neural network and genetic algorithm to maximize the weighted efficiency at 0.8Qdes, 1.0Qdes and 1.2Qdes, and the results are analyzed. The results show that the weighted efficiency of the forced and compound vortex design is improved by 1.33% and 1.69% respectively compared to free vortex design. The internal flow analysis reveals that the improved efficiency of the compound vortex design can be attributed to the improved impeller-outlet flow regime. Finally, the energy characteristics of the preferred and baseline models are compared using the entropy production method.
Wang et al. (Sun,) studied this question.